The contribution of non-sensory information processing to perceptual decision making is not fully understood. Choice biases have been described for mice and humans and are highly prevalent even if they decrease rewarding outcomes. Choice biases are usually reduced by discriminability because stimulus strength directly enables the adjustments in the decision strategies used by decision-makers. However, choice biases could also derive from functional asymmetries in sensory processing, decision making, or both. Here, we tested how particular experimental contingencies influenced the production of choice biases in mice and humans. Our main goal was to establish the tasks and methods to jointly characterize psychometric performance and innate side-choice behavior in mice and humans. We implemented forced and un-forced visual tasks and found that both species displayed stable levels of side-choice biases, forming continuous distributions from low to high levels of choice stereotypy. Interestingly, stimulus discriminability reduced the side-choice biases in forced-choice, but not in free-choice tasks. Choice biases were stable in appearance and intensity across experimental days and could be employed to identify mice and human participants. Additionally, side- and alternating choices could be reinforced for both mice and humans, implying that choice biases were adaptable to non-visual manipulations. Our results highlight the fact that internal and external elements can influence the production of choice biases. Adaptations of our tasks could become a helpful diagnostic tool to detect aberrant levels of choice variability. Stereotypical choice behavior exhibits low variability from trial to trial and has no apparent goal or function  and un-forced (2AUC) visual tasks for mice and humans, with equally rewarded alternatives. In the 2AFC tasks, the discriminative stimulus predicted the side of reinforcement, whereas in the 2AUC tasks, both sides were equally reinforced. Using these adaptations, we found that stimulus similarity increased the side-choice biases in the 2AFC tasks but not in the 2AUC tasks. Furthermore, both groups of mice and humans displayed stable levels of choice biases, forming continuous distributions from low to high levels of choice stereotypy. Although choice biases were stable in appearance and intensity across experimental days, they were also influenced by recent choice and reward histories. Favoring the notion that decisional biases can also be internally mediated, we found that side-biased and alternating choice sequences could be employed to identify mice and human participants with a high degree of certainty. Also, by reinforcing side-preference and alternation, we found that choice biases were strongly adaptable to non-visual manipulations. Altogether, this study established and validated the conditions to jointly characterize psychometric performance and side-choice behavior in mice and humans. Choice biases could become useful biomarkers to diagnose psychopathologies and mental disorders that are characterized by aberrant levels of behavioral variability.We used 2–4-month-old C57BL/6J male mice (18–32 g) housed in groups of up to three animals per polycarbonate cage  under standard laboratory conditions, with unrestricted access to food  and water. The housing room operated with regular light/dark cycle, with constant temperature (22°C ± 2°C) and humidity (55 ± 20%). The cages were changed once per week with fresh sawdust. All experiments were done during the light phase of the day, between 8 a.m. and 6 p.m., from Monday to Friday. Our animal experiments followed the Mexican animal welfare guidelines  and were approved by the ethics committee of our institution .D because it predicted the existence of two elevated platforms placed to the left and right sides of that arm. Animals choosing the ‘correct arm’ displaying the SD were allowed to rest on the elevated platform for 40 s, but they could rest only for 10 s after choosing the ‘wrong arm’. The arm projecting the SD was selected pseudo-randomly on each trial, but it could not repeat over consecutive trials  aquatic discrimination task that we described in detail recently . The appe trials .Visual performance was measured as the group average correct choices/mouse, whereas the escape latencies (in s) were taken as the time it took the animal to reach an escape platform. The overall training procedures were identical to those described previously and concluded when the mice achieved ≥ 90% correct choices for two consecutive days . We charWe performed experiments with 55 healthy volunteers (26 men and 29 women). Their mean age was 23.9 ± 0.7 years . Most participants were right-handed (≥90%), with normal or corrected vision, and without detectable neurological disorders or history of drug abuse. We obtained written consent from all participants. The ethics committee of our institution approved all these procedures .® Xeon® @ 3.40 GHz; 64 Bit Operating system; Graphics card: NVIDIA Quadro K600, 8 GB). We measured the response times  as the interval between the appearance of the visual stimuli and the moment when the participants placed the joystick in the appropriate response regions.Participants sat upright on a stool (with adjustable height) at a desk in front of a manipulandum centered at the midline of a 19-inch computer monitor  projected during 1 s to the left or right side of a distractor (the SD). Concluding the projection of both stimuli, two white arrows appeared on the center of the screen, pointing toward the right and left inferior corners, respectively, indicating the two response options. These arrows remained on the screen until the participant responded. Next, the SD was projected at the center of the screen for either 750 ms or 3 s if the participant responded correctly or incorrectly, respectively . During the second choice phase, the participants could select left or right sides with forced  or free (2AUC) side choices, respectively. Here, participants could visualize two white arrows that appeared on the center of the screen, pointing toward the right and left superior corners, indicating the two response options . For mouse experiments, we kept the training conditions fixed across pairs of experimental days , whereas for human experiments, we switched contingencies every 60 trials with a total of 12 blocks/day.We implemented 2AFC tasks for mice and humans, where the discriminative stimulus predicted the side of reinforcement: (1) in the ‘correct arm’ for the mouse task or (2) within the lower portion of the stimulus-projection search space for the human task. We extended this task design to include a second 2AUC phase in which we measured unforced-choices . In the 2AUC phase, both sides were equally reinforced and, therefore, the discriminative stimulus did not predict the side of reinforcement: (1) already inside the ‘correct arm’ for the mouse task or (2) within the upper portion of the projecting screen for the human task. Thus, in the [2AFC→ 2AUC] tasks, the discriminative stimulus predicted reinforcement for the first visual choice (‘correct arm’ for mice and ‘correct lower side’ for humans), but provided no information about the second side-choice (left or right side-choice). In experiments illustrated in Both mice and human participants could select left or right sides during the second forced/free-choice phase of our experiments. They typically displayed a choice bias toward one preferred side . To grapD and the chosen platform as potential predictors of current choice behavior. The scalar coefficients were fit using MATLAB function ‘glmfit.m.’To estimate the contributions of previous visual choices, side-choices, and their outcomes (success or failure) on the production of current side-choices, we carried out multiple logistic regression model (MLRM) analyses, as described before . For mict-tests and one-way ANOVA tests for performance comparisons and repeated measures ANOVA (RM-ANOVA) tests with Bonferroni’s or Wilcoxon Signed Rank post hoc tests for group comparisons. We compared the probability distributions using Kolmogorov–Smirnov (KS) tests. We illustrate our group data as averages ± SEM with a significance set at P ≤ 0.05.We used D) predicted the reinforcer side. We controlled the difficulty of the perceptual task through scalable contrast, structural similarity (SSIM), and similarity (SIM) between the target SD and distractor SD stimuli  by using gratings with decreasing  and increasing  and escape latencies  for both subgroups of mice  tendency to choose one alternative over another. Notably, although side-choice preferences in these tasks are heterogeneous across animals, they are strongly sensitive to changes in stimulus discriminability . Thus, w of mice . We pool of mice .x-axis . The pooled data illustrates how stimulus discriminability influenced human visual performance , tested in a 2AFC task with increasing  in such a way that access to either left or right reinforcer was always available and, therefore, identical (we will refer to it as the ‘[2AFC→ 2AUC] task’). We tested two subgroups of mice  with increasing , in agreement with previous findings  and alternation  probabilities were similar across all SSIM values , yet with some differences in their response times   and alternation  probabilities across all stimulus categories (upper and lower panels in We took a similar approach to explore the influence of stimulus discriminability on a [2AFC→ 2AUC] task for humans . We test< 0.001) . SimilarD with predictive value. This contingency established a direct link between stimulus discriminability and choice biases  of the changes in bias probabilities referenced to the one we obtained with max. discriminability (we refer to this measure as ΔBias). In agreement, stimulus discriminability had a strong influence on ΔBias in the 2AFC for mice . We found that both mouse task variants lacked such sequential effects . Note how the cumulative match characteristic curves increased rapidly with the number of trials considered.Because choice bias probabilities are quite invariant in time, this opens the door to use them as a behavioral feature to identify individuals. Using choice records from 500 trials from a group of 86 mice, we calculated and sorted the probability distributions for biased left and right choices and complementary alternating sequences  and trained the mice/participants across different training blocks with stationary or variable reward landscapes, by reinforcing (i) alternating, (ii) left, or (iii) right sequences , and it was >2000% bigger compared to those blocks in which we reinforced side-choices , with a subtle reduction in their escape latencies over training  and   and a reduction in RTs when comparing d1 and  . Similar to what we found in mice, the biased probability was similar across days during which we reinforced side-choices , but it was ∼970% bigger (AUC) than the biased probability obtained when reinforcing randomized choices  but were ∼797% bigger than the alternation probability we found when reinforcing side-choices  but, remarkably, the general profile of side-choice behavior of these mice quickly returned to baseline values after concluding reinforcement . These results suggest that adjustments in side-choice behavior can be reinforced, but the idiosyncratic side-preferences of the mice tend to return to a baseline level once the differential reinforcement is removed.To further explore the sensitivity of mouse side-choice behavior to external influences, we reinforced the un-preferred side of a group of 8 mice for two consecutive days, while measuring side-choice behavior before (Pre), during (reinforcement), and after (Post) this training procedure. The asymmetric reinforcement had clear effects in the observed biased choices  visual choices, (iii) reinforcers, and (iv) side-choices influenced the production of current side-choices from our mice and human experiments  Seriments . We hypoeriments . In agreeriments . AltogetWe adapted two-alternative tasks for mice and humans to study their visual capacities and choice biases . The tasOur results offered a side-by-side comparison of rodent and human findings. By adjusting the similarity of training stimuli, we found that stimulus discriminability reduced choice biases in the 2AFC tasks for both species. In the [2AFC→ 2AUC] task, the stimulus predicted reinforcement during the forced-choice  but was irrelevant during the second free-choice (left or right side-choice), as both sides were equally reinforced. In consequence, the production of choice biases in the [2AFC→2AUC] tasks was insensitive to changes in stimulus similarity. Thus, stimulus discriminability had a direct influence on the production of choice biases in forced-choice but not in free-choice tasks. Using the [2AFC→ 2AUC] tasks, we found that choice biases varied substantially in magnitude and preferred side across individuals . The mixWe explored for sequential effects between visual and side-choices. Only for the mouse tasks, we found that the side choices were fully independent of the visual choices. In contrast, the side-choices in the [2AFC→2AUC] task for humans carried some information from the previous discriminative decisions. Therefore, the lack of sequential effects in the mouse [2AFC→2AUC] task makes it an ideal experimental platform to explore the non-sensory contributions and mechanisms involved in side-choice behavior. Furthermore, the side-choices of the mice also exhibited sensitivity to salient features (drifting direction) of non-predictive stimuli.One limitation of this study is that we only used male mice, and we did not explore gender differences. Although male and female mice usually reach similar performance levels, they tend to adopt different strategies during learning, with male mice changing strategies more frequently than females . DespiteSide choices exist in 2AFC tasks despite producing sub-optimal performance . They coIn stable rewarding landscapes, improving performance usually means decreasing trial-to-trial variability with practice . HoweverA source for choice biases and their dependence on recent history could involve stable working memory representations . HistoryLow levels of behavioral variability and/or high stereotypy characterize some human psychopathologies and mental disorders . Human sAt the circuit level, we still do not understand the mechanisms that underlie choice biases and why these traits are present in different proportions in mice and humans. Irrespective of whether side choice behavior is innate or learned, the fact that we could use it to identify subjects clearly indicates that this behavior should have a stable internal representation, one which could derive from functional asymmetries in neuronal circuitry. Internal factors and asymmetries in sensory encoding and inhibitory control have been associated with choice biases . Most imVariability at the circuit level is generated by many neurophysiological mechanisms that include the stochastic nature of synaptic transmission with relevant interactions across large neural populations and with distributed neuromodulation effects . Where eThere is a lack of knowledge of the mechanisms that mediate and regulate the manifestation of repetitive behaviors . AnalysiThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by ET092018-271; Instituto de Neurociencias, Universidad de Guadalajara, Mexico. The participants provided their written informed consent to participate in this study.MT conceived the project, designed and built the devices, made projection and analysis algorithms, analyzed the data, made the figures, wrote the manuscript, and answered the reviewers’ comments. RM-C adjusted the projection of visual stimuli, oversaw the proper functioning of devices used for behavioral experiments, and made figures. BH performed the tests with human participants.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Colorectal cancer (CRC) is the fourth most common cancer in men and the third most common cancer in women worldwide. The incidence and mortality of CRC was increasing rapidly in China. Lymph node-negative colorectal cancer patients with synchronous liver metastasis (LNLM1) was defined as “skip” lymph vascular invasion on hepatic metastasis, who presenting poor prognosis. We aiming to investigate the potential mechanism for the “skip” lymph vascular invasion on hepatic metastasis in colorectal cancer. The microarray was applied for screening the transcription landscape of circRNA in lymph node negative CRC patients with synchronous liver metastasis (LNLM1) or without liver metastasis (LNLM0). We identified the aberrant increased circRNA circ_0124554  in tumor tissues of LNLM1 patients comparing with either the tumor tissues of LNLM0 or adjacent tissues of LNLM1. Circ-LNLM1 expression was highly correlated with liver metastasis and vascular invasion. Ectopic expression of cytoplasmic located circ-LNLM could promote invasion of CRC cells and induced the liver metastasis in animal models through the direct binding with AKT. The phosphorylation of AKT (T308/S473) was activated due to the blocked ubiquitination site of Lys in 0-52aa peptide of circ-LNLM. Endogenous plasma expression of circ-LNLM induced poor prognosis of LNLM1 and could distinguish LNLM1 patients from LNLM0. In conclusion, the circ-LNLM blocked the ubiquitination of AKT could promote the early metastasis especially for the lymph node-negative colorectal cancer patients with synchronous liver metastasis. The circ-LNLM might be prognosis and diagnosis biomarker for LNLM1 patients. Worldwide, about 1.8 million new cases of colorectal cancer occur each year, and about 880,000 colorectal cancer deaths occurred3. In developed countries such as Europe and America, the incidence and mortality of colorectal cancer have reached the third and second place among all cancer types4. In China, with the improvement of people’s living standards and the change of diet structure, the incidence and mortality of colorectal cancer are increasing year by year. In combination with China’s specific national conditions, the rate of early diagnosis and treatment of colorectal cancer is much lower than that in developed countries such as Europe, America and Japan5. Most of the patients are diagnosed with local advanced stage or distant metastasis, especially liver metastasis, with poor treatment effect and poor prognosis.According to the Cancer Statistics in 2020, colorectal cancer (CRC) is one of the most common gastrointestinal malignancies, ranking the third in males and the second in females, with high incidence, difficult treatment and poor prognosis7. The prognosis of patients with liver metastasis is very poor. Without any treatment, the natural survival is about 6.6 months, the 3-year survival rate is 5%, and the 5-year survival rate is less than 1%8. The main reason is that when liver metastasis is found, the resection rate of liver metastasis foci is not high, the patients have a late stage of disease, and the disease progresses rapidly. Therefore, in order to improve the treatment effect and prognosis of patients with colorectal cancer survival, explore early metastasis of colorectal cancer development of specific molecular mechanism, and, in turn, screening effective drug targets, reducing colorectal cancer metastasis and improving the effect of the therapy of colorectal cancer, it is of great clinical significance and research value.Previous studies have shown that about 15–25% of patients with colorectal cancer have synchronous liver metastasis, the most common organ with distant colorectal cancer metastasis, while as many as 20–25% of patients will have synchronous liver metastasis after primary resection of colorectal cancer10. Therefore, the in-depth exploration of the tumor biological function of these patients, the analysis of the specific mechanism of simultaneous liver metastasis in lymph node negative colorectal cancer patients, and the search for key targets to promote the progression of tumor invasion can provide an important theoretical basis for the subsequent development of liver metastasis targets for colorectal cancer.Although lymph node metastasis is the most common mode of colorectal cancer metastasis, lymph node metastasis also plays an important role in predicting the prognosis of colorectal cancer patients. In addition, colorectal cancer patients with synchronous liver metastasis also have positive postoperative lymph node metastasis, but in clinical work, we gradually found that lymph node negative colorectal cancer patients at the initial diagnosis of synchronous liver metastasis. Combined with the colorectal cancer metastasis pathway, this type of lymph node negative colorectal cancer with simultaneous liver metastasis, the cancer cells may have been transferred to the liver by blood route. This is mainly decided by the anatomical features: portal vein system exists between intestinal blood and liver vascular network, transferred to the liver cancer cells through the portal system, and early in the lymphatic system transfer, prompt such there may be a more aggressive tumor biology function, and there is evidence that such relative to lymph node negative patients without hepatic metastasis in patients with worse prognosis11. CircRNAs can be classified into three categories according to their source: intron circRNAs, exon circRNAs, and circRNAs formed by a mixture of introns and exons12. CircRNA is widely distributed in cells with time-specific, tissue-specific, and disease-specific. CircRNA is widely distributed in tissues and highly conserved in structure14. Due to the absence of poly(A) tail structure and cap structure, circRNA is highly stable to nucleic acid exonuclease and extremely stable to cells in tissues and exosomes15. Therefore, combined with clinical practice.Circular RNA, also known as circRNA, was first discovered in RNA viruses in 1976. In recent years, with the rapid development of high-throughput sequencing and other technologies, a large number of circRNAs have been discovered and their biological roles have been gradually investigatedIn order to deeply analyze the potential mechanism of liver metastasis of colorectal cancer, this study took lymph node negative with liver metastasis (LNLM1) as the object of study: such patients had relatively poor prognosis and suggested that the tumor had strong invasion ability. We intended to screen for circRNAs that were highly associated with colorectal cancer metastasis through high-throughput techniques, compared with lymph node negative without liver metastasis (LNLM0) in lymph node negative colorectal cancer patients. In order to avoid the error of metastasis event caused by tumor invasion of other adjacent organs, according to TNM stage, only patients with T stage T1-T3 were included in this study.In this study, Total RNA were extracted from the tumor tissues, corresponding adjacent tumor tissues and normal tissues from colorectal cancer patients with synchronous liver metastasis (LNLM1) or without liver metastasis (LNLM0). The total circRNA transcriptomic profiles was detected. A differential expression profile was obtained by comparing the microarray signals from the two groups, which showed that 289 circRNAs were differentially expressed in LNLM1 group with fold changes of 4/0.25. In an unsupervised clustering analysis of all the transcripts, we detected significant differences in the expression signatures of the three sets of samples Fig. . The sepWe examined these investigated circRNAs candidates in tumor tissues, adjacent tumor tissues and paired normal tissue samples from 40 LNLM1 patients and 40 LNLM0 patients. Results confirmed that hsa_circ_0124554 was consistent with the chip expression in all three groups, hsa_circ_0097743 was not statistically significant in the paracancer tissues, and hsa_circ_000958 and hsa_circ_0079875 were contrary to the chip expression in the paracancer group and normal tissues Fig. . Next, thigh and hsa_circ_0124554low group. We found higher expression of hsa_circ_0124554 was correlated with vascular invasion and liver metastasis, no correction was found in T stage, tumor differentiation or pathological types indicating that its transfer may be associated with tumor cells into the bloodstream  was used to predict the binding region in detail. As shown in Fig. Based on these data, the bioinformatic software catRAPID  was found in the 0-52aa amino acid site of AKT, so the team speculated that the interaction between hsa_circ_0124554 and AKT might involve the regulation of the ubiquitination level of AKT. By adding IGF1 as stimulator, we found the phosphorylation of AKT, downstream of AKT including GSK-3β and mTOR (both T308 and S473) and total expression of AKT was enhanced with hsa_circ_0124554 overexpression while could be attenuated by silencing of hsa_circ_0124554. No difference was identified without treating with IGF , AKT is phosphorylated at P13K, PDKl, and ILK at Thr308 in the catalytic domain and Ser473 in the regulatory domain. Only phosphorylation at these two sites can fully activate AKT. Activated AKT has been demonstrated to be highly expressed in many human cancers and to play a significant role in facilitating the migration, invasion, survival, and resistance of tumor cells. In this study, we found that hsa_circ_0124554 can specifically bind to AKT, reduce the degradation of AKTt by mediating the ubiquitination of AKT, and continue the continuous activation of downstream signaling pathways of Akt, including GSK-3β and mTOR, so as to generate events targeting the early metastasis of tumors.Taken together, our study identifies a new circular RNA, termed hsa_circ_0124554 regulated in lymph node negative CRC patients with synchronous liver metastasis. Hsa_circ_0124554 directly binds with AKT, activates the AKT associated signaling, promotes the metastasis of tumor and indicates worse prognosis of CRC. Our data provide a platform for further elucidating mechanisms underlying the metastasis of CRC and identifying diagnostic or therapeutic targets.Eighty CRC patient samples were obtained from 2003–2017, including their tumor tissues, corresponding adjacent tumor tissues and normal tissues. The CRC patients was diagnosed by surgery and pathology in the First Affiliated Hospital of Nanjing Medical University. After sample collection, liquid nitrogen was frozen and transported, and stored in −70°C deep cryogenic refrigerator. Written consents were approved by the patients enrolled in this study. The clinical stage was based on the 8th edition of the International Union Against Cancer (UICC) on Tumor-Node-Metastasis (TNM) staging system. All experiments were performed in compliance with government policies and the Helsinki Declaration. The individuals were informed about the study and gave consent prior to the specimen collection. All experiments were approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University.Total RNA from each sample was quantified using a NanoDrop ND-1000. The sample preparation and microarray hybridization were performed based on Arraystar’s standard protocols . In brief, total RNA was digested with RNase R  to remove linear RNAs and enrich for circular RNAs. Then, the enriched circRNAs were amplified and transcribed into fluorescent cRNA using a random priming method . The labeled cRNAs were hybridized onto the Arraystar Human circRNA Array V2 . Next, the slides were washed and the arrays were scanned using an Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was applied to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the R software limma package. Differentially expressed circRNAs with statistical significance between two groups were identified through Volcano Plot filtering. Significantly differentially expressed circRNAs were then further filtered through analysis of Fold Change. Hierarchical Clustering was performed to highlight distinguishable circRNAs expression patterns among samples.CRC cell lines including DLD-1, HCT 116, HT 29, LoVo and Caco 2 were purchased from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences , and were cultured and stored according to their instructions. The culture mediums include Dulbecco’s modified Eagle’s medium , 10% fetal bovine serum, 100 U/ml penicillin, and 100 µg/ml streptomycin. And all the cells were incubated a 5% CO2 humidified incubator at 37 °C.TRIzol reagent was applied to isolate RNA from colonrectal tissues or cell lines according to the manufacturer’s protocol. Then 1 μg RNA was used to synthesize cDNA, followed by analysis of gene expression on ABI 7900 qPCR system. Relative expression was normalized to GAPDH or U6. The detailed primer sequence information was listed in Supplementary Table Sanger sequencing was applied to determine the full length of the amplification products. The divergent primers  were designed to confirm the backsplice junction of Hsa_circ_0124554. The distinct product was amplified by outward-facing primers of Hsa_circ_0124554 and was confirmed by Sanger sequencing .Transcription was prevented by the addition of 2 mg/ml Actinomycin D or DMSO  as the negative control. Total RNA (2 μg) was incubated for 30 min at 37 °Cwith 3 U/μg of RNase R . After the treatment, we using qRT-PCR to detected the RNA expression levels of FOXP1 and circFOXP1.4 Cells were seeded on a cover glass-bottom confocal dish and cultured overnight. RNA FISH assay was performed using RNA FISH kit  according to manufacturer’s instruction. Nuclei were stained with 4,6-diamidino-2-phenylindole. Images were acquired on ZEISS LSM 880 with Airyscan .Fluorescence labeled oligonucleotide probes complementary to hsa_circ_0124554 were designed using the Clone Manager suite of analysis tools. 1 × 10Approximately 10 µg of total RNA was separated in a 1.2% agarose gel containing formaldehyde. The RNA was then transferred to Amersham hybond-N1 membranes . The membranes were hybridized with digoxin-labeled DNA oligonucleotides specific to hsa_circ_0124554 in Church buffer  at 37 °C and washed in 2× SSC  with 0.1% SDS at room temperature. The membranes were finally exposed on phosphorimager screens and analyzed using Quantity One or Image Lab software .The shRNA technology was used to suppress the endogenous expression of hsa_circ_0124554 in cell lines. In shRNA sequence or full length of hsa_circ_0124554 was designed or synthesized and cloned into lentivirus vectors following the manufacturer’s instruction by Genechem . Two independent target region was designed for each Forty-eight hours later, puromycin was added to medium for the selection of stable clones. The transfection efficacy was determined by qRT-PCR. The detailed shRNA sequence was listed in Supplementary Table Proteins were collected from cells and tissues according the manufactory’s protocol (KeyGEN BioTech). In brief, after extraction with RIPA buffer with protease inhibitor and phosphatase inhibitor cocktails  and quantified with a BCA kit , equal loading proteins of cell lysates or tissue lysates were added to each well of SDS PAGE. Followed by electrophoresising, transfer-membraning, and blocking with 5% non-fat milk in PBST for 1 h, then diluted primary antibodies were incubated at 4 °C overnight.Protein expression levels were detected by ECL Plus  with a Bio-Imaging System. The antibody information is listed in Supplementary Table 3 cells were seeded in 96-well plates and were cultured for 24 h, 48 h, 72 h and 96 h. The absorbance values at 450 nm were then measured using an enzyme immunoassay analyzer .Cell proliferation was measured using CCK-8 assays as previously reported. Briefly, 1 × 10Cells were harvested via trypsinization, washed in ice-cold PBS, fixed in ice-cold 75% ethanol in PBS, centrifuged at 4 °C and suspended in PBS. RNase A  was then added at a final concentration of 4 mg/ml, followed by incubation at 37 °C for 30 min. Then, 20 mg/ml propidium iodide  was added, and the sample was incubated for 20 min at room temperature. The cells were finally analyzed via flow cytometry .4 cells (per well) in serum-free medium were on. Medium containing 10% FBS was added to the lower chamber. After 24 h (48 h based on different cell lines) of incubation, cells on the bottom of the inserts were fixed and stained with 0.5% crystal violet solution.Transwell assays and invasion assays were conducted using Millicell cell culture inserts  according to the manufacturer’s instructions. Matrigel  was firstly coated to inserts, and then 8 × 105 cells per well. After 24 h incubation, scratch the monolayer with a new 200 ul pipette tip across the center of the well. After scratching, washing the well twice and replenishing the well with fresh medium containing no FBS. Growing cells for another 24 h, photographs were taken to estimate closure of the gap. The gap distance was quantitatively evaluated using software-ImageJ.Seeding cells into 6-well culture plate at a density of 5 × 10The biotin-labeled circRNA and the antisense were transcribed with a Biotin RNA Labeling Mix  and the T7 RNA polymerase , treated with RNase-free DNase I  and purified with a RNeasy Mini Kit . Cells were incubated with biotinylated RNAs and 60 μL of streptavidin agarose beads . The associated proteins were resolved by SDS-PAGE, and specific bands were excised. Proteins were eluted, digested and subjected to the Orbitrap Velos Pro LC/MS system . Data were analyzed by Proteome Discoverer and the resulting peak lists were used for searching the NCBI protein database with the Mascot search engine.RIP assay was performed according to protocols reported previously. The Magna RIP RNA-Binding Protein Immunoprecipitation Kit  was employed and conducted according to the manufacturer’s instructions. The IgG antibody were incubated in the magnetic beads’ suspension with rotation for 30 min was negative control. After washing with cold RIP wash buffer, the immunoprecipitated RNA was purified and detected by qRT-PCR.6 Cells with different treatment were suspended in 20 μL phosphate-buffered saline (PBS) were injected into the tail vein. The metastasis node was monitored by IVIS ® Spectrum in vivo imaging system every week. The liver metastasis model was established by spleen injection with these transfection groups of CRC cell lines. After 45 days, we sacrificed the mice and evaluated the liver metastasis ability.5- to 6-week-old male BALB/c nude mice were purchased from the animal center of Nanjing University. All animal experiments were performed under the experimental animal use guidelines of the National Institutes of Health. 1 × 10χ2 tests and the Student’s t-test analysis of variances were used to evaluate statistical differences in demographic and clinical characteristics. Pearson correlation analysis was used to analyze the relationship of associated factors. Statistical analysis was performed using STATA10.0. P < 0.05 was considered significant.Data are presented as mean ± SEM. The Supplement for review


Meningiomas are the most common primary intracranial tumors, but meningioma metastases are rare. Accordingly, the clinical workup, diagnostic testing, and molecular classification of metastatic meningioma is incompletely understood. Here, we present a case report of multiply recurrent meningioma complicated by liver metastasis. We discuss the patient presentation, imaging findings, and conventional histopathologic characterization of both the intracranial lesion and the metastatic focus. Further, we perform multiplatform molecular profiling, comprised of DNA methylation arrays and RNA-sequencing, of six stereotactically-guided samples from the intracranial meningioma and a single ultrasound-guided liver metastasis biopsy. Our results show that DNA methylation clusters distinguish the liver metastasis from the intracranial meningioma samples, and identify a small focus of hepatocyte contamination with the liver biopsy. Nonetheless, DNA methylation-based classification accurately identifies the liver metastasis as a meningioma with high confidence. We also find that clustering of RNA-sequencing results distinguishes the liver metastasis from the intracranial meningiomas samples, but that differential gene expression classification is confounded by hepatocyte-specific gene expression programs in the liver metastasis. In sum, this case report sheds light on the comparative biology of intracranial and metastatic meningioma. Furthermore, our results support methylation-based classification as a robust method of diagnosing metastatic lesions, underscore the broad utility of DNA methylation array profiling in diagnostic pathology, and caution against the routine use of bulk RNA-sequencing for identifying tumor signatures in heterogeneous metastatic lesions. Meningiomas metastases are rare, occurring in less than 1% of all cases . HoweverThe patient is a 52-year-old female who initially underwent resection of a sporadic intracranial meningioma, WHO grade I, at age 27. At age 38, she underwent salvage resection for multifocal intracranial recurrence, with surgical pathology revealing transformation to atypical meningioma, WHO grade II. At age 48, she again presented with intracranial progression and was treated with external beam radiotherapy, followed by stereotactic radiosurgery to satellite intracranial lesions at age 50. At age 52, a surveillance MRI of the brain revealed further intracranial recurrence Fig. a, and shNF2. However, we did observe 4 CNVs that were present in the intracranial lesions but lost in the liver metastasis, which may have been driven, in part, by the underlying normal hepatocyte contamination in the metastatic sample. Notably, these changes did not appear to affect DNA methylation-based tumor classification, and could, alternatively, have been reflective of metastasis of a meningioma clone not captured in the 6 intracranial meningioma samples we profiled. In summary, DNA methylation profiling indicates that metastatic meningioma, while containing detectable contaminating cells, is primarily composed of meningioma cells with a similar CNV profile to matched intracranial samples.To elucidate the molecular features associated with the meningioma metastasis, we performed DNA methylation profiling and RNA-seq on 6 spatially distinct sites from the intracranial meningioma and the liver metastasis. Intracranial samples, as well as the liver core biopsy, were flash frozen in liquid nitrogen immediately after collection, and DNA and RNA were simultaneously extracted from the same sample from each site. Unsupervised hierarchical clustering of methylation data revealed that the liver metastasis demonstrated a distinct epigenetic profile from the 6 intracranial lesions Fig. a, likelyWe next used RNA-seq and differential expression analysis to compare the transcriptomes of the 6 intracranial samples with the liver metastasis. Unsupervised hierarchical clustering of transcriptomic data segregated the liver metastasis from the intracranial samples Fig. a. NotablIn summary, we performed DNA methylation profiling and RNA-seq of intracranial and metastatic samples from a recurrent meningioma. We found that both DNA methylation and RNA-seq distinguished the metastasis from intracranial meningioma samples, but while DNA methylation-based classification correctly identified the metastatic sample as meningioma in origin, RNA-seq of the same metastatic sample was confounded by hepatocyte contamination, even though the vast majority of the sample was comprised of meningioma cells. More broadly, these data further support the diagnostic utility of epigenetic profiling for meningioma, and suggest that DNA methylation-based classification of central nervous system tumors may be a robust assay for identifying metastatic lesions. However, these data also caution against RNA-seq based analysis of heterogeneous metastatic lesions, perhaps foreshadowing the potential importance of single cell approaches to elucidate the molecular mechanisms underlying metastasis. Finally, our findings underscore the importance of classical diagnostic features, such as histopathology and immunohistochemistry, for clearly establishing the diagnosis of metastatic meningioma when emerging technologies may be limited in ways that have yet to be rigorously defined, as is illustrated by this case.This study was approved by the authors institutional review board . The patient provided written informed consent for research on both the liver and brain tumor samples (IRB #10–01318). We stereotactically collected 6 spatially distinct samples from the intracranial meningioma  during craniotomy for tumor resection, and 1 sample from the liver metastasis via ultrasound-guided fine needle aspiration, for histopathology, DNA methylation profiling, and RNA-sequencing.DNA and RNA were isolated from the same biopsy specimen for each sample using the All-Prep Universal Kit . Flash frozen tumor samples were thawed in RLT Plus Buffer with beta-mercaptoethanol and were mechanically lysed using a TissueLyzer (QIAGEN) with stainless steel beads at 30 Hz for 90 s. QiaCubes were used for standardized automated nucleic acid extraction per the manufacturer’s protocol (QIAGEN). RNA quality was assessed by chip-based electrophoresis , and clean-up was performed as needed using the RNeasy kit (QIAGEN). DNA quality was assessed by spectrophotometry, and clean-up was performed as needed using DNA precipitation.P < 0.05 in all samples were included for further analysis. Data were normalized using functional normalization [Methylation analysis was performed according to the manufacturer’s instructions on the Illumina Methylation EPIC Beadchip. Preprocessing and normalization were performed in R using the minfi Bioconductor package , 18. Onllization , CNV estlization , and tumlization  were carhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were subsequently mapped to the human reference genome hg19 using HISAT2 with default parameters [Library preparation was performed using the TruSeq RNA Library Prep Kit v2  and 50 bp single end reads were sequenced on an Illumina HiSeq 2500 at the Center for Advanced Technology at the University of California San Francisco. Quality control of FASTQ files was performed with FASTQC, and after trimming of adapter sequences, reads were further filtered to remove bases that did not have an average quality score of 20 within a sliding window across 4 bases  > 1 (“Filtered_TPM > 1”) and genes enriched in the liver metastasis (“Up_in_Met”) or intracranial samples (“Down_in_Met”) based on combined absolute expression and relative fold change thresholds.


C. elegans hermaphrodites, sto-3 promoter has been previously shown to drive gene expression in RIBL/R neurons and in three unidentified non-neuronal cells in the tail . (A) We have found that in C. elegans males, in addition to RIBL/R, two pairs of bilaterally-symmetrical tail neurons show strong Psto3::wrmScarlet expression ). These neurons send their processes to rays 4 and 8 of the male tail. In the figure, right lateral aspect is shown; arrows indicate rays containing processes expressing Psto3::wrmScarlet. (B) All rays in the male tail are innervated by A and B type neurons . To identify which neuron type expresses the sto-3 reporter, we have crossed Psto3::wrmScarlet transgenic males with a Ppkd-2::GFP reporter strain MT11318, which expresses GFP in the B-type neurons of rays 1-5, 7-9 and not in the A-type neurons . In the F1 cross-progeny males, which also carried the ceh-30(n3714) mutation in the background, Psto-3::wrmScarlet expression is colocalized with Ppkd-2::GFP for both pairs of ray neurons expressing Psto-3::wrmScarlet. This indicates that sto-3 is expressed in the R4BL/R and R8BL/R ray neurons. Left lateral aspect; arrows point at the cell bodies of Psto3::wrmScarlet-expressing neurons.In zfEx898[Psto-3::wrmScarlet + lin-15(+)]; lin-15 (n765ts) X. Psto-3::wrmScarlet transcriptional fusion. The plasmid was made by cloning 971 bp promoter region of sto-3 into a wrmScarlet-unc-54 3’UTR vector with a pUC19 vector backbone.Strains: QW1876.


Stress granules (SGs) are stress-induced RNA–protein assemblies formed from a complex transcriptome of untranslating ribonucleoproteins (RNPs). Although RNAs can be either enriched or depleted from SGs, the rules that dictate RNA partitioning into SGs are unknown. We demonstrate that the SG-enriched NORAD RNA is sufficient to enrich a reporter RNA within SGs through the combined effects of multiple elements. Moreover, artificial tethering of G3BP1, TIA1, or FMRP can target mRNAs into SGs in a dose-dependent manner with numerous interactions required for efficient SG partitioning, which suggests individual protein interactions have small effects on the SG partitioning of mRNPs. This is supported by the observation that the SG transcriptome is largely unchanged in cell lines lacking the abundant SG RNA-binding proteins G3BP1 and G3BP2. We suggest the targeting of RNPs into SGs is due to a summation of potential RNA–protein, protein–protein, and RNA–RNA interactions with no single interaction dominating RNP recruitment into SGs. Stress granules (SGs) are ribonucleoprotein (RNP) assemblies that form during stress when translation initiation is limited . SGs areBased on super-resolution microscopy, mammalian SGs formed during arsenite stress are nonuniform in nature and contain local regions of protein and RNA concentration, referred to as SG cores . In addiAn additional possibility is that RNA–RNA interactions contribute to defining the RNA composition of SGs. Consistent with this model, the transcriptome of protein-free in vitro RNA assemblies, formed from total yeast RNA, shows remarkable overlap with the transcriptome of yeast SGs . MoreoveGAPDH, are largely depleted from SGs  and ΔΔG3BP1/2 cell lines. Taken together, our results indicate that while G3BP1 binding can promote RNA targeting to SGs, it is not required for SG localization. We suggest a model in which RNA localization to SGs arises through the summation of many RNA–RBP and RNA–RNA interactions, which each individually play a small role in localization, but through synergistic effects lead to the observed RNP partitioning into RNP granules.Supplemental Fig. S1). Using this reporter system, with a downstream SV40 poly(A) signal, we examined how adding sequences or binding sites for RBPs affected SG partitioning.The firefly luciferase was used as an RNA reporter to assess how different elements affect RNP partitioning into SGs for two reasons. First, luciferase is not endogenous in mammalian cells, thus, we can eliminate the endogenous background in our analysis. Second, single molecule fluorescent in situ hybridization (smFISH) analyses revealed that luciferase mRNA is poorly enriched in SGs, with ∼15% of its cytoplasmic RNA molecules in SGs upon an hour of sodium arsenite treatment  and significantly increased compared to the luciferase reporter (16.7%) A–C. ThusTo determine whether one or more elements in the NORAD RNA were promoting SG partitioning, we constructed luciferase mRNAs with either each half, each of the four quarters, or each eighth of the NORAD RNA inserted into the same site in the 3′-UTR.Supplemental Table S1). For example, either half of NORAD yielded ∼60% reporter enrichment within SGs, each quarter gave between ∼55% and 35% recruitment, and each eighth gave between 22% and 50% recruitment . These increases are not solely due to increased length since insertion of additional luciferase sequences of similar lengths in the same position within the 3′-UTR gave only limited increases in SG recruitment . Indeed, the relationship between length and SG enrichment only gave a small positive slope, suggesting that length, per se, is not the major driving force in SG enrichment and that the previously defined length dependence is likely due to long transcripts harboring more RBP sites and/or RNA–RNA interaction motifs. Similarly, insertion of antisense sequences from NORAD typically resulted in less recruitment of the reporter into SGs than corresponding sense-strand inserts . The limited correlation with length is consistent with the SG transcriptome in which there is an overall length determinant; however, mRNAs of similar length can show differences in their partitioning into SGs . In the NORAD RNA there are 13 AU-rich elements (AREs) with an AUUUA motif and 19 Pumilio recognition elements (PREs), which can bind Pumilio and other RBPs  binding, into the luciferase 3′-UTR. We observed a significant decrease in luciferase reporter, from 71% to 50%, in SGs B,C. SincTaken together, these observations argue that NORAD contains multiple elements that can increase an mRNAs partitioning into SGs. Moreover, the correlation of enrichment with protein binding sites suggests that at least some of the SG enrichment will be due to RBPs targeting mRNAs to SGs.To directly examine how the interactions with mRNA-binding proteins affect mRNA partitioning into SGs, we next focused on G3BP1 for three reasons. First, G3BP1 is one of the most abundant cytoplasmic RBPs . Second,To determine if G3BP1 could increase a given RNA's localization to SGs, smFISH was used to monitor localization of the luciferase RNA reporter with and without G3BP1 tethering sites. We used the λN-BoxB system to artificially tether G3BP1 to the luciferase mRNA . λN was Supplemental Fig. S3), suggesting the effect is not a result of G3BP1 overexpression. Cells that highly over-expressed G3BP1, which leads to the formation of constitutive SGs . To estimate Pearson's r value for each fit, we linearized the data by performing a logarithmic transformation . For both TIA1 and G3BP tethering experiments, curve fitting yielded a fit that showed a maximum R value for the fit in which n = 2 . Given the assumptions in this model and the cell to cell variation in SG enrichment, these numbers should not be taken literally, but should be used as “ballpark” estimates.Third, assuming all SG-targeting interactions are similar, one can make an estimate for the number of interactions a given mRNA has with SGs. From this curve, we made a transcriptome-wide first approximation of the number of interactions a given mRNA forms with SGs . In this analysis, we counted PREs, which serve as binding sites for Pumilio , and that when we summed the total number of SG RBPs for each segment of NORAD there was a good correlation between SG CLIP sites and NORAD segment enrichment . Taken together, these results are consistent with a model in which RBPs can act in tandem to define the RNA composition of SGs.We also estimated the number of RBPs bound to NORAD segments based on CLIP sites from a database containing a meta-analysis of RBP CLIP studies . Total RNA and SG core purification via PABPC1 pulldown yielded reproducible transcriptomes . SG RNA transcriptomes based on the purification of SG cores with PABPC1 antibody showed little similarity to total RNA transcriptomes, with 3251 transcripts enriched in SGs (P < 0.01), and 3693 transcripts depleted from SGs (P < 0.01)  C. Thus,  < 0.01) . Additio < 0.01) , which cSupplemental Figs. S8,S9; Since ΔΔG3BP1/2 cell lines form SGs during sorbitol treatment, we planned on analyzing SG transcriptomes from sorbitol-treated cells with and without G3BP1/2 expression. Before sequencing PABP-containing SGs from WT and ΔΔG3BP1/2 cells during sorbitol stress, we first examined whether the RNA composition of sorbitol-induced SGs was similar to arsenite-induced SGs. Thus, we compared the transcriptome of SG cores purified via PABPC1 and GFP-G3BP1 immunopurification during hyperosmotic stress induced by sorbitol . Strikingly, sorbitol-induced SGs contained a similar transcriptome regardless of whether cells expressed or lacked G3BP1 and G3BP2 (R = 0.94), suggesting that the enrichment of RNA in SGs is largely independent of G3BP during sorbitol stress. We interpret this observation to argue that G3BP1 and G3BP2 do not generally affect the mRNAs recruited to SGs.We then examined whether G3BP1 and G3BP2 affected the mRNAs partitioning into SGs during sorbitol stress. To test this possibility, we purified sorbitol-induced SG cores from WT and ΔΔG3BP1/2 U-2 OS cells using antibodies to PABPC1 .Another possibility is that only transcripts that exhibit G3BP binding capability would show differential recruitment to SGs. Thus, we utilized data from a recent eCLIP study which examined G3BP eCLIP targets . HoweverG3BP is required for SG formation during arsenite stress, but not sorbitol stress. Thus, one possibility is that G3BP simply does not have any influence over the partitioning of RNAs during sorbitol stress. To test this possibility, we examined if tethered G3BP1 could target the luciferase mRNA to SGs under sorbitol stress in a manner similar to what was observed under arsenite stress. We observed that G3BP1 tethering led to a significant and reproducible increase in luciferase localization to SGs during sorbitol stress E. Thus, A final possibility for why the SG transcriptome does not change in the ΔΔG3BP1/2 cell lines is that depletion of G3BP affects all transcripts equally, which might be missed in our sequencing data. Thus, we examined whether global RNA recruitment to SGs was altered in ΔΔG3BP1/2 cells. By smFISH, we detected no discernable difference in the localization of AHNAK F,G. ThisIn this work, we present evidence that specific RNA-binding proteins can localize mRNPs into SGs. The critical observation is that tethered G3BP1, TIA1, or FMRP can increase the partitioning of the luciferase mRNA into SGs , 3. AlthWe present two lines of evidence that the partitioning of an mRNP into SGs will be based on multiple elements acting in an additive manner. First, we observed that multiple elements within the NORAD RNA were sufficient to increase the SG enrichment of a luciferase reporter RNA . Second,−ΔG/RT. Moreover, we observed that the SG enrichment of individual RNPs was correlated with the number of interactions in a logarithmic manner . In this model, deletion or depletion of any single RBP would have a negligible effect on RNA localization to SGs, which is supported by the evidence that G3BP1 and G3BP2 deletions had essentially no effect on the SG transcriptome  in conjunction with 1 µg appropriate luciferase reporter construct  using Jet Prime reagent. Transfection of pRP2854 alone was used as a negative control. Twenty-four hours following transfection, cells were split from a six-well plate to a 10 cm dish. After another 24 h, media was replaced with new media containing 1 µg/mL puromycin to begin selection for cells with genomic integration. Following 24 h with puromycin selection, media was replaced with fresh puromycin. After all cells were dead in the negative control plate, media was replaced with fresh media lacking puromycin for 48 h. This is an optional step to help get rid of any residual plasmid. Puromycin was then added for another 48 h to finalize the selection. Single colony selection was not done for these experiments.http://www.biosearchtech.com/stellaris-designer (version 4.2). AHNAK and NORAD smFISH probes, labeled with Quasar 670 dye, and firefly luciferase probes, labeled with Quasar 570, were ordered from Stellaris (Biosearch Technologies).Custom Stellaris smFISH probes against AHNAK, NORAD, and firefly luciferase transcripts were designed with Stellaris RNA FISH Probe Designer (Biosearch Technologies), available online at https://biosearchassets.blob.core.windows.net/assets/bti_custom_stellaris_immunofluorescence_seq_protocol.pdf).Sequential immunofluorescence and smFISH on fixed U-2 OS cells was performed with Stellaris buffers or homemade buffers  accordinBriefly, U-2 OS cells were seeded on sterilized coverslips in six-well tissue culture plates. At ∼80% confluency, media was exchanged 1 h before experimentation with fresh media. After stressing cells (see section “Stress conditions”), the media was aspirated and the cells were washed with prewarmed 1× PBS. The cells were fixed with 500 µL 4% paraformaldehyde for 10 min at room temperature. After fixation, cells were washed twice with 1× PBS, permeabilized in 0.1% Triton X-100 in 1× PBS for 5 min and washed once with 1× PBS.For IF detection, coverslips were incubated in primary antibody for 1 h. Coverslips were washed three times with 1× PBS for 10 min each wash. Then cells were incubated in secondary antibody (Thermo Fisher Scientific A-31553). Again, coverslips were washed three times with 1× PBS for 10 min each wash. Then, cells were treated with smFISH Buffer A for 5 min. Coverslips were transferred to a humidifying chamber with smFISH probes and placed in the dark at 37°C for 16 h. Coverslips were placed in Buffer A for 30 min in the dark, washed with Buffer B for 5 min and placed onto a slide with VECTASHIELD Antifade Mounting Medium with DAPI . For assays requiring quantification of smFISH probes in stress granules, VECTASHIELD Antifade Mounting Medium without DAPI was used .In order to maintain consistency, the same protocol was utilized in IF only experiments; however, the portions of the protocol calling for smFISH were omitted. Antibodies that were used include PABP (Abcam ab21060), G3BP (Abcam ab56574). In all imaging experiments at least 10 cells were imaged.https://www.biosearchtech.com/support/education/stellaris-rna-fish). Briefly, oligos were labeled by incubating 4 µL of 200 µM pooled oligonucleotides (Integrated DNA Technologies), 1 µL of TdT (Thermo Fischer Scientific EP0161), and 6 µL of ddUTP (Axxora JBS-NU-1619-633) fluorophore for 8 h. After 8 h, another 1 µL of TdT enzyme was added and the reaction was allowed to continue overnight. Probes were then ethanol precipitated by adding 164.5 µL of nuclease free water, 0.5 µL of 0.5 mg/mL linear acrylamide, 20 µL of 3 M sodium acetate (pH 5.5) and 800 µL of prechilled 100% ethanol. This mixture was then placed at −80°C for at least 20 min. Labeled oligos were pelleted by centrifugation at 16,000g at 4°C. Probes were then washed three times by adding 1 mL of 80% ethanol and centrifugation at 16,000g at 4°C. After washing, the pellet was allowed to air dry for 10 min and probes were resuspended in 25 µL of nuclease free water.smFISH probes were labeled based on a recent protocol . OligonuZ-sections were taken for each experiment. All images in the manuscript are processed by FIJI  with a 100× objective and a PCO Edge sCMOS camera. At least five images with 20  by FIJI  or ImariTo measure the fraction of smFISH spots in stress granules, deconvolved images were analyzed using Bitplane Imaris image analysis software as described in detail in a previous manuscript . In shor2.Human osteosarcoma U-2 OS cells expressing G3BP1-GFP (Paul Taylor Laboratory), U-2 OS cells, and U-2 OS ΔΔG3BP1/2  were use2 TC-treated culture dishes . One hour prior to stress, cell culture media was exchanged with fresh media. Cells were then stressed with either NaAsO2 or sorbitol (see section “Stress conditions”). After stress, cells were washed once with media, transferred to falcon tubes, and pelleted at 1500g for 3 min. Upon aspirating the media, the pellets were immediately flash-frozen in liquid N2 and stored at −80°C until isolation of mammalian SG cores was performed. PABPC1 SG cores were purified as previously described for the purification of G3BP cores; however, instead of using anti-GFP for the pulldown of SG cores, 20 µL of anti-PABPC1 (ab21060) was used  for 1 h. For osmotic stress, cells were stressed in 0.5M D-sorbitol (Sigma-Aldrich S1876) for 2.5 h. Cells were fixed after the completion of each stress with 4% paraformaldehyde (Fisher Scientific NC0179595).RNA quality was assessed by TapeStation analysis at the Biofrontiers Institute Sequencing Facility. Paired-end cDNA libraries were prepared at the Biofrontiers Institute Sequencing Facility using the KAPA HyperPrep with RiboErase. cDNA libraries were sequenced on a NextSeq High output 150 cycle (2 × 75).http://www.geneontology.org/). Transcript lengths were acquired from Ensembl's Biomart Tool. All sequencing data can be found at NCBI GEO GSE119977.Read quality was assessed using fastqc. An index genome was acquired from GENCODE (Release 19 GRCh37.p13). Reads were aligned using hisat2. Differential expression analysis was performed using Cuffdiff (version 2.2.1) with the default parameters . Gene Onn, was allowed to vary from one to 10 and the best fit was chosen by examining the Pearson's r value of each line. The data was linearized by performing a log-transformation of the data and r values, slope, and intercept positions were calculated using the sklearn package in Python. The code for this analysis is available at the following github repository: https://github.com/tmatheny/stress_granule.Curve fitting was performed using Python's scikit-learn package. The median percent enrichment within stress granules was plotted against the number of box-b sites. The initial number of potential interactions the luciferase can form with SGs, which we refer to as Supplemental material is available for this article.


The potential of double-stranded RNAs (dsRNAs) for use as topical biopesticides in agriculture was recently discussed during an OECD (Organisation for Economic Co-operation and Development) Conference on RNA interference (RNAi)-based pesticides. Several topics were presented and these covered different aspects of RNAi technology, its application, and its potential effects on target and non-target organisms . This review presents information relating to RNAi mechanisms in vertebrates, the history of safe RNA consumption, the biological barriers that contribute to the safety of its consumption, and effects related to humans and other vertebrates as discussed during the conference. We also review literature related to vertebrates exposed to RNA molecules and further consider human health safety assessments of RNAi-based biopesticides. This includes possible routes of exposure other than the ingestion of potential residual material in food and water , the implications of different types of formulations and RNA structures, and the possibility of non-specific effects such as the activation of the innate immune system or saturation of the RNAi machinery. The Conference was divided into three sessions. The first session provided a summary of the state-of-the-art of this technology: molecular mechanism and relevant RNAi pathways, current understanding of RNAi in different organisms, specificity level and its potential impact on non-target species, as well as the challenges associated with achieving RNAi efficacy in insects. The second session dealt with factors related to variation in insect responsiveness to environmental dsRNA, the environmental dissipation of dsRNA molecules in soil, water and plants, and the aspects of RNA-seq (RNA-sequencing) to validate RNAi data. It also presented an overview of available literature on the possible effects of exogenous dsRNA in humans and other vertebrates and addressed the regulatory experience with dsRNA applications in human therapeutics.The OECD (Organisation for Economic Co-operation and Development) Conference on RNA interference (RNAi)-based pesticides provided an overview of the current state of the art related to externally applied double-stranded RNA (dsRNA)-based products, also called exogenously or topically applied dsRNA. The purpose of the meeting was to facilitate exchanges between policymakers, academia, and industry on the implications of these products in health, the environment, and regulation, and to solicit inputs and recommendations based on these discussionsThe third and final session summarized regulatory and risk assessment experiences whereby presenters identified “problem formulation” as a regulatory pathway similar to that for other biologically based active ingredients, explored better decision making ideas, and considered a case-by-case approach to assess ecological risks. It also reviewed the use of formulations to overcome hurdles for controlling insects that are recalcitrant to dsRNA and the experience of experts on the regulation of RNAi-based genetically engineered (GE) crops. To date, there is no precedent of externally applied RNAi-based products approved by regulatory agencies, even though the technology has already been developed and approved for genetically engineered (GE) crops. Although there are substantial differences between RNAi-based GE crops and biopesticides that impact the risk assessment of both products differently , the experience of experts on regulation of RNAi-based GE crops provides helpful information on the safety of this new technology, guides interpretations of new studies, and supports regulatory requirements.This report focuses on the third OECD meeting session that presented the possible effects of RNAi-based biopesticides in humans and other vertebrates. To introduce RNAi technology and its mode of action, the RNAi mechanism and main gene silencing pathways are described. RNAi technology comes as an alternative crop protection solution that enables a more specific, safer, and environmentally friendly tool for agricultural production . Even thRNAi is a post-transcriptional process ubiquitous in eukaryotes that results in degradation or translational suppression of specific messenger RNA (mRNA) molecules, leading to a reduction in protein production. Suppression of the gene product occurs inside the cells and is initiated from either exogenous dsRNA or RNA molecules originating internally from the nucleus (reviewed by The siRNAs are short duplex sequences of ∼21–23 nucleotides (nt)  derived The miRNAs are processed from their long dsRNA precursors into short ssRNA molecules that feed into an analogous pathway to that described above for siRNAs, leading to gene product suppression of complementary mRNA targets. This can occur through both translational suppression  and mRNAAs presented above, RNAi is a highly conserved mechanism in eukaryotes for gene expression regulation. Small RNAs such as siRNAs and miRNAs are ubiquitous in commonly consumed plant and animal-derived foods. A number of small RNAs with perfect complementarity to human and animal genomes and transcriptomes have been identified in crops widely consumed globally, such as soybean, corn, and rice . Corn spTM potatoes with reduced acrylamide production potential and blackspot bruise resistance  epithelial cells that a given RNA must transverse and if such an RNA is to reach the bloodstream, both apical and distal membranes of the vascular endothelium. This must also occur for an RNA to cross into a distal tissue, e.g., the RNA would have to again leave the vasculature through the endothelium and cross the epithelium of another tissue to have the potential to regulate gene expression in that tissue. Each of these cellular membrane layers is a lipid bilayer that is highly impermeable to polar macromolecules such as RNA . Any RNAThe efficacy of these barriers can be observed in therapeutic studies in which injected RNA drugs that are unformulated are both readily degraded and rapidly excreted . ObservaThe above discussion on biological barriers to exogenous RNA molecules emphasizes the oral route of exposure as the relevant route of exposure to RNAs in foods, including those that confer traits to GE crops. The oral route is also of key importance to the potential impact of any RNA residues in foods resulting from topical uses of nucleic acids in an agricultural setting. Based on the history of safe consumption of RNA molecules in the diet (including dsRNAs with sequence identity to the consuming organism) and the biological barriers detailed above, the weight of the evidence suggests that ingested nucleic acids are neither absorbed to a significant extent nor capable of triggering a biological response in a consuming organism. This is largely due to the collective impact of each of the individual biological barriers discussed above, leading to a significant reduction in dsRNA levels in terms of the amount available for possible functional activity relative to the amount ingested; these barriers result in insufficient copies being available within a given cell to mediate biological function . Plant mThe concept of potential uptake and activity of dietary small RNAs was evaluated in the context of rice miRNAs. In a 2012 peer-reviewed publication, it was suggested that a specific miRNA in rice (miR168a) was absorbed into the bloodstream of ingesting mice and into systemic tissues where it reduced levels of a targeted protein  and mediated downstream physiological impacts on cholesterol levels . PossiblWhen droplet digital PCR was leveraged to evaluate miRNA uptake from feeding experiments in rhesus monkeys, uptake from the diet was not evident over a time course . This PCDespite substantial evidence from well-controlled feeding studies in rodents and humans that calls into question the ability for RNA from the diet to undergo significant uptake and have putative activity in mammals , the diein vivo data demonstrate modest changes in relative protein levels of approximately 1–3 fold (by Western blot with anti-human antibodies) in a small sample size (n = 4) at a single time point (7 days). Without a deeper understanding of the comparative nutritional components of the fed diets  and evaluation of the normal range of expression variability of the evaluated target proteins, along with thorough histopathological assessment of these animal intestines, the physiological relevance of the slight changes in mRNA and protein levels and the noted changes in morphology following corn feeding is difficult to assess. Furthermore, the results of this paper are inconsistent with results demonstrating no uptake of corn miRNAs into the bloodstream of mice after 2 weeks of oral dosing  or, if it occurs, that sufficient quantities are taken up to exert a biologically relevant effect  noted that, “Based on the current knowledge, gained in pharmaceutical research and development, RNAi molecules show limited bioavailability, quick turn-over , fish , chicken , and turtle (Trachemys scripta) and concluded that there is enough evidence to support an effective, potent, and reproducible RNAi response in these organisms using invasive delivery techniques, such as microinjections and electroporation. Similar responses could also be observed in birds (Zonotrichia leucophrys gambelli) when siRNAs were directly administrated into their brains (Petromyzon marinus), gene suppression and phenotype effects were observed via injection of siRNA into embryos. Larvae of lampreys also showed gene suppression after feeding on a siRNA with a liposome-based formulation as the transfection reagent, however, feeding naked siRNA (without transfection reagents) failed to induce a response (Colinus virginianus) and the animals were observed for 14 days. Body weights, signs of toxicity, abnormal behavior, and mortality were recorded, and no adverse effects were observed, indicating an absence of non-specific RNAi responses , therefore, respiratory exposure should be considered as another potential exposure route. During a pesticide spray application, a significant portion of the product may not reach the intended target and may be transported to other areas through spray drift , which cChallenges with the development of inhaled RNA therapeutics demonstrate how challenging it is to use this route to effectively deliver RNA molecules in humans. Some recent inhaled RNA therapeutic studies have shown advances in the delivery of RNAi-based drugs to the lungs . At present, there seems to be an absence of published data concerning the potential biological impact of inhalation of RNA molecules. Given that these can potentially be immunostimulatory molecules  per the literature, a non-sequence specific inflammatory response may occur upon significant exposures, hence the recommendation to use the appropriate PPE to limit inhalation exposures for spray applications as with any other sprayed crop protection product.It is widely known that different species respond differently to environmental dsRNA . In inseAnother approach to enhance RNAi response in some pests is the development of formulations  aiming tThe wide variety of substances and technologies that can be developed and optimized into a cost-effective formulation to enhance dsRNA delivery and stability in the field can be a reality in the future. The recommendation raised during the OECD meeting to mitigate this risk is to consider each new formulation individually in a case-by-case assessment.in vitro systems that leverage transfection reagents and high RNA concentrations, and in some cases, in animal models. These responses are mediated via receptors that interact with dsRNA such as the Toll-like receptors , and enzymes including the dsRNA binding protein kinase PKR, and the RIG-I and MDA-5 RNA helicases  under experimental conditions permitting high levels of RNA exposure . As revielicases . In animelicases  and therIn a set of mouse and blood cell studies from the pharmaceutical industry, sequence-dependent stimulation of the innate immune response in mice was demonstrated following intravenous delivery of dsRNAs (siRNAs) encapsulated in liposomes . HoweverAlthough a review article has indicated immunostimulation as a possible hazard from oral exposure to dsRNAs in agriculture , biological barriers, and their fate TR and JP contributed equally to draft the work and revised it critically for important intellectual content.


P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis ( Gastric cancer can metastasise to the peritoneal cavity; predicting in which patients this will occur is important for clinical management of the disease. Here, the authors use multi-photon imaging to derive a collagen signature of the primary cancer that allows the prediction of metastasis. The peritoneum is the most frequent metastatic site for GC after radical surgery3. The median survival time is only 4 months once peritoneal metastasis is diagnosed, compared with 14 months in GC without peritoneal metastasis3. Serosal invasion is the strongest indicator of peritoneal metastasis in GC5. Most patients with GC with serosal invasion will succumb to peritoneal metastasis within 2 years after surgery despite radical gastrectomy6. Therefore, early detection of peritoneal metastasis is integral to improving the prognosis of GC patients with serosal invasion.Gastric cancer (GC) is one of the most commonly diagnosed malignant diseases and the second leading cause of cancer-related deaths worldwide7. To date, the safety and efficacy of EIPL have been proven, but the long-term oncological outcomes are still unclear8. IPC was used to eliminate suspected malignant cells through locoregional chemotherapy. Several studies have reported that IPC is favourable for improving the oncological outcome and decreasing an incidence of peritoneal metastasis in GC with serosal invasion10. However, a considerable number of GC patients will not suffer from peritoneal metastasis despite serosal invasion. Moreover, IPC is costly and associated with an increased rate of postoperative complications, including digestive fistula, haematologic toxicity and systemic sepsis11. Thus, accurate prediction of the risk of peritoneal metastasis after radical gastrectomy is extremely important for the choice of IPC in GC with serosal invasion.Currently, there are two therapies for preventing peritoneal metastasis for GC patients with serosal invasion: extensive intraoperative peritoneal lavage (EIPL) and intraperitoneal chemotherapy (IPC)12. Some imaging modalities, including computed tomography (CT) and endoscopic ultrasonography (EUS), are common examination tools for GC; however, the accuracy of these imaging modalities for the diagnosis of peritoneal metastasis is not satisfactory13, and it is not until patients are suffering from peritoneal metastasis that these imaging modalities can identify the outcome. Considering the limited performance of the clinical variables and the high complication rates of IPC, a novel biomarker is needed for the prediction of peritoneal metastasis in GC with serosal invasion after radical gastrectomy to influence decision making.Peritoneal metastasis is difficult to predict on clinical grounds. Cytologic examination of peritoneal lavage, which has been used to assess the risk of peritoneal metastasis in GC with serosal invasion, has been reported to lack sensitivity because a large number of patients still die from peritoneal metastasis even though they have negative cytologic results17. The increased collagen density around cancer cells directs local invasion and metastasis14. Moreover, the radial alignment of collagen at the tumour-stroma boundary improves the invasiveness of cancer cells17. Previous investigations have shown that serosal changes predict peritoneal metastasis in GC with serosal invasion19. As the main component of the serosa, collagen can be quantified to determine the serosal changes in GC20. Thus, we hypothesize that collagen alterations in the tumour microenvironment of the serosa are associated with peritoneal metastasis in GC with serosal invasion.It has been revealed that collagen alterations in the tumour microenvironment are correlated with cancer dissemination and prognosis21. Multiphoton imaging is sensitive to changes in collagen and provides multiple quantitative metrics, including morphological and textural features, for diagnosing and predicting diseases23. Here, we propose a collagen signature with high-throughput quantitative collagen features that are automatically extracted using multiphoton imaging, to comprehensively quantify the extent of collagen alterations in the tumour microenvironment.Over the past decade, multiphoton imaging has emerged as a powerful tool for visualizing the assembly of collagen in tissues at a supramolecular level because of its underlying physical origin25. The least absolute shrinkage and selection operator (LASSO) regression is an effective approach for the regression of high-dimensional parameters and has been broadly applied for prognostic analysis26.The integration of multiple biomarkers into a single signature has the potential to substantially improve predictive value over that of a single biomarkerTherefore, in this study, we use multiphoton imaging and LASSO regression to establish a multi-feature-based classifier, i.e., a collagen signature, to predict peritoneal metastasis. For clinical use, we develop and validate a competing-risk nomogram that integrates the collagen signature and clinicopathological risk factors for the individual postoperative prediction of peritoneal metastasis in GC with serosal invasion.The clinicopathological characteristics of the training and validation cohorts are summarized in Table In the training cohort, the median follow-up duration (IQR) was 37 20.75–44) months. The 3-year overall survival (OS) and disease-free survival (DFS) rates were 60.1% and 49.5%  and DFS   and 3.09  for the 3-year OS and DFS rates, respectively, in the Cox regression analysis.In the training cohort, there was a significantly higher 3-year cumulative peritoneal metastasis rate in patients with the high collagen signature (58.57% vs. 29.69%) than in those with the low collagen signature  Fig. . Kaplan–01) Fig.  than patP < 0.001) , respectively, along with 3-year DFS rates of 22.6% and 60.5% (log-rank P < 0.001)  and 2.66  for the 3-year OS and DFS rates, respectively.The same analyses were performed in the validation cohort. Similar results were found between the high and low collagen signature groups for the 3-year cumulative peritoneal metastasis rate (58.06% vs. 21.92%)  Fig. . The 3-y01) Fig. , respectAs shown in Table The time-dependent receiver operating characteristic (ROC) curve of the nomogram to predict peritoneal metastasis at 3 years in the training cohort is presented in Supplementary Fig. Decision curve analysis revealed that if the threshold probability in the clinical decision was less than 67%, using the competing-risk nomogram to predict peritoneal metastasis would add more net benefit than the treat-all scheme and the treat-none scheme  and positive predictive value (PPV) of the nomogram in the training cohort were 82.3%, 82.4%, 82.3%, 87.7% and 75.6%, respectively. In the validation cohort, the sensitivity was 81.4%, the specificity was 60.8%, the accuracy was 66.9%, the NPV was 88.9%, and the PPV was 46.8%. In the total cohort, the sensitivity was 82.0%, the specificity was 72.4%, the accuracy was 75.8%, the NPV was 88.0%, and the PPV was 62.1% . A previous study has reported that an increased chemical cross-link density of collagen heightened the stromal stiffness and stimulated the invasive properties of tumour cells32. Thus, whether there is any connection between the physical cross-link density and chemical cross-link density and how the physical cross-link density affects the biological behaviours of tumour cells needs to be further investigated.As the scaffold of the extracellular matrix, collagen accounts for most of its functions34. It took only approximately 10 min to complete the multiphoton imaging. There was no treatment on the unstained serial sections before the measurements, and the paraffin did not need to be removed22. Moreover, multiphoton imaging is a label-free and noninvasive tool to obtain the tissue structure and cell morphology of specimens; it is comparable to hematoxylin-eosin (H&E) staining and does not affect the collagen signature36; thus, experienced pathologists could master multiphoton imaging with little training, and it is possible to define regions of interest based on multiphoton imaging. In addition, it has been reported that tissue fixation and paraffin embedding have negligible effects on collagen detection and quantification; thus, a sample that was fixed overnight compared to one fixed over a few days, prior to paraffin embedding, would not influence multiphoton imaging37. Therefore, multiphoton imaging is promising for clinical transplantation.In this study, the collagen signature was constructed based on multiphoton imaging. Currently, with the development of interdisciplinary medicine, multiphoton imaging has been applied in the field of biomedical research39. Gabor wavelet transformation features are also textural features that are used to reflect the spatial relationship of collagen in different scales and orientations after image convolution40. In our previous studies, we extracted four types of the above-mentioned features to evaluate liver fibrosis using multiphoton imaging41. Based on these results, we established the collagen signature from four types of collagen features.Multiphoton imaging can visualize biomolecular arrays in cells, tissues and organisms; thus, the structural or molecular assignment may be linked to different collagen features. In this work, the high-throughput quantitative collagen features obtained suggest that the high-dimensional features of collagen including morphological and textural features from multiphoton imaging could be extracted after image processing. To date, a common consensus about the selection of collagen feature types has not yet been achieved to comprehensively quantify collagen alterations based on multiphoton imaging. The morphological features, such as collagen length and width, are easily understood. Histogram- and grey-level co-occurrence matrix (GLCM)-based features are two main types of textural features of collagen that have been reported by several studies and have potential clinical applications in the diagnosis of diseases42. In this study, the LASSO regression mainly selected Gabor wavelet features as potential predictive variables, which indicates that the combination of the three selected Gabor wavelet features and the mean of cross-link density was most associated with the risk of peritoneal metastasis. We found that there were correlations among the three Gabor wavelet transformation features . These results indicated that the fat content in the serosa did not affect the construction of collagen signature. However, fat should be avoided as much as possible.Adiposity occurs in the serosa is variably distributed and might influence the collagen matrix. Fat content is associated with individual body mass index (BMI). We found that the distribution of the collagen signature between patients with high and low BMI was similar  to 0.720  by removal of other variables  level, carbohydrate antigen 19-9 (CA19-9) level, tumour location, tumour size, Lauren classification, tumour differentiation status, lymph node metastasis (N stage), postoperative chemotherapy and follow-up data. Routine adjuvant chemotherapy was initiated after surgery if the patients’ physical conditions were available according to the National Comprehensive Cancer Network guidelines. The diagnosis of peritoneal metastasis was determined by abdominal ultrasonography, computed tomography (CT) or positron emission tomography (PET)-CT, clinical signs, such as ascites, an intraabdominal mass, and even reoperation, during follow-up.The formalin-fixed paraffin-embedded samples of each patient were used to determine the regions of interest for multiphoton imaging. All samples were sectioned at 5-μm thickness and processed for H&E staining. Two independent pathologists who were blinded to clinical characteristics and prognosis reassessed the invasive region of the gastric serosa using a microscope. When the two pathologists had different opinions, a third pathologist was consulted, and they discussed together to make a decision. Finally, five regions of interest with a field of view of 500 × 500 μm per sample within the invasive region of the gastric serosa were randomly selected.47. The system contained a high-throughput scanning inverted Axiovert 200 microscope  equipped with a mode-locked femtosecond titanium (Ti): sapphire laser , tunable from 700 to 980 nm . An acousto-optic modulator was used to control the attenuation of the laser intensity. A Plan-Apochromat 20× objective (Zeiss) was employed for focusing the excitation beam and for collecting the backward signals. The META detector collected the backward multiphoton signals from the tissue sample. The two-channel mode achieved two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG), which was separated by a dichroic mirror in the detection path. One channel corresponds to a wavelength range of 430 to 708 nm to show the morphologies of the tissue components from the TPEF signals, whereas another channel covers the wavelength range from 387 to 409 nm to present the microstructures of the tissue components from the SHG signals. The excitation wavelength (λex) used in this study was 800 nm. Imaging acquisition was performed on another unstained serial section and compared with the H&E staining for histological assessment.The regions of interest were imaged with a multiphoton imaging system41. The extracted collagen features were summarized in Supplementary Table 48. The binary collagen mask image was then processed using a fibre network extraction algorithm49 to trace each collagen fibre in the image and to identify cross-link points, which are defined as connecting points between two or more fibres. Moreover, we quantified an orientation index to reflect the collagen alignment based on Fourier transform spectra50. For histogram-based features, a histogram-based approach was used. The mean, variation, skewness, kurtosis, energy and entropy were calculated from the histogram of the SHG pixel intensity distribution. We also included 80 GLCM-based texture features51. The contrast, correlation, energy and homogeneity were calculated from the GLCM with five different displacements of pixels at 1, 2, 3, 4 and 5 and four different directions at 0, 45, 90 and 135 degrees. In addition, forty-eight Gabor wavelet transform features were included for analysis52. To calculate the Gabor wavelet transform features, we convolved the SHG image with Gabor filters at four different scales and six different orientations, and the mean and variation in the magnitude of the convolution over the image at each setting were calculated. The GLCM-based features provide a second-order statistical representation of the distribution of grey levels within a specific region of interest, which in turn provide the basis for textural analysis. GLCM is built by calculating the occurrence of a certain grey level pair i next to grey level j at the distance δ along the direction α. After GLCM is obtained, the probability density function, Pδ, α, of finding certain pairs of pixel intensity i and j are calculated. Therefore, GLCM textural analysis considers the variation in pixel grey levels within a certain distance. Histogram-based features summarize the collagen signal intensities within the region of interest, and the inter-pixel correlation is ignored. Gabor wavelet transformation is a kind of textural analysis that reflects spatial relationship of images in different scales and orientations after convolution of images40. In a word, these three types of textural features were used to describe the spatial distribution of the collagen from different perspectives.The extraction of collagen features was performed automatically via MATLAB 2015b 53. The method uses an L1 penalty to shrink the coefficients to zero. The penalty parameter λ, also called the tuning constant, controls the strength of the penalty. If we reduce λ and relax the penalty, then more predictors can enter the model. In this study, five-time cross validations were used to determine the optimal value of λ. Finally, the λ was selected via 1-standard error (SE) criteria.LASSO regression was used to select the potential predictive features from all collagen features, and a multiple-feature-based collagen signature was then established. LASSO regression is an effective method for high-dimensional predictors, especially in problems wherein the number of predictors far exceeds the number of observations54, and the same threshold was applied to the validation cohort. Survival analyses were conducted to assess the impacts of the collagen signature on peritoneal metastasis, DFS and OS. DFS was defined as the time from surgery to recurrence at any site, or all-cause death, whichever came first. OS was defined as the interval between surgery and death from any cause.Patients were classified into high and low collagen signature subgroups according to the threshold selected by using X-tile in the training cohort55; treating deaths, local recurrence and distant metastasis before peritoneal metastasis as competing events, the SHR with the corresponding 95% CI was acquired. Variables with P < 0.05 in the univariate analyses were selected for the multivariate analysis. Finally, a competing-risk nomogram was constructed. A clinicopathological model containing only clinicopathological risk factors was also constructed for comparison.The primary end point of the analysis was the time to peritoneal metastasis. The follow-up duration to peritoneal metastasis was calculated from the date of surgery to the date when peritoneal metastasis was diagnosed or to the last follow-up, and information about the survival status and recurrence type was also documented. Fine-Gray competing-risk regression analysis was used to identify the risk factors for peritoneal metastasis in the training cohort56. The calibration was graphically assessed with a calibration curve56. The validation cohort was analyzed to validate the performance of the nomogram57. C-index and AUROC were used to compare the performance between the nomogram based on the collagen signature and the clinicopathological model. The C-indexes and AUROCs of the two models were compared using Mann–Whitney U test and Delong test, respectively.The discrimination of the nomogram was measured by the C-index and the time-dependent ROC curve58. Decision curve analysis is a novel tool for assessing the potential population impact of adopting a risk prediction instrument into clinical practice, and was initially introduced by Vickers and Elkin in 200659. The context for decision curve analysis is a situation in which individuals’ risks for an undesirable outcome are assessed, and individuals with sufficiently high risk are recommended for some intervention or treatment. Decision curve analysis provides a net benefit, which is calculated byA decision curve analysis was performed to determine the clinical usefulness of the nomogram by calculating the net benefits at different threshold probabilitiesPt is the threshold probability at which the expected benefit of treatment is equal to the expected benefit of avoiding treatment.where The maximum Youden index of the 3-year time-independent ROC curve of the nomogram in the training cohort was selected as the optimal cutoff value. Then, all 343 patients were divided into the high-risk and low-risk groups. The sensitivity, specificity, accuracy, PPV and NPV were calculated to evaluate the prediction performance of the nomogram.t-test or the Mann–Whitney U test. Categorical variables were compared by the χ2 test or Fisher’s exact test. The Kaplan–Meier method and log-rank test were used to estimate DFS and OS, and a Cox proportional hazard regression was conducted to compute the HR. The cumulative incidence function was employed to show the cumulative peritoneal metastasis rate, and differences between the subgroups were compared using Gray’s test. All statistical analyses were performed using R software (version 3.4.2) and SPSS software (version 19.0). LASSO regression was performed using the “glmnet” package. Fine-Gray competing-risk regression analysis and nomogram development were performed by the “cmprsk”, “rms” and “mstate” packages. Assessment of the performance and validation of the nomogram were conducted using “pec” and “riskRegression” packages. ROC curves were plotted using “pROC” package. Decision curve analysis was performed with the function of “stdca.R”. The “survminer” package was used for computing survival analyses. A two-sided P < 0.05 was considered statistically significant.Continuous variables, where appropriate, were compared by independent samples, unpaired, 2-sided Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Software 1Reporting Summary