Infectious diseases are one of the most intimidating threats to human race, responsible for an immense burden of disabilities and deaths. Rapid diagnosis and treatment of infectious diseases offers a better understanding of their pathogenesis. According to the World Health Organization, the ideal approach for detecting foreign pathogens should be rapid, specific, sensitive, instrument-free, and cost-effective. Nucleic acid pathogen detection methods, typically PCR, have numerous limitations, such as highly sophisticated equipment requirements, reagents, and trained personnel relying on well-established laboratories, besides being time-consuming. in vitro properties of CRISPR-Cas effectors, turning activated nucleases into basic amplifiers of a specific nucleic acid binding event. These effectors can be attached to a diversity of reporters and utilized in tandem with isothermal amplification approaches to create sensitive identification in multiple deployable field formats. Although still in their beginning, SHERLOCK and DETECTR technologies are potential methods for rapid detection and identification of infectious diseases, with ultrasensitive tests that do not require complicated processing. This review describes SHERLOCK and DETECTR technologies and assesses their properties, functions, and prospective to become the ultimate diagnostic tools for diagnosing infectious diseases and curbing disease outbreaks.Infectious diseases are one of the most intimidating threats to human race, responsible for an immense burden of disabilities and deaths. Rapid diagnosis and treatment of infectious diseases offers a better understanding of their pathogenesis. According to the World Health Organization, the ideal approach for detecting foreign pathogens should be rapid, specific, sensitive, instrument-free, and cost-effective. Nucleic acid pathogen detection methods, typically PCR, have numerous limitations, such as highly sophisticated equipment requirements, reagents, and trained personnel relying on well-established laboratories, besides being time-consuming. Thus, there is a crucial need to develop novel nucleic acid detection tools that are rapid, specific, sensitive, and cost-effective, particularly ones that can be used for versatile point-of-care diagnostic applications. Two new methods exploit unpredicted  Emerging and reemerging infectious diseases are one of the most intimidating threats to human race, responsible for an immense burden of disabilities and deaths . Pandemi9–trans reporter (DETECTR), a rapid (∼30 min), low-cost, and accurate CRISPR-Cas12-based lateral flow assay for detection of viral infections , making them useful for nucleic acid detection applications . This method depends on the collateral activity of Cas12a protein activated after recognition of target RNA by Cas12a. The authors demonstrated that Cas12a protein from Lachnospiraceae bacterium strain ND2006 (LbCas12a) exhibits nonspecific collateral activity and degrades all adjacent DNA molecules after recognizing target RNA. If the reaction with Cas12a protein and targeting crRNA is complemented by single-stranded DNA reporters (probes) and then mixed with the biological sample, crRNA-dependent recognition of pathogenic nucleic acids by Cas12a turns on collateral activity that destroys DNA probes. DNA probes are designed similarly to conventional TaqMan probes, in which one end of the reporter is bound to a fluorophore and the opposite is linked to a quencher. Degradation of the DNA probes releases fluorophores and results in stable and strong fluorescent signal detected by a fluorimeter. Additionally, DETECTR has been combined with an isothermal preamplification step to enrich target sequences (RPA). RPA enhances analytical sensitivity of the diagnostic test and helps to avoid the need for sophisticated and expensive equipment. Other orthologous proteins from different organisms, such as AsCas12a (Acidaminococcus sp.), FnCas12a (Francisella novicida), and AaCas12b (Alicyclobacillus acidoterrestris)  , also harestris) . In crudLeptotrichia wadei. Cas13 specifically recognizes and cleaves only RNA, rather than DNA like Cas12a. In vitro transcription of the isolate enables recognition of DNA targets. Isothermal amplification by RPA can be used to enrich target molecules and increase sensitivity. The amplified RNA fragments are mixed with Cas13 protein crRNA and fluorescent RNA probes. If the target molecules are present in the sample, Cas13 recognizes them via crRNA and indiscriminately cleaves  fluorescent RNA probes, disrupting the interaction between the fluorophore and the quencher. The presence and intensity of the fluorescent signal thus indicate the amount of the target in the biological sample. The authors demonstrated that SHERLOCK detects Zika virus, dengue virus, various pathogenic bacteria, and single nucleotide polymorphisms (SNPs) in DNA with attomolar sensitivity  include infections that are entirely new in a population or that may have existed before in the population but are now gaining rapidly and continue to spread and/or have a wide geographical range . Several55–61–Coronavirus infections represent an intimidating threat to the global health. In December 2019, a new strain spread across Wuhan City, China. It was designated SARS-CoV-2 by the WHO . In lateThe recent COVID-19 infection was shown to be commonly asymptomatic. Thus, screening of people and timely isolation of infected persons cannot be performed with the use of infrared thermography. The DETECTR system has been used for detection of SARS-CoV-2 and in the protocol reported focuses on detecting the presence of the N and E gene mutations specific to SARS-CoV-2. A positive result is generated if both genes are identified, and the method has been improved to exclude false positives resulting from other coronaviruses .The recoAs mentioned, the ideal diagnostic assay should provide accurate and sensitive identification of the pathogen while being affordable, portable, and able to distinguish different variants of the pathogen. Currently, no such test exists. Developing new tools which meet the requirements of the WHO standard diagnostic test can completely reshape epidemiological surveillance and medical health care systems for the majority of infectious and noninfectious diseases in the world , 68.The most interesting features of DETECTR are the accuracy and speed in providing results in matter of minutes, Broughton at al. provide evidence-based comparison between DETECTR, SHERLOCK, and CDC/WHO on the 2019 novel coronavirus SARS-CoV-2 using a CRISPR-based DETECTR lateral flow assay  Fig. 2)Fig. 2).DETECTR has been used to detect viruses and differentiate human papillomavirus (HPV) genotypes in either virus-infected human cell lines or clinical patient samples .SHERLOCK can differentiate bacterial strains using a universal 16S rRNA gene V3 RPA primer set .Also, SHERLOCK has been used for the detection and genotyping of bacterial and viral infectious disease agents, and finding antibiotic-resistance genes .Another application of SHERLOCK is that it may be used to perform SNP screening, when a vital SNP is known, by cautiously designing a crRNA to target the region having the SNP of interest so it that favors selective binding of one mutation over another .in vitro RNA target detection, catalytically inactive LwaCas13 retains its RNA-binding activity such that it can be coupled to a fluorescent probe to enable live cell RNA tracking (In simulated cell-free DNA (cfDNA) samples, SHERLOCKv2 can detect two cancer mutations under low allelic fraction with single-base mismatch sensitivity . In additracking . This prThe single-nucleotide specificity of SHERLOCK has been applied to provide genotyping profiles of cancer patients by revealing cancer-associated mutations from circulating cell-free DNA, even in serum or urine samples, to low attomolar concentrations reaching to 0.1% allelic fraction. In similar cases, the specificity of Cas13 can be boosted by the introduction of a “synthetic mismatch” into the crRNA , 46. TheSHERLOCK is ultrasensitive and specific. It is capable of single-molecule detection in 1-μl sample volumes (2 aM) of both DNA and RNA targets. In addition, by scaling up the preamplification volume, it is possible to achieve single-molecule detection in large sample input volumes  . SHERLOC71–Regardless of its advantages over existing detection technologies, SHERLOCK has several caveats that can make it unacceptable for certain cases. SHERLOCK currently involves the preparation and testing of reaction components, some of which require expertise in protein purification and RNA biology. Moreover, predesigned assays, including reaction mixtures and DNA/RNA oligonucleotides, are currently not commercially available for SHERLOCK. Existing standard detection technologies may also be more appropriate for applications that do not demand the speed or portability of SHERLOCK, such as oncology assays .Another potential limitation of SHERLOCK is the multistep nucleic acid amplification process, which may affect precise target quantification. Although recently Kellner et al. demonstrated the quantitative detection of nucleic acids with SHERLOCK, absolute digital quantification, such as in digital droplet PCR, is currently not possible, and small differences in target quantity (<2× changes) may not be detected. SHERLOCK may therefore be less useful for precise gene expression profiling .On the other hand, DETECTR possess unique features, of which the most important one is the speed . Other aGenerally, CRISPR-Cas screening methods can only be used to detect known DNA sequences, which could limit their application in some cases .In conclusion, SHERLOCK and DETECTR have begun a new era in the molecular diagnostics field by providing portable, highly sensitive diagnostic tools suitable for diagnosing emerging infectious diseases, as well as noninfectious diseases, in a matter of an hour. Still, it remains to be resolved that the CRISPR-Cas technology faces the same challenge of low sensitivity that current point-of-care analytical devices face.Although still in their infancy, SHERLOCK and DETECTR technologies are potential game changers for our ability to identify infectious disease pathogens with ultrasensitive tests that do not require a lot of complicated processing, thus offering an opportunity for population screening and better control of infectious outbreaks, extensive distribution of diagnostic tools, and field-deployable diagnostics tools with affordable cost, which is desirable for resource-limited countries. With this evidence, we believe CRISPR-Cas systems are driving a biotechnological revolution.


Flavivirus comprises numerous, small, single positive-stranded RNA viruses, many of which are important human pathogens. To store all the information required for their successful propagation, flaviviruses use discrete structural genomic RNA elements to code for functional information by the establishment of dynamic networks of long-range RNA–RNA interactions that promote specific folding. These structural elements behave as true cis-acting, non-coding RNAs (ncRNAs) and have essential regulatory roles in the viral cycle. These include the control of the formation of subgenomic RNAs, known as sfRNAs, via the prevention of the complete degradation of the RNA genome. These sfRNAs are important in ensuring viral fitness. This work summarizes our current knowledge of the functions performed by the genome conformations and the role of RNA–RNA interactions in these functions. It also reviews the role of RNA structure in the production of sfRNAs across the genus Flavivirus, and their existence in related viruses.The genus  Flavivirus comprises many small, enveloped viruses commonly known as flaviviruses. Of global health importance, they are responsible for many emerging and re-emerging outbreaks of human disease, with dengue virus (DENV), zika virus (ZIKV), Japanese encephalitis virus (JEV), yellow fever virus (YFV) and West Nile virus (WNV) among the most important. Flaviviruses can be largely classified according to their arthropod vector as either mosquito-borne flaviviruses (MBFV), tick-borne flaviviruses (TBFV), no-known vector flaviviruses (NKFV), insect-specific flaviviruses (ISFV), or as the unclassified Tamana bat viruses (TABV) (The genus s (TABV)  1,2].,2.FlavivThe flavivirus genome consists of a single positive-stranded RNA molecule with just one open reading frame (ORF) flanked by untranslated 5′ and 3′ regions (UTRs). The ORF encodes a polyprotein that is processed to produce three structural and seven non-structural viral proteins . The RNAFlavivirus, and their putative existence in related viruses, is also provided.This review examines our current knowledge of the structure/function of the flavivirus genome, with special attention given to the networks of interactions that occur among different structural RNA units. An overview of the involvement of genomic RNA structural elements in the production of subgenomic flavivirus RNAs (sfRNAs) across the genus All flaviviruses bear UTRs at both ends of their genome; these are essential for viral viability. Their functioning relies on their structure, which varies widely among members of different ecological groups and even different clades of the same group. UTR folding involves a number of discrete, functionally active structural RNA units with defined roles. It is noteworthy that, regardless of their different folding, overall UTR functioning is preserved across the flaviviruses, revealing that this functionality can be achieved with different RNA conformations.The 5′UTR is ~100 nt long . AlthougThe 3′UTR region is between 400 and 800 nt long. A remarkable feature of this region is the existence of structural duplications. The existence of alternative folding intermediates dependent on duplicated elements increases the functional diversity. Indeed, such functional diversification is known to exist, providing, for example, adaptation to different hosts.The 3′UTR is organized into three major domains in the MBFV  15]::15]:(i) Domain I, located downstream from the translation stop codon, contains a variable structural element followed by a number of conserved stem-loops (SLs). Indeed, significant variations in the number of SLs have been reported. For example, some members of the MBFV group, such as YFV, possess tandem duplications of two SLs (known as RYF) a 16]. W. W16]. WThe existence of SL duplications in domain I of different flaviviruses may be an adaptive mechanism that assists in the movement between natural hosts without interfering with any other activities of the 3′UTR . For exaAn important structural feature of flavivirus RNA genomes is that they bear a variable number of conserved pseudoknot structures (PK) in domain I . They in(ii) Domain II shows moderate variations in its structural organization, not only between different ecological groups but also among different clades of the same group . For exaA significant feature of flavivirus genomes with double DB elements is the formation of at least one PK structure stabilized by four-way junctions . The PK The DB elements also incorporate conserved and reverse-conserved sequences, named CS2 and RCS2, respectively  29,35].,35.29,35(iii) Domain III is the most structurally conserved region within the 3′UTR. It contains a large terminal SL-3′SL, preceded by a small hairpin structure, sHP . Both 3′The sHP element is a small hairpin carrying different concatenate sequence motifs  and CS) and is involved in genomic cyclization. It can adopt different conformations in the cyclized and linear forms . Systema3′SL bears a conserved pentanucleotide sequence motif (5′-CACAG-3′) in the apical loop ,39,40 thTwo conserved base pairs can be also found at the base of the 3′SL element in the JEV and DENV clades, which are flanked by a metastable structure identified as two symmetrical bulges  58,59].,59.58,59It would thus appear that a dynamic network of RNA–RNA interactions regulates the critical processes of the viral life cycle and the switches between them. The diversification of RNA genome functionality, including successful replication in two ecologically different hosts, is determined by the acquisition of new structural RNA elements.From a structural point of view, one of the most interesting and complex flavivirus phenomena is the acquisition of a circular closed-loop topology by the RNA genome (the cyclization process) . The acqIn flaviviruses, cyclization depends on the establishment of non-covalent interactions between complementary sequences at the 5′ and the 3′ ends of the genome . In MBFV(i) The cyclization sequence 5′CS ,39,74 lo(ii) The 5′ downstream sequence of the AUG region, 5′DAR, and its partner 3′DAR, which overlaps with the sHP element (and therefore the 3′CS motif) in the linear conformation of viral RNA  69,78,7,778,79. (iii) The 5′ upstream sequence of the AUG region, 5′UAR, and its complementary sequence 3′UAR within the basal portion of the stem in the 3′SL element  11,12].,12.11,12The compilation of functional data ,81,82,83The cyclization process can be facilitated by additional cis-structural elements. For example, different MBFVs form a three-stemmed PK downstream of the 5′CS (DCS-PK)  85], wh, wh85], Genome cyclization may fit into the intracellular viral infection process as follows: in its linear form, the actively translated RNA genome presents the UFS duplex, which is crucial for NS5 recruitment to the SLA element. This induces a translationally repressed state . Genome In addition to the functional roles of the discrete 3′UTR elements in the formation of the secondary and tertiary structures described above, the 3′UTR participates in the production of small, flavivirus-specific, non-coding RNAs known as sfRNAs. sfRNAs result from the incomplete degradation of the viral RNA genome; they accumulate in the cytoplasm of infected cells .Uncapped viral genomic RNAs are digested by the 5′-3′ exoribonuclease XRN1 in mammalian cells, or by its homolog Pacman in insect cells ,92. ThisThe members of the MBFV group show differences in their production of sfRNAs. Members of the JEV clade bear the SL-II element, which participates in the formation of xrRNA1 ; when XRTombusviridae [Alternative pathways leading to the accumulation of sfRNAs have been proposed. One suggests the existence of promoter sequences close to the XRN1 stalling site, as seen in the 3′UTR of JEV clade members. In this case, subgenomic RNA production would derive from RNA transcription. This assumption is further supported by the observation that, in these viruses, sfRNAs accumulate even more strongly in XRN1-knock down cells . A similsviridae , suggestThe overall folding of xrRNAs is well-conserved across flaviviruses, although significant variability in the primary and secondary structural elements can be found. Different sequence motifs and secondary structures can therefore induce the same three-dimensional conformation.Several experimental strategies have been used to elucidate the architecture of the flavivirus 3′UTR xrRNA structures, the secondary structure elements, and the tertiary interactions involved in their formation. Although all xrRNAs share a common three-dimensional structure ,106, difFlavivirus share three SLs that fold into a three-way junction [In vitro assays have shown that the xrRNA ring-like structure also blocks 5′-3′ viral RNA degradation performed by unrelated exoribonucleases [All xrRNA structures found across the genus junction ,100,106.junction ,100,106.ucleases . Howeverucleases . Thus, xucleases .2+ ions and water molecules to stabilize the final structure [A clear example of how different primary and secondary structures can lead to the same 3D xrRNA has been reported in the divergent flavivirus Tamana bat virus (TABV) ,109,110.The requirement of XRN1 for the biogenesis of sfRNAs involves the shortage of this exoribonuclease for physiological cellular mRNA turnover, promoting cytopathic effects ,111,112.Flaviviridae. It also occurs via the direct sequestration by sfRNAs of cellular factors—such as TRIM-25, G3BP1/2 and CAPRIN1—that participate in the interferon response pathway [sfRNAs are also involved in strategies that interfere with antiviral responses. One of these strategies involves the recognition and binding of isoforms of the protein Dicer along with protein components of the interference machinery. Thus, proteins essential to the cell for the physiological production of miRNAs are sequestered. This also prevents the generation of siRNAs from double-stranded regions of the flavivirus genome . Flavivi pathway . ProteinThe importance of sfRNAs as driving elements in the evolution of flaviviruses has been reviewed by Slonchak et al. (2018). The accumulation of mutations in specific domains within the 3′UTR modifies the genomic:subgenomic RNA ratio; larger numbers of sfRNA may help to promote outbreaks of disease . It shouFlaviviridae family. Computational studies have identified putative xrRNAs in representative members of the genera Pestivirus, Pegivirus and Hepacivirus, expanding the presence of these structural elements to the entire Flaviviridae family [Since XRN1 can completely degrade the RNA of any viral RNA genome, the existence of xrRNAs and sfRNA-like subgenomic RNAs in viruses other than flaviviruses was thought to be unlikely . Howevere family  and suppe family .The simple presence of xrRNAs does not necessarily dictate the production of subgenomic RNAs. For example, viruses with subclass 1b xrRNAs do not produce them (and interestingly they do not infect different hosts), unlike the viruses containing subclass 1a xrRNAs that alternate between two host. In many cases, these 1b xrRNAs do not locate to the 3′UTR of the viral genome ,115,119.Flaviviridae. Following their search for subgenomic RNAs, Charley et al. [Bunyavirales) contain structural elements in their 3′UTR that can efficiently stall XRN1 leading to the production of sfRNA-like subgenomic RNAs. Interestingly, these elements differ in their primary and secondary structure to those described for members of the family Flaviviridae. For example, XRN1 stalling in phlebovirus seems to be dependent on G-rich stretches, similar to the poly(G) sequences reported to stall XRN1 in yeast. Nevertheless, additional conformational determinants must be necessary to block XRN1 activity since not all G-rich sequence motifs promote this effect [The phenomenon of XRN1-stalling mediated by xrRNAs extends beyond the family y et al.  reporteds effect . In the s effect . SurprisTombusviridae and Luteoviridae [Tombusviridae [Benyviridae) or RCNMV , the minimum core for generating the xrRNA is smaller than that described for flaviviruses [Finally, up to 40 putative xrRNAs have been reported in different members of the families oviridae ,119,120,sviridae . In contiviruses ,120.Taken together, these observations again highlight that ring-like folding relies on a plethora of different secondary structural elements that together govern the stalling of XRN1.The conservation of the overall structure of flaviviral RNA genomes ensures their correct functioning and passage through the viral cycle. However, this structure can depend on the equilibrium between different foldings governed by a dynamic network of regulated RNA–RNA interactions (the interactome). Flaviviruses have evolved through the acquisition and preservation of discrete structural RNA units distributed throughout the genome; the RNA–RNA interactions among them determine genome functionality. The switch between essential functions  is finely regulated and governed by changes in the above network of interactions. This leads to the acquisition of structures competent for the recruitment of the viral and/or cellular factors needed for a specific function. Different flaviviruses may show different structural solutions for the same function. Knowledge of the essential roles played by viral RNA genome structures may provide new targets for combating RNA virus infections.


Risk factors vary in terms of the pattern of lung cancer metastasis and specific metastatic organs. In this study, we retrospectively analyzed the clinical risk factors of tumor metastasis in lung cancer patients and used second‐generation gene sequencing to characterize relevant gene mutations. The risk factors of different metastatic sites of real‐world lung cancer were explored to find the differentially expressed genes and risk factors in different metastatic organs, which laid a foundation for further study on the metastasis patterns and mechanisms of lung cancer. The clinical risk factors of tumor metastasis in 137 lung cancer patients who attended our department from May 2017 to March 2019 were retrospectively analyzed and grouped based on bone metastasis, brain metastasis, other distant metastasis, and no metastasis. Single‐ or multi‐factor logistic regression analysis was performed to analyze the effect of neutrophil/lymphocyte ratio/platelet/lymphocyte ratio/lymphocyte to monocyte ratio on platelets (PLTs) and bone metastasis by combining PLT values, age, pathology type, gender, and smoking history. Based on the presence or absence of bone metastasis, distal metastasis, and PLT values of lung cancer, 39 tissue specimens of primary lung cancer were taken for 773 gene grouping and gene mutation characterization. The tumor mutation load, gene copy number instability, microsatellite instability, and tumor heterogeneity among different groups were analyzed. Age and PLT level were independent risk factors for bone metastasis and distal metastasis, but not for brain metastasis. The RB1 gene was mutated during bone metastasis, and tumor heterogeneity was less in the elevated PLT group. PLT values were an independent risk factor for distant metastases from lung cancer other than the brain. Age has a significant effect on bone metastasis formation. RB1 gene mutation was significantly associated with bone metastasis. Age and platelet level were independent risk factors for bone metastasis and distal bone metastasis, but age and platelet level were not risk factors for brain metastasis. The RB1 gene was mutated during bone metastasis, and tumor heterogeneity was less in the elevated platelet group. Platelet values are an independent risk factor for distant metastases from lung cancer other than the brain. Age has a significant effect on bone metastasis formation. Mutations in the RB1 gene are significantly associated with bone metastasis. Identifying metastatic risk factors for specific organs of lung cancer using multifactorial analysis has received attention, but there exist many inconsistencies in the previous reports. In this study, we evaluated the clinical risk factors for lung cancer metastasis to different organs using logistic analysis. We also explored the pattern of lung cancer metastasis and the possible molecular mechanisms using lung cancer specimens with 39 routine 773 gene sequencing, lung cancer specimens were grouped based on different metastatic organs and their risk factors, for gene mutation characterization.22.19–268 × 109/L in the check system of our hospital. To deeply study the relationship between the increase of PLT count and tumor metastasis, the patients were allocated into three groups:PLT > 210 × 109/L and PLT > 350 × 109/L as cut‐off values. NGS with fixed panel was performed in all populations. The samples included primary lesion tissue or plasma. A total of 79 patients were tested for 773 gene sequencing and 58 patients for 1406 or 543 gene sequencing. Although several panels covered part of the same gene, the specific sites covered were not completely the same. For the consistency in sample analysis, only 39 tissue samples from primary lung cancer lesions tested for 773 gene sequencing were included in this study for subsequent mutation characterization. The study protocol and all data were approved by the Ethics Committee of Army Medical Center. All patients have signed the informed consent.This study included 137 patients with lung cancer who were clinically treated at our hospital and pathologically confirmed from May 2017 to March 2019. Patients were registered for age, pathological staging, the clinical stage, gender, smoking history, blood count, and site of metastatic lesions. The normal range of PLT count was 94 × 102.2Clinical data were extracted by Army Medicine Centre and genetic testing data were sequenced and analyzed by Genecast Biotechnology Co., Ltd. After the quality test of sequence measurements, the genomic positions were first determined using the sequence comparison software bwa, with the human genome reference sequence hg19 as a template; Next, single nucleotide variation (SNVs), Indels, and copy number variation (CNVs) were detected using the widely used detection software and algorithms . The detection of SNVs, Indels, CNVs A mutation type and frequency is inferred primarily from the pileup of sequenced reads, based on the support numbers and the quality of sequencing of different bases at uniform genomic positions. All mutation types are functionally annotated using the software annovar. The original mutations were then screened for somatic SNVs according to the following screening criteria and then tumor mutation burden (TMB) and tumor heterogeneity (MATH) were calculated: (i) located in intergenic or intronic regions; (ii) synonymous SNVs; (iii) allele frequencies ≥0.002 in the database exome aggregates (ExAC) and genomes; (iv) allele frequencies of 0.05 for tumor samples and 0.01 for plasma samples was 0.01; (v) bias mutations in reads; (vi) support reads <5; (vii) depth <30.Using the cnvkit software, the copy number values and gene values were calculated for each case as paired samples, and unstable CNI values were calculated for the whole sample based on the CNV values of the samples. If the copynumber of a gene was >4, the gene was considered to have experienced a CNV Gain. If the copynumber of a gene was <1, the gene was considered to have experienced a CNV Loss.2.3U test was used to analyze differences in the distribution of specific blood values between the two groups. The chi‐square test or Fisher's exact test was used to analyze correlations between important clinical factors. Univariate and multivariate logistic regression analysis was performed using SPSS to identify predictors for distant metastasis. The mutated genes and tumor mutation load (TMB), MATH, microsatellite instability (MSI), copy number instability (CNI), and copy number variation load (burden of copy number) in clinical samples were analyzed using bioinformatics methods, and the Wilcoxon test was used to rank and test the mutated genes and TMB in different groups. The Wilcoxon test was used to rank and test the variance of mutated genes and TMB/MATH/MSI/CNI/Burden of copy number in different populations. Statistical analysis was performed using R software. The enrichment analyses were carried out using KOBAS 3.0 software.Data analysis was performed using both R3.5.1 and SPSS 23.0. Based on the clinical characteristics of the patients, the Mann–Whitney 33.1p = 0.0084, p = 0.0015)  and 210 × 109/L < PLT ≤ 350 × 109/L (57.9%) than that in patients with PLT ≤ 210 × 109/L (27.3%), showing a statistically significant difference (p = 0.007)  than those with PLT values ≤210 × 109/L (27.3%), with a statistically significant difference (p = 0.002)   and PLTs  were independent risk factors for bone metastasis.  Figure . The per) Figure . Patient) Figure . Patient) Figure . Moreove3.2p = 0.001). Patients aged ≥55 years had more distal metastases (78.2%) than those aged <55 years (61.1%), with a statistically significant difference (p = 0.045). However, multivariable logistic regression analysis showed that only PLT value was an independent risk factor for distal metastasis   than those with PLT values ≤ 210 (51.5%) , respectively, of brain metastasis. The results did not reveal a statistically significant effect on brain metastasis.3.3p = 0.029). However, no statistical difference in the values of NLR/PLR between low and high PLT groups was found. Moreover, no statistical difference was found between NLR/PLR/LMR and the risk of bone or distant metastasis. Logistic regression analyses did not find any statistical difference between NLR/PLR/LMR values and low and high PLT groups as well as bone or distant metastasis. Taken together, these results suggested LMR could not be responsible for bone or distant metastasis . We found the effect of LMR on PLT values had a statistically significant difference  and PLT‐L  groups were analyzed by bioinformatics methods and statistically analyzed. The TOP10 mutated genes of the two groups were partially different, and no statistical differences were found. The gene base conversion variants and reversal variants in samples of each group were statistically analyzed using bioinformatics methods. The PLT‐H group showed Tv>Ti, where the variant type was most frequently C>A, followed by C>T; the PLT‐L group showed Ti>Tv, where the variant type was most frequently C>T, followed by T>G. We found that there was a statistical difference in the number of mutations in ARIDIB and NF2 mutations between the PLT‐H group and the PLT‐L group. . Next, we focused on the mutations in several important cancer driver genes. We found that the mutational rate of EGFR/KRAS/ALK/TP53 genes showed no statistical difference between the PLT‐L and PLT‐H groups . No statistical difference was seen in the analysis of SNV variant gene KEGG pathway enrichment between the PLT‐H group and the PLT‐L group. No statistical difference was seen in CNV between the PLT‐L group and the PLT‐H group , while no statistical difference was seen in terms of MATH between the bone metastasis and distal metastasis groups. No statistical difference was seen in the burden of copy number between the different subgroups. This result suggests that single nucleotide variants were not the main causes for distal metastasis. The structural features of genes determine whether genes are prone to recombination. Hence the above data suggested that genomic structural variants were not the main influencing factors of lung cancer metastasis .The occurrence of SNV in the tumor eventually leads to the change of phenotype, which requires the comutation of multiple genes in the same or multiple pathways. For example, in ovarian cancer, only the simultaneous truncation mutation and missense mutation of ARID1A and PI3KCA may cause the growth of malignant metastatic tumor cells. In this study, only 37 samples were performed for NGS. It is difficult to find the difference of SNV as a binary variable between bone metastasis and no bone metastasis groups at the single‐gene level. Our research is only a preliminary exploration.5In conclusion, PLTs represent a huge biorepository of tumor‐derived and bioactive molecules. PLTs help CTC to attach to endothelial cells and provide signals to establish the pre‐metastatic microenvironment. It is important to investigate the role of PLTs in tumor metastatic spread and tumor angiogenesis. In this study, we concluded that age and PLTs were independent risk factors for bone metastasis of lung cancer, respectively, and there were more RB1 gene mutations in bone metastasis and less tumor heterogeneity in the PLT‐H group. However, because the clinical sample size in this study was small, these conclusions need to be validated by more follow‐up studies.The authors declare that they have no competing interests.Hua Zou and Jinlu Shan designed this study. Bin Wang and He Xiao collected the samples. Jiao Zhang and Dandan Liang performed the whole‐exome sequencing of all included samples and statistical analyses. Bin Wang and Shu Chen drafted the manuscript. Hua Zou provided critical comments, suggestions, and revised the manuscript. All authors read and approved the final version of the manuscript.The studies involving human participants were reviewed and approved by the Ethics Committee of Army Medical Center of PLA. Ethics Committee of Army Medical Center of PLA Approval of Medical Research Involving People Ethical Ratification No: 2020 (58). The patients/participants provided their written informed consent to participate in this study.


Liquid-liquid phase separation of multivalent proteins and RNAs drives the formation of biomolecular condensates that facilitate membrane-free compartmentalization of subcellular processes. With recent advances, it is becoming increasingly clear that biomolecular condensates are network fluids with time-dependent material properties. Here, employing microrheology with optical tweezers, we reveal molecular determinants that govern the viscoelastic behavior of condensates formed by multivalent Arg/Gly-rich sticker-spacer polypeptides and RNA. These condensates behave as Maxwell fluids with an elastically-dominant rheological response at shorter timescales and a liquid-like behavior at longer timescales. The viscous and elastic regimes of these condensates can be tuned by the polypeptide and RNA sequences as well as their mixture compositions. Our results establish a quantitative link between the sequence- and structure-encoded biomolecular interactions at the microscopic scale and the rheological properties of the resulting condensates at the mesoscale, enabling a route to systematically probe and rationally engineer biomolecular condensates with programmable mechanics. Here the authors show that disordered polypeptide-RNA condensates exhibit rheological properties similar to that of a viscoelastic Maxwell fluid, and use simple polypeptide design rules to create microcondensates with tunable viscoelasticity. Past studies have reported a variety of biomolecular condensates with a broad range of physiological functions including stress response8, mRNA processing9, transcriptional activity control12, and genome organization13. The dynamic liquid-like condensates formed by RNA-binding proteins, such as FUS, TDP43, and hnRNPA1 can further undergo a liquid-to-solid transition over time (known as maturation or aging of condensates), which can lead to pathological aggregates16. It is now generally accepted that the material states and dynamical properties of the protein condensates, which include viscosity, surface tension, network elasticity, and transport properties of constituent macromolecules, are key determinants of their biological functions and pathological effects inside living cells18. Typically, dynamic liquid-like micro-environments are deemed ideal for active regulation of biomolecular reactions in a signaling event, whereas irreversible condensate maturation has been linked to neurodegenerative disorders as well as tumorigenesis20. However, identifying the physiochemical factors that determine the material properties of biomolecular condensates remains a key challenge due to the complex dynamical properties of condensate fluid network across different length and timescales.Biomolecular condensates represent a class of dynamic membrane-less bodies that are central in compartmentalizing subcellular biochemical processes in viruses23. Multivalency in a protein chain is typically encoded either by intrinsically disordered regions (IDRs) with amino acid repeat sequences (such as the Arg3 and PRM modules), or a combination thereof22. Multivalent homotypic protein-protein and heterotypic protein-RNA interactions driving biomolecular condensation have been described by the stickers-and-spacers polymer framework26. For IDRs, the residues that can enable inter-chain attractive interactions include arginine (R) in R/G-rich IDRs and tyrosine (Y) in prion-like IDRs27 and are usually referred to as stickers. Additionally, the linker residues connecting these stickers are considered as spacers. The patterning of stickers and spacers can alter the physical properties of condensates and their phase behavior28. In theory29, it is conceivable that network fluidity and stiffness can be encoded by the sequence composition and sticker identity in a polypeptide chain. Hence, understanding how the sticker-spacer architecture of disordered protein chains regulates the physical properties of biomolecular condensates will be insightful for understanding biological mechanisms and designing synthetic condensates.A central force that drives the formation of biomolecular condensates is multivalent interactions between protein and RNA chains, leading to a liquid-liquid phase separation and/or a percolation transition33. The viscoelasticity is presumably a result of transient network-like structures that form via physical crosslinking among protein and/or RNA chains with finite bond lifetime34. This has led to a growing interest in utilizing suitable experimental methods to probe condensate material properties across different timescales. In this study, we adopt passive microrheology with optical tweezers (pMOT) to quantify the viscoelastic properties of a series of artificial condensates formed by disordered sticker-spacer polypeptides and RNA. We find that at shorter timescales, peptide-RNA condensates have an elastically dominant rheological response, while at longer timescales, the same condensates behave as predominantly viscous liquids. The network relaxation time, viz., the timescale at which the condensate transitions from an elastically dominant behavior to a viscous behavior, is determined by the chemical identities of the sticker and spacer residues in the polypeptide chain. Using complementary biophysical assays and molecular dynamics simulations, we show that the variable viscoelastic behavior of condensates across different polypeptide variants is strongly correlated with differences in the strength of inter-chain attractions. Accordingly, we stipulate generalizable sequence heuristics that govern the viscoelasticity of peptide-RNA condensates and connect the same to their thermodynamic phase behavior. Utilizing this acquired knowledge of sequence-phase behavior-material property relations, we test simple strategies to fine-tune the condensate viscoelasticity over multiple orders of magnitude. Similar to the polypeptides, we further show that RNA sequence and shape influence the mechanics of peptide-RNA condensates. Our findings shed light on the origin of viscoelasticity in biomolecular condensates and allude to its interconnection with fundamentally relevant physical properties such as inter-molecular attractive interactions and temperature-dependent phase behavior.Several recent studies have indicated that biomolecular condensates are network fluids with variable viscoelastic properties22. The linear viscoelastic (LVE) behavior of homotypic protein condensates has recently been studied by active oscillatory microrheology using a dual-trap optical tweezer31. However, passive non-oscillatory measurements using a single optical trap offers an attractive orthogonal route to probe the LVE properties of complex fluids due to a wide range of experimentally accessible frequencies36. Furthermore, passive microrheology with optical tweezers (pMOT) only requires a single optical trap and does not need independent calibration measurements40, making it convenient and well-suited for relatively high throughput studies. Here, we employ in-droplet pMOT to map the sequence-encoded and frequency-dependent viscoelastic properties of polypeptide-RNA condensates in vitro over three decades of frequencies from a single measurement (Supplementary Note 395 and a homopolymeric RNA [rU]40 (rU40)  at both low and high frequencies (1 to 100 Hz), indicating a predominant liquid-like behavior  microrheology41 and found good agreement  Fig. . We founior Fig. . The zerncy Fig. , suggestent Fig. . Therefo50. The design of these peptide sequences was inspired by naturally occurring Arginine/Glycine-rich (R/G-rich) IDRs in eukaryotic ribonucleoproteins (RNPs)52, which function as promiscuous RNA-binding motifs. Previous studies by our group and others have reported that Arginine in R/G-rich IDRs can be classified as stickers that potentiate RNP phase separation with RNA through a combination of electrostatic, cation-π and π-π interactions53. Due to their multivalent architecture, we hypothesized that the R/G-rich IDRs have the potential to serve as programmable stickers-and-spacers polypeptides with tunable condensation behavior in presence of RNA  and viscous behavior at low frequencies . This frequency-dependent mechanical behavior of R/G-repeat condensates is reminiscent of a typical Maxwell fluid37  and vice-versa .To probe how polypeptide sequence features govern the condensate viscoelastic properties, we utilized short modular multivalent polypeptides that undergo phase separation with RNAsRNA Fig. . To testd37 Fig.  with a sork Fig. , which srsa Fig. . This isuid Fig.  within tmentclass2pt{minim49. Our bioinformatics analysis of RG/RGG motifs from >400 human RNA-binding proteins54 reveal that R/G-rich IDRs are primarily interspersed with four uncharged amino acids: Tyrosine (Y), Phenylalanine (F), Serine (S), and/or Proline (P) . Setting X = Phe, we observed a 2-fold increase in the terminal relaxation time from 60 ± 10 ms to 120 ± 20 ms, and ~2-fold increase in the zero-shear viscosity of peptide-rU40 condensates  revealed two clear diffusion modes for [RGYGG]5-rU40 condensates, representing distinguishable elastic and viscous regimes , indicating mostly viscous behavior. This is consistent with results obtained from the pMOT assay  Fig. . The occtes Fig. . Intrigutes Fig.  and the tes Fig. . The higtes Fig. . We noteior Fig. .5-rU40 condensates. The zero-shear viscosity of [RGPGG]5-rU40 condensates was ~0.19 ± 0.09 Pa.s, which is ~25-fold lower than the [RGRGG]5-rU40 condensates  was significantly higher than that of [RGRGG]5-rU40 (τ = 23 s), whereas [RGYGG]5-rU40 condensates displayed lowest recovery rate . We observed that the apparent mesh size of [RGPGG]5-rU40 is ~6 nm, which is 50% larger than that of [RGYGG]5-rU40 (~4 nm) . We found that the rate of fluorescence recovery of the client RNA within [RGPGG]26. To explore such an effect, we designed a generic polypeptide [RxRxx]5 where x represents either glycine, glutamine, alanine, proline, or leucine . We observed that G-to-P substitutions substantially reduced the condensate viscosity . This indicates that replacing arginine  residues with proline directly impacts the peptide-RNA interactions and results in larger effects on the condensate rheological properties. For RQRQQ repeats, we also found a reduced viscosity and relatively weaker elastic component than [RGRGG]5-rU40 condensates  resulted in the formation of arrested networks instead of liquid-like condensates  is usually suppressed at higher salt concentrations44  of selected peptide-RNA mixtures with a broad range of dynamical properties at various salt concentrations and constructed a temperature-salt state diagram. We observed that [RGPGG]5-rU40 condensates dissolve at a temperature of Tph = 55 ± 2 °C, while [RGRGG]5-rU40 condensates have a Tph > 90 °C at the same experimental buffer containing 25 mM NaCl . Importantly, Tph progressively increased from [RGRGG]5 to [RGFGG]5 to [RGYGG]5 at a fixed salt concentration, thereby indicating increased strength of intermolecular interactions by P-to-R-to-F/Y substitutions . Our results show a clear and robust trend of Tyr (GYG) being the sticker residue outflanking the charged Arg (GRG) and uncharged Pro . Upon close inspection of the free energy curves  units by tuning the short-range energy coefficient of a standard Lennard-Jones potential to match the difference in the free energy minima from the all-atom simulations. Electrostatic forces are accounted for through the standard Debye-Huckel term. Inspecting the equilibrium configurations and plotting the density profiles across the simulation box, we found that [RGYGG]5-rU40 showed the strongest phase separation behavior with the highest molecular density in the condensed phase  formed homogeneous condensates at all mixing ratios with LVE properties that can be controlled by the molar ratio of [RGYGG]5 and [RGPGG]5  and the dissimilarity in sticker identity allows for composition-dependent regulation of the condensate viscoelasticity ] display substantially slower fusion speed as compared to homopolymeric pyrimidine-containing RNAs [poly(U)]44. This was attributed to the presence of stronger short-range interactions (cation-π and π-π) between purine bases and R-rich IDRs70. Here we considered the possibility of altering the condensate dynamical properties by sequence patterning and secondary structure of RNA chains. To test this idea, we first used a single-stranded nucleic acid  with a stem-loop structure, and compared the resultant condensate network dynamics with condensates formed by an unstructured nucleic acid dT40  and frontotemporal dementia (FTD) patients72. At the same time, G-quadruplex structure forming NAs have previously been reported to interact with R/G-rich IDRs in ribonucleoproteins75. We observed that (G4C2)5 NA formed spherical condensates in the presence of [RGPGG]5 5 condensates contain an elastic network which flows very slowly (τM > 5 sec). This behavior is opposite to [RGPGG]5-dT40 condensates, which showed a linear MSD curve . This is achieved through systematically exploring the frequency-dependent LVE properties of generic IDP-RNA condensates formed by RNP-inspired sticker-spacer polypeptides. Our results reveal that R/G-repeat polypeptide-RNA condensates resemble Maxwell fluids, which behave as an elastic solid at short timescales and a viscous fluid at long timescales. Both viscosity and the time-dependent network dynamics can be precisely controlled via polypeptide chain sequence, RNA sequence composition and secondary structure. These findings add significantly to the growing interest in characterizing and engineering rheological properties of biomolecular condensates by presenting a set of sequence analytics.Quantitative understanding of the molecular determinants of biomolecular condensates’ viscoelastic properties is important for two reasons: (i) the knowledge of the condensate physical behavior at different timescales  can provide invaluable insights into their functional regulation and their pathological liquid-to-solid transformation, and (ii) the establishment of a sequence-phase behavior-material property paradigm for disordered biopolymers akin to sequence-folding paradigm for globular proteins can enable building predictive models of biomolecular condensates and rational designing of synthetic membraneless organelles with programmable phase behavior and material properties. A growing number of recent studies indicate that biological condensates are viscoelastic fluids, and therefore exhibit distinct mechanical behavior at different timescales based on their network reconfiguration time RGG condensates displayed the weakest viscoelastic behavior  and exogenous effectors  in manipulating their physical properties. Finally, engineered condensates formed by sticker-spacer polypeptides with programmable viscoelasticity can inspire new routes to design IDP-based soft biomaterials with tunable mechanics and provide a suitable platform to investigate how biochemical reaction dynamics are regulated by the condensate viscoelasticity.In summary, our work reports that the dynamical properties of a phase separated condensate depend on the nature and strength of inter-chain interactions. The material properties of biomolecular condensates are thought to be critical to their physiological function and arrested phase separation (or liquid-to-solid transitions) have been linked to pathological dysfunction5, [KGKGG]5, [RGYGG]5, [RGPGG]5, [RGFGG]5, [RGSGG]5, [RARAA]5, [RQRQQ]5, [RPRPP]5, [RLRLL]5, RG1Y, RG3Y, FUS-RGG, TAF15-RGG, and EWS-RGG were synthesized by GenScript USA Inc. . All peptides contained a cysteine at the C-terminal for site-specific fluorescence labeling. Peptides were reconstituted in RNase-free water (Santa Cruz Biotechnology) containing 50 mM dithiothreitol (DTT) to prevent cysteine oxidation and the stock solutions were split into multiple aliquots and stored at −20 oC. Homopolymeric RNA [rU]40 , ssDNA [dT]40 , G5T30C5 and (G4C2)5 ssNA were purchased from Integrated DNA Technologies (IDT) and reconstituted in RNase-free water. All peptide and RNA stock solutions were checked under the microscope to ensure complete solubilization and the absence of aggregates. No additional purification was performed on the peptides or the RNA.See Table-S1 for a list of peptides used in this study. All peptides  were added to the buffer at a low concentration (0.0003 % solids). These beads were found to partition well within peptide-RNA condensates41. For experiments performed with G5T30C5 and (G4C2)5 ssNAs, the same buffer was used except for the addition of 2.5 mM MgCl2. For FET-RGG condensates , the polypeptide concentration was 10 mg/ml, the ssDNA T40 concentration was 1 mg/ml, and the buffer contained 25 mM Tris-HCl (pH 7.5), 25 mM NaCl and 20 mM DTT.Each type of condensate was prepared by mixing peptide and RNA in a buffer containing 25 mM Tris-HCl (pH 7.5), 25 mM NaCl and 20 mM DTT. The peptide final concentration was 5.0 mg/ml and the rU40 RNA concentration was 2.5 mg/ml, corresponding to a mass ratio of 0.5. This ratio was chosen based on solution turbidity measurements of [RGRGG]40 and is built on the work on optical tweezer-based microrheology by Mason and Weitz35. The complex modulus of a material (condensate) is calculated from the analysis of the position fluctuations of an embedded probe particle trapped with an optical tweezer. The motion of a trapped bead within the medium is mediated by the thermal fluctuations of the medium and constrained by the harmonic potential of the optical trap. This is one of the few microrheology methods that do not require an independent optical trap calibration procedure87.The method of passive microrheology used in our study was introduced by Tassieri, Preece, and EvansCondensate-forming samples (5 µl) of peptides and RNA were placed on a Tween20-coated microscope coverslip . Next, a glass slide with two double-sided tape strips was gently placed on the sample, sandwiching it between the coverslip and the glass slide. ~100 µl of mineral oil was injected into the chamber between the coverslip and the glass slide such that oil surrounds the sample from all directions. This was done to ensure that the sample does not experience evaporation or evaporation-induced flow. The condensate sample was left to equilibrate for a minimum of 1 h. The pMOT experiments were not initiated until all droplets were settled on the coverslip surface and no droplet fusion events were observed. To measure the frequency-dependent viscoelastic moduli, the custom-made flow chamber was loaded onto a correlative confocal microscope-optical tweezer setup (LUMICKS C-Trap) with a 60x water immersion objective and a bright-field camera. Next, the optical trap was used to trap a bead that is embedded within a peptide-RNA condensate. The trapping power was set to ~100 µW initially to trap the bead. Using the optical trap, the bead was then positioned at the center of the condensate and ~2–5 µm above the coverslip surface. The trapping power was lowered to the minimum value that ensures constraining the bead within the optical trap. The trap-constrained motion of the bead was tracked using the bright-field camera with a 500 Hz acquisition rate. The trajectory of the bead (X-Y coordinates as a function of time) was extracted using the built-in tracking algorithm in the instrument (LUMICKS C-trap). For each peptide-RNA system, we collected trajectories from 3–5 condensates for three independently prepared samples . Every single trajectory was obtained by tracking the bead for a minimum of 10 min and a maximum of 45 min depending on the peptide-RNA system and the quality of the extracted autocorrelation curves . All trajectories were analyzed using custom-built python scripts to obtain frequency-dependent viscoelastic moduli and viscosity. Trajectories that showed asymmetric motion in the X-Y direction were discarded. The analysis of the pMOT experiments as well as additional control measurements are discussed in detail in Supplementary Note 88 plugin in Fiji-ImageJ. Mean squared displacement (MSD) was calculated from the trajectories for several lag times 5-rU40 condensates), the MSD can be fitted to extract the diffusion coefficient  were used for VPT measurements. The samples were prepared at identical conditions as described in the sample preparation section. Fluorescence video imaging was done using a Zeiss Primovert inverted microscope equipped with a 100x oil immersion objective and a Zeiss axiocam 503 monochrome camera. Movies of the beads diffusing within the condensate were collected for approximately 2–5 min. Particle tracking was performed using Trackmate1/2 values. For each peptide-RNA sample, 5 recovery traces were collected and averaged. Intensity error bars were estimated using the standard deviation at each recorded time point.Peptide-RNA condensates were prepared using 5.0 mg/ml peptide and 2.5 mg/ml rU40 RNA mixed in 25 mM Tris-HCl (pH 7.5), 25 mM NaCl, 20 mM DTT containing ~500 nM of FAM-labeled RNA oligo rU10 (purchased from IDT). Samples were injected in a Tween20-coated imaging chamber and loaded to the confocal microscope stage (LUMICKS C-trap). Bleaching was achieved using a 488 nm laser at 100% laser power for ~0.5 seconds. The fluorescence intensity of a given region of interest (ROI) was then recorded until the recovery was complete. The bleaching ROI size and shape were kept identical across all samples. The intensity time traces were plotted for comparison and extraction of tph), the sample was heated first to maximum temperature (90 °C) until all condensates were dissolved. Next, the sample was cooled down in steps of 5 °C and an equilibration time of 5 min until LLPS was observed. Once the LLPS transition was noted, samples were heated again until condensates were dissolved and the LLPS temperature was subsequently approached in steps of 2 °C and equilibration time of 5 min in each temperature to determine the Tph.Peptide-RNA condensates were prepared using 5.0 mg/ml peptide, 2.5 mg/ml rU40 RNA mixed in a buffer  with the desired salt concentration as noted in Fig. 57. The smallest dextran molecule that partitioned negatively in the condensate signifies the upper limit of the mesh size.Peptide-RNA samples were prepared at 5.0 mg/ml peptide and 2.5 mg/ml rU40 RNA in a buffer containing 25 mM Tris-HCl (pH 7.5), 25 mM NaCl and 20 mM DTT. After mixing the peptide and the RNA and the formation of condensates, a small concentration (~500 nM) of tetramethylrhodamine-labeled dextran of desired size was added to the sample. The sample was mixed and subsequently imaged under a confocal microscope (LUMICKS C-trap). If the hydrodynamic radius of the dextran molecule is smaller than the mesh size of the condensate, it is expected that dextran molecules will be positively recruited into the condensates. However, if the hydrodynamic radius of the dextran molecule is larger than the mesh size of the condensates, dextran recruitment will not occur. The partition behavior of dextrans of variable molecular weights (and variable hydrodynamic radii) was recorded and the mesh size range was determined accordingly89. Briefly, glass slides were incubated in 2% Hellmanex solution for two hours and then rinsed with milliQ water and dried under compressed airflow. Next, the glass slides were immersed in a solution containing 20 mg/ml of methyl-PEG5K-Silane and 100 mM HCl dissolved in toluene and incubated for 18 h in a closed glass staining jar. Glass slides were then rinsed with toluene, 90% ethanol, and MilliQ and dried under compressed airflow.For the temperature-salt state diagram, mPEG5K-Silane was used to coat glass slides and coverslips to prevent condensates from sticking to the glass surfaces. The PEG coating procedure was performed according to the description of Alberti and coworkersFor all other experiments in this work, Tween20 coating was used to prevent droplet spreading on the glass surface. Glass slides and coverslips were first cleaned with 70% ethanol and dried under compressed airflow. Next, glass slides and coverslips were immersed in a 20% vol/vol solution of Tween20 for 30 min. Subsequently, glass slides and coverslips were rinsed 6–7 times with MilliQ water and dried using compressed air. Finally, glass slides and coverslips were dried in an oven set at 40 °C for 4–8 h and stored at room temperature for later use.5 with rU40 RNA at 5.0 mg/ml peptide and 2.5 mg/ml RNA concentrations in a buffer containing 25 mM Tris-HCl (pH 7.5), 25 mM NaCl, and 20 mM DTT. Carboxylate-coated beads were added to the buffer along with the bait molecule (either [RGRGG]5-A594 or rU10-FAM) before mixing the peptide and RNA. The bait molecule concentration was kept at ~200 nM. Upon mixing the peptide and the RNA, condensates were formed. The sample was thoroughly mixed and then sandwiched between a coverslip and a microscope glass slide spaced by two layers of double-sided tape. The sample was kept to equilibrate for 10 min and then loaded onto a confocal microscope . Several bead-harboring condensates were imaged using both brightfield and fluorescence illumination. For each bait molecule ([RGRGG]5 or rU10), two independent samples were prepared and several condensates were imaged. Intensity profiles were plotted to inspect the fluorescence intensity at the bead surface and compare it with the intensity inside the droplet.Samples were prepared by mixing [RGRGG]The explicit all-atom (AA) simulations with model tri-nucleotide and tri-peptide systems were done with OpenMM 7.5 using Amber a99SB-disp*-ildn force field for biomolecules and TIP3P model for water. The system consisted of tri-peptides and tri-nucleotides packed in a cubic box with a concentration of ~100 mg/ml, solvated, and charge neutralized with Na+ and Cl− ions at 10 mM concentration at peptide:RNA ratio of 1:2. The total number of atoms was ~100,000. In total 10 independent replica simulations were run for 1 microseconds each. The combined trajectory from the 10 replicas was used for deriving the potentials of mean force by inverting the radial distribution functions of monomeric units. For each simulation trajectory, integration time was set to 2 fs. After minimization, the simulations were done in an NPT ensemble where the pressure was kept at 1 atm via Monte Carlo Barostat and the temperature was kept at 300 K using a standard Langevin integrator with 1/ps set for the friction coefficient.42. The short-range potentials were calibrated according to the all-atom simulations to account for the differential stickiness of amino acids. Slabs were made by first randomly packing polypeptide and RNA chains in a cubic box. The box was then compressed to ~25 nm over the course of 105 steps at a high temperature corresponding to T = 600 K. The z-axis of the box was then extended to ~125 nm, and the packing was equilibrated for 106 steps to allow the dense and dilute phases to form while quenching the system to the target temperature. Finally, the equilibrated dense and dilute phases were simulated for an additional 107 steps to collect data. Simulations were run at various temperatures with 4000 polypeptide chains and an appropriate number of RNA chains ensuring a net chain charge neutrality. In all simulation steps, a Langevin integrator was used with a friction coefficient of 0.01.The simulations with coarse-grained (CG) models of protein and nucleic acid chain mixtures were done using HOOMD-Blue 2.9.6 molecular dynamics library. We used a 1 bead per residue/base model for amino acids and RNAs based on the formulation first presented in Alshareedah and coworkers54. RG/RGG motifs within these proteins were extracted using a custom python script. RG/RGG motif was defined as per Thandapani et al.52. Briefly, peptide motifs with the sequences RG-x(0-4)-RG and RGG-x(0-4)-RGG were extracted, where x can be any amino acid. A binary presence/absence test was performed to determine how many of the extracted sequences contain each of the 20 amino acids within the spacer . Results are presented as a bubble chart in the main text Fig. Amino acid sequences of RNA-binding proteins were downloaded from Uniprot (RefSeq) based on the annotation provided in RBPDB90 (version 1.52p) was used for image processing. OriginPro (2018b) was used for Graphing. Adobe Illustrator CC  was used for the figure assembly and production. ZEN  was used for image recording using a Zeiss Primovert microscope. Bluelake (v1.6.11) was used for image recording and particle tracking using Lumicks C-Trap microscope. HOOMD-Blue 2.9.6 was used for coarse-grained molecular dynamics simulations and OpenMM 7.5 was used for all-atom simulations. Ovito 3.5 was used for generating phase coexistence snapshots and nglview 3.0 was used for visualizing all-atom residue-base interaction snapshots shown in Fig. pMOT analysis was done using custom-made python scripts. Fiji-ImageJAll experiments reported in this manuscript have been repeated at least three times with consistent results. The viscosity measurements are repeated for at least 12 data points from different condensates across three independently prepared samples. The relaxation times are obtained from at least 12 data points from different condensates across three independent sample preparations. The VPT experiments were replicated three times across different sample preparations. Bright-field and fluorescence microscopy images are representative of a large set of images exhibiting similar features. For temperature-salt state diagrams, random points were selected and reproduced three times successfully. FRAP experiments were repeated five times (within 1-2 sample preparations) for each peptide-RNA system. Microscopy images in Figs. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Positive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS), is the first to use a positive and unlabeled learning framework to account for the under-detection of metastasis events in building a classifier. PLUS is specifically tailored for studying metastasis that deals with the unbalanced instance allocation as well as unknown metastasis prevalence, which are not considered by other methods. PLUS achieves superior performance on synthetic datasets compared with other state-of-the-art methods. Application of PLUS to The Cancer Genome Atlas Pan-Cancer gene expression data generated metastasis potential predictions that show good agreement with the clinical follow-up data, in addition to predictive genes that have been validated by independent single-cell RNA-sequencing datasets.Metastatic cancer accounts for over 90% of all cancer deaths, and evaluations of metastasis potential are vital for minimizing the metastasis-associated mortality and achieving optimal clinical decision-making. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable. The under-diagnosis of metastasis events results in biased classification labels, and classification tools using biased labels may lead to inaccurate estimations of metastasis potential. This issue is further complicated by the unknown metastasis prevalence at the population level, the small number of confirmed metastasis cases, and the high dimensionality of the candidate molecular features. Our proposed algorithm, called  Metastasis is the major cause of cancer-related deaths, and evaluations of metastasis risk are essential for tailored treatment of cancer patients. Existing methods often build a classifier using the clinical metastasis diagnoses as binary responses or detect genomic features significantly associated with metastasis-related survival outcomes. However, these methods tend to identify genomic predictors that have little consistency across different cancer types. Thus, there is an urgent need for a powerful tool to characterize the cancer metastasis potential applicable across a wide span of cancer types. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable, which results in biased estimations of metastasis potential. Our proposed algorithm, called PLUS, considers patients with metastasis diagnosis as positive instances and the remainder as unlabeled instances, meaning they are either metastatic or non-metastatic. Such a classifier given by PLUS rendered concordance between the predicted cancer metastasis and observed metastasis survival outcomes in the follow-up data for almost all cancer types considered. The selected genes were found to perform functions consistent with experimental research findings and are capable of clustering the single cells based on their levels of metastasis potential. PLOS Computational Biology Methods paper.This is a Metastatic cancer is responsible for over 90% of all cancer deaths ,2. CompaPrevious work has provided strong evidence indicating that a number of genomic markers in primary tumors are associated with the propensity of a patient to develop metastatic relapse, and that distant metastasis events can be inferred from gene expression profiles within the primary tumor bulk ,9. For eOn one hand, many of these metastasis predictors have been developed for a certain cancer type and thus are less generalizable to other cancer types. In fact, the molecular signatures of cancer metastasis reported in different studies hardly overlap . RecentlPositive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS) to address the aforementioned challenges. The ultimate goal of PLUS is to enable early metastasis event prediction at the pan-cancer level, as well as to infer biologically meaningful gene markers for metastasis potential. PLUS belongs to a category of classifiers called positive and unlabeled learning (PU learning). Whereas the input to a binary classifier normally consists of positive and negative incidence sets, in PU learning, a learner has access to only positive and unlabeled incidences, and it is assumed that the unlabeled data can contain both positive and negative incidences . In According to this observation, we propose a new way to transform lied see . g(·) isAt each iteration, we 1) randomly sample the same number of observed positive instances from the unlabeled set with replacement and 2) conduct a one-step EM calculation, but only use only the new transformation function (6). In this manner, we can handle observation unbalancedness by maintaining a reasonably high ratio at each step. Then we 3) update the estimated probability for each unlabeled instance. Repeat step 1–3 until the estimated probabilities are stabilized. We take advantage of this bootstrap scheme to reduce the noise and increase the robustness. The details of the algorithm, as well as a flowchart, are given in E←fθ(S)L      End;P and the new LU, to yield the final estimation Run a PLR based on L.To comprehensively assess the proposed method, we designed a series of simulation studies. Four different scenarios for the noise distribution were simulated, corresponding to a balanced population with two well-separated classes , a balanced population with two classes not well-separated , an unbalanced population with two well-separated classes , and an unbalanced population with two classes not well-separated . Alterations to the population unbalancedness and separation are achieved by designing different propensity score functions as follows:Clear Balanced Scenario: Noisy Balanced Scenario: Clear Unbalanced Scenario: Noisy Unbalanced Scenario: Pr(Y = 1) for each of the scenario in The functions look similar, but their non-linear properties are distinguished. Here, we show distributions of Xn×p~MVN, where the total number of covariates p can take is 100, 200, and 400 and the sample size n is 2000. For all environments, only six of the variables are relevant to the binary outcome Y. To generate PU instances, we randomly flip the true label 1 to 0, and the probability of a sample being flipped determines the observation balancedness. A higher flipping probability corresponds to a lower observation balancedness, with the sum of these two values equaling 1. The level of observation balancedness ranges from 0.2 to 0.5. Its impact is shown in x-axis.We simulate the covariate matrix PUlasso algorithm, which implements the EM algorithm for penalized likelihood estimation [Ada-KNN, (3) Ada-Logit, and (4) Ada-SVM methods all belong to a multi-method wrapper, called AdaSample [XGBoost is a state-of-the-art decision-tree-based classification algorithm that uses a gradient boosting framework [random forest is a classification algorithm consisting of many decisions trees, which uses bagging and feature randomness when building each individual tree in an attempt to create an uncorrelated forest of trees whose combined prediction is more accurate than that of any individual tree [PRL places an L1 penalty on the logistic regression coefficients to enable variable selection. Notably, the PLR method was applied to the synthetic data using the true underlying label for method validation. For real data, PLR was also applied as a baseline method with the observed labels treated as true, which leads to an underestimated metastasis potential. Of all the methods, the Ada- methods, like most of the PU-learning methods, are incapable of variable selection. For each simulation setting, we conducted 100 repetitions. In each repetition, a predictive model is trained by each method, which generates an ROC curve with false and true positive rates at the x- and y- axis and the AUC. The average AUC among the 100 repetitions is used to evaluate the prediction performance for each method and simulation setting.PLUS was compared with six state-of-the-arts PU learning or binary classification methods and one oracle reference method: (1) The timation , and it daSample . AdaSampdaSample . (5) XGBramework . (6) Theual tree . (7) PRLhttps://bioinformatics.mdanderson.org/BatchEffectsViewer/). Specifically, the UNC GA- and BCCA GAII-sequenced samples were separately adjusted to the UNC HiSeq data.We retrieved the RNA-seq data from the PanCanAtlas . The retBaseline and follow-up clinical information for all cancer samples was retrieved from the Genomic Data Commons Data Portal. Baseline diagnosis of a distant metastasis event was retrieved from the baseline clinical data, based on whether the patient is diagnosed as stage M1 in the TNM stage information. Specifically, a patient is determined to be diagnosed with metastasis if (1) the patient is in the M1 stage according to at least one of the “ajcc_metastasis_pathologic_pm”, “ajcc_metastasis_clinical_cm”, “clinical_M”, and “pathologic_M” criteria in the TCGA clinical information or (2) there exists direct evidence of metastasis under one of the following terms: “metastatic_dx_confirmed_by”, “metastatic_dx_confirmed_by_other”, “metastatic_tumor_site”, “metastasis_site”, “metastatic_site_other”, “metastasis_site_other”, “metastatic_tumor_indicator”, “metastatic_disease_confirmed”, “metastatic_site”, and “other_metastatic_site”. Based on these criteria, 553 samples were confirmed to have a metastasis diagnosis while the remainder were treated as unlabeled samples.To evaluate the accuracy of the predicted metastasis potential, we utilized the available TCGA follow-up data and collected PFS data. We specifically defined events in PFS as a patient having a new tumor event, whether this event was a progression of disease, local recurrence, distant metastasis, new primary tumor at any sites, or deceased with the cancer and no new tumor event, including cases with a new tumor event whose type is N/A, based on which the PFS specific to metastasis events can be computed. The unit of progression-free interval time is days. For the progression events, we collected either new_tumor_event_dx_days_to or death_days_to, whichever was applicable, and for the censored cases, we collected either last_contact_days_to or death_days_to, whichever was applicable. These collection criteria are in accordance with the existing literature .GSE103322) were retrieved from the Gene Expression Omnibus database [GSE103322 contains data for 1040 cancer cells from 6 primary head and neck cancer tissues with extracapsular extension, a significant indicator of a metastasis event at a primary site, and 1468 cancer cells from 14 primary cancer tissues without extracapsular extension. Notably, the cancer cells from each scRNA-seq dataset can be classified as having high or low metastasis potential by the provided pathological or phenotypic information.Two scRNA-seq data sets for human breast cancer (GSE75688) and head and neck cancer (database –37. The s(i) of each cell and the silhouette coefficient defined below were utilized to determine whether the cells from the tissues with higher metastasis potential were more closely clustered when the metastatic-predictive genes were used. We note that there are only two oracle cell clusters, namely, cells of high and low metastasis potential, in each dataset.Cm and Cnm represent cells with high and low metastasis potential, d is the distance between cells i and j in the dimension-reduced space, and s(i) is the silhouette width of the cell i. Notably, only the cells in Cm are utilized to compute the silhouette width and silhouette coefficient. The rationale here is that the silhouette coefficient can validate the use of the predicted metastasis-predictive genes and can better cluster the cells of high metastasis potential in independent scRNA-seq data. Here, 2D UMAP-based dimension reduction was utilized for the results visualization and computation of the distance d. The average s(i) values of the cells with high metastasis potential derived from the cell clusters inferred by using all genes or metastasis-predictive genes were compared, where a higher average silhouette width suggests a better clustering of the cells with high metastasis potential.Both GSE75688 and GSE103322 were collected by using C1-Fluidigm or C1-SMART-seq protocol, and the sequencing saturation for both datasets are high. We selected only the malignant cells based on the cell labels provided in the original works. Seurat 3.0 was utilized for basic data processing . All theIn addition to the silhouette width, we also assessed whether the PLUS-selected metastatic-predictive genes are more enriched by genes that are significantly associated with the metastasis status in each scRNA-seq dataset. For each gene, the association between the single cells’ gene expression and their metastasis potential, which is defined as the metastasis potential of the patients where the cells were derived from, was tested by a Student’s t-distribution-based test of their Pearson correlation, with p<0.01 as the significance cutoff. Genes with significant positive association with the metastasis potential are called metastasis Enrichment of the scRNA-seq data-derived metastasis-associated genes in the TCGA data-derived metastatic-predictive genes was tested by a hypergeometric test.S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 Table(CSV)Click here for additional data file.S5 Table(CSV)Click here for additional data file.S6 Table(CSV)Click here for additional data file.S1 Fig(EPS)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigThe x-axis is the probability of a positive label and the y-axis is frequency. (a) Noisy Balance represents the case of the positive and negative labels are balanced, and the two classes are less separable. (b) Noisy Unbalanced represents the case of the positive and negative labels are unbalanced, and the two classes are less separable. (c) Clear Balanced represents the case of the positive and negative labels are balanced, and the two classes are separable. (d) Clear Unbalanced represents the case of the positive and negative labels are unbalanced, and the two classes are separable.(TIF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Following the publication of the original article , we were2Shanghai Skin Disease Clinical College of Anhui Medical University, Shanghai Skin Disease Hospital, Shanghai, 200443, People’s Republic of China.3Department of Dermatopathology, Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai, 200071, People’s Republic of China.


High-grade serous ovarian cancer (HGSOC) is the predominant and deadliest form of ovarian cancer. Some of its histological subtypes can be distinguished by frequent occurrence of cancer-associated myofibroblasts (CAFs) and desmoplastic stroma reaction (DSR). In this study, we want to explore the relationship between therapy outcome and the activity of CAF-associated signaling pathways in a homogeneous HGSOC patient collective. Furthermore, we want to validate these findings in a general Epithelial ovarian cancer (EOC) cohort.The investigation cohort consists of 24 HGSOC patients. All of them were treated with platinum-based components and clinical follow-up was available. The validation cohort was comprised of 303 patients. Sequencing data (whole transcriptome) and clinical data were extracted from The Cancer Genome Atlas (TCGA). RNA of HGSOC patients was isolated using a Maxwell RSC instrument and the appropriate RNA isolation kit. For digital expression analysis a custom-designed gene panel was employed. All genes were linked to various DSR- and CAF- associated pathways. Expression analysis was performed on the NanoString nCounter platform. Finally, data were explored using the R programming environment (v. 4.0.3).In total, 15 CAF-associated genes were associated with patients’ survival. More specifically, 6 genes  were linked to poor therapy outcome. Though a variety of different pathways appeared to be associated with therapy failure, many were related to CAF paracrine signaling, including MAPK, Ras and TGF-β pathways. Similar results were obtained from the validation cohort.In this study, we could successfully link CAF-associated pathways, as shown by increased Ras, MAPK and PI3K-Akt signaling to therapy failure (chemotherapy) in HGSOC and EOCs in general. As platinum-based chemotherapy has been the state-of-the-art therapy to treat HGSOC for decades, it is necessary to unveil the reasons behind resistance developments and poor outcome. In this work, CAF-associated signaling is shown to compromise therapy response. In the validation cohort, CAF-associated signaling is also associated with therapy failure in general EOC, possibly hinting towards a conserved mechanism. Therefore, it may be helpful to stratify HGSOC patients for CAF activity and consider alternative treatment options. According to the Global Cancer Statistics 2020 (GLOBOCAN), ovarian cancer ranks high as the deadliest tumor originating from gynecological sites, especially when comparing new cases 313.959) and disease-related deaths 207.252) 3.959 and. Type 1 .252 3.95The two pillars of HGSOC therapy are cytoreductive surgery and adjuvant chemotherapy. The outcome is directly depended on disease stage . Since tIn recurrent cases, platinum-based chemotherapy is also the treatment of choice for patients deemed platinum-sensitive. Moreover, platinum-sensitive tumors are especially vulnerable for treatment with Poly-ADP-Ribose-polymerase (PARP)-inhibitors in the first-line as well as the recurrent situation , 17. PlaOne particular molecular subtype of HGSOC, the mesenchymal subtype is characterized by frequent generation of desmoplastic stroma. Mixed subtypes containing both epithelial and mesenchymal structures are also known. The occurrence of desmoplastic stroma in HGSOC is linked to decreased overall survival and resistance to platinum-based chemotherapy –22. ThisConsidering the abundance of CAFs in certain HGSOC subtypes and the link to dismal outcome, it seems very plausible that CAFs and associated stroma support tumor cells by paracrine signaling and providing a physical barrier, which facilitates the often-occurring platinum-resistance in HGSOC and decreased survival –35. We eIt should be noted that the following methods were applied as it was described in . Howeverth edition of TNM guidelines , a binary outcome variable was defined that correlates to resistance against cisplatin. Patients displaying poor therapy outcome or therapy resistance were characterized by an RFS below 6 months after therapy completion, which conforms to the sources mentioned above  of epithelial ovarian cancers (EOC) served as a validation cohort for this study. Gene expression data (RNA Seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) database . The primary site of tumors within the validation cohort were the ovaries, though three tumor samples were derived from the omentum. A key selection criterion was sufficient follow-up that allowed for calculation of therapy outcome after platinum treatment. Poor therapy outcome or therapy resistance was defined as it was described in 2.1.1 (RFS below 6 months after therapy completion) –15. MediTumor tissue used for the study was formalin-fixed and paraffin-embedded (FFPE). All samples have been collected between 2005 and 2010. Only one tumor per patient was selected for further analysis. Each sample analyzed contained at least 85% tumor cells. All specimens have been stored at room temperature in the archives of the Institute of Pathology, University Hospital Essen. Tissue sections (thickness: 10 microns) were made using a “Microm HM340E” microtome . The amount of sections was dependent on available tumor tissue (at least two sections per sample). The first tissue section from the surface layer has been discarded due to possible oxidation processes. In order to avoid loss of RNA yield, slides were stored by freezing (-20°C) until the RNA isolation procedure commenced.® RSC Instrument  using a Maxwell® RSC RNA FFPE kit . The process was conducted according to the manufacturer’s instructions. In the final step, RNA was eluted in 50 µL RNase-free water.RNA was isolated in a semi-automatic workflow with the help of the MaxwellAfter the isolation process, RNA yield was quantified using a Qubit 2.0 fluorometer . Samples were prepared for Qubit measurement by utilizing an RNA broad-range assay kit  according to the manufacturer’s instructions. In short, the fluorometric quantification is based on linear regression using predefined standards provided within the kit.Samples harboring sufficient RNA yield were analyzed on the NanoString nCounter MAX/FLEX platform. 100 ng total RNA were used for each reaction. Digital expression analysis of 221 genes associated with DSR, TGF-β-, PI3K-Akt and MAPK signaling was performed utilizing a customized panel encompassing key genes of those pathways . Beside probes covering the target genes, a variety of technical and biological controls are included in the panel. First, eight different negative controls comprising probes with sequences not complimentary to the human transcriptome are included to estimate unspecific binding capability and identify potential alterations in the hybridization process. Second, six artificial RNA sequences with predefined concentration are included in the panel . Those serve for detection of technical issues as well as to define the dynamic range of the assay and for calibration of linear regression, as those controls are diluted in a predefined manner and can be used as a standard curve. Samples without linear growth of those inherent positive controls indicating incomplete hybridization or elevated negative controls leading to decreased signal to noise ratio have been re-run. Third, nine reference genes for biological normalization purposes have been included in the panel, covering three high, three medium and three low expressed targets.t-test of negative controls and target specific counts in all samples to identify genes not relevantly expressed (p < 0.05)  .Statistical and graphical analyses were also performed within the R statistical programming environment (v. 4.0.3)t-test was utilized  were calculated based on existing survival data and combined survival curves. Beside p-value, hazard ratio (HR), time-dependent survival rate and median survival time have been calculated. Gene set enrichment analysis (GSEA) was performed using the WEB-based Gene SeT AnaLysis Toolkit  website . Results were considered significant at p < 0.05 after adjustment .Predictably, outcome after chemotherapy is linked to reduced overall survival (p < 0.05). However, in multivariate analysis, it turned out that this influence was independent of other clinical covariates like age at time of diagnosis, tumor grading or tumor stage  and RFS . Group-based survival differences were asserted by cox proportional hazard models. The patient groups were separated based on whether genes displayed high or low expression rates. In either case, high expression of both MMP13 and CGA were detrimental to patients’ survival  was performed 5. The lAccording to GSEA, genes in association with worsen therapy outcome were highly expressed in signaling pathways linked to “Necroptosis”, “Cytokine-cytokine receptor interaction” and “Alcoholism”. However, they were barely expressed in signaling pathways linked to “Hypertrophic cardiomyopathy”, “Malaria” and “Amoebiasis”  like MMP13. Other important signaling pathways, which are also linked to fibroblast activity are the TGF-β- . Thus, it was composed of different malignant entities, including HGSOC. Similarly, TGF-β, MAPK-, PI3K-Akt activity was increased when correlated to therapy outcome  may be helpful to unveil reasons for poor therapy outcome in HGSOCs. As a key factor within TME, CAFs employ miscellaneous functions. One of their best-known functions is the generation of fibronectin and collagen, two substantial components of stroma tissue and the extracellular matrix. Simultaneously, they counterbalance this activity by production of matrix-metalloproteases , 28, 33. cascade , 57, it  cascade , 59. The cascade , 32.Based on histological and genetic subtyping, five variants of HGSOC can be distinguished. One of them is the mesenchymal subtype which is characterized by occurrence of DSR. Furthermore, this subtype is also associated with a poor survival prognosis , 25, 60.via secretion of TGF-β1, which then leads to invasion and metastasis . The patients/participants provided their written informed consent to participate in this study.Conceptualization: PB and FM. Methodology: MW, EM, SB, and FM. Software: MW and FM. Validation: EM, SB, AB, PM, PB, and FM. Formal analysis: MW, EM, SB, AB, PM, PB, and FM. Investigation: MW, EM, SB, AB, and PB. Resources: KS and RK. Data curation: MW, EM, SB, AB, PM, PB, and FM. Writing-original Draft preparation: MW, PB, and FM. Writing-review and editing: All authors. Visualisation: MW and FM. Supervision: FM. Project administration: KS, RK, PB, and FM. Funding acquisition: KS, RK, PB, and FM. All authors have read and agreed to the published version of the manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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