In multiple solid tumours, including gliomas, the mechanical properties change as the disease progresses. If and how mechanical cues regulate tumour cell proliferation is currently not fully studied. PIEZO1 has recently been identified as a crucial mechanosensitive cation channel in multiple solid tumours. However, we didn’t find any clinical data describing the association between PIEZO1 expression and glioma. To investigate the role of PIEZO1 in gliomas, we analysed PIEZO1 gene expression at the transcriptome level, genomic profiles and the association of PIEZO1 with clinical practice. In total, 1633 glioma samples with transcriptome data, including data from the Chinese Glioma Genome Atlas RNAseq, the Cancer Genome Atlas RNAseq and GSE16011 databases, were included in this study. Clinical information and genomic profiles including somatic mutations were also obtained. We found that PIEZO1 expression was highly correlated with malignant clinical and molecular subtypes of glioma. Gene ontology analysis showed that expression of PIEZO1 was correlated with tumour microenvironment-related genes that encode proteins involved in extracellular matrix (ECM) organization, angiogenesis and cell migration. Additionally, PIEZO1 was shown to be involved in tumour progression by serving as the central checkpoint of multiple ECM remodelling-related signalling pathways to modulate tumour cell proliferation and the tumour microenvironment in turn. Finally, high PIEZO1 expression was correlated with reduced survival time and acted as a robust biomarker for poor prognosis in gliomas. Taken together, the results indicated that high PIEZO1 expression is closely associated with highly malignant gliomas. Importantly, PIEZO1 serves as a key factor involved in sensing mechanical properties in the tumour and can regulate both tumour cells and their microenvironment to promote glioma progression, and it is also a potential therapeutic target for the treatment of gliomas. The TME comprises all components surrounding a tumour; this includes blood vessels, immune cells, signalling molecules and extracellular matrix proteins. The TME interacts closely with tumour cells by providing nutrients and structural support, by communicating through the exchange of signalling molecules and often by promoting tumour growth and metastasis.The current gold standard treatment for glioma consists of chemotherapy, radiation therapy and surgery. Treatment strategies targeting the tumour microenvironment (TME) are gaining traction in the clinic3. Tumour cells sense mechanical stimuli such as tissue pressure (stiffness), cell membrane tension, shear stress, and tissue pressure from the extracellular matrix (ECM). These mechanical cues are transduced into biochemical signals to modulate both tumour cells and the TME, promoting cancer progression4. These processes strongly depend on mechanosensitive ion channels.In fact, it has yet to be identified that apart from regulation, nutrition and structural support, mechanical stimuli sensed by tumour cells and neighbouring cells are also provided by the TME and affect every step of tumour progression and metastasis6. First described by Coste et al.7, homologues PIEZO1 and PIEZO 2 independently induce cationic non-selective activated currents in response to mechanical stimuli11. In breast cancer cells, PIEZO1 has been shown to increase motility in response to physical cues12, and PIEZO2 has been shown to play a role in the anchoring of brain metastatic cells13. As tumour cells grow, the mechanical forces within the tumour and TME increase, and the Piezo channel is activated by these mechanical signals and interacts with focal adhesions and integrin-FAK signalling to regulate tumour development14.PIEZO transmembrane proteins have been identified as mechanosensitive cation channels15. Despite advances in comprehensive care, the median survival time of patients who suffer from gliomas is only 15 months17. Prolonging the survival time is a critical challenge for clinicians treating glioma patients. In the past decade, there has been a surge in studies investigating molecularly targeted therapies and immunotherapies for the treatment of gliomas. However, interindividual tumour heterogeneity appears to temper the success of these studies18. It has become increasingly apparent that tumours do not result from the unregulated growth of a single cell but that tumour formation involves bidirectional abnormal communication between tumour cells and their microenvironment19. This not only plays an essential role during tumour progression and metastasis but also has a profound impact on therapeutic efficacy20. Additionally, recent studies addressed a feedforward circuit mediated by PIEZO1 and tumour tissue mechanics to promote glioma growth21. However, until now, the relationship between PIEZO1 expression and glioma malignancy in human patients has not been studied.Glioma is the most prevalent primary malignant tumour of the central nervous system (CNS) in adultsTranscriptomes from 325 gliomas from the Chinese Glioma Genome Atlas (CGGA) dataset were analysed to investigate the expression of PIEZO1 in gliomas, and we further verified these results by analysing RNA data from The Cancer Genome Atlas (TCGA) network derived from 1032 gliomas and a GSE16011 microarray of 276 cases. Moreover, genomic profiles containing somatic mutations were also analysed in our study. This is the first integrative study characterizing PIEZO1 expression in whole grade glioma both molecularly and clinically. Our work aims to improve the current knowledge of the role of mechanical forces in tumour aggressiveness mechanisms and provide a potential therapeutic target for gliomas.22. mRNAseq data obtained from 1032 WHO grade II to grade IV gliomas were downloaded from The Cancer Genome Atlas (TCGA) . We included 276 cases from the GSE16011 cohort. The RNAseq data used to study the specific tumour anatomic structure in GBM was collected from the Ivy Glioblastoma Atlas Project .The present study was approved by the Beijing Tiantan Hospital Institutional Review Board (IRB)/Ethics Committee of Beijing Tiantan Hospital. In total, 325 RNAseq samples were included in the CGGA dataset. We collected the molecular and clinical data from the CGGA database. The IDH mutation status detection method was described in Guan et al.23 to evaluate the copy number alternations corresponding to PIEZO1 expression. The thresholded copy number at the alteration peaks was determined by GISTIC analysis 24. The gene ontology (GO) analysis in DAVID Bioinformatics Resources 6.8 was used to explore the biological functions of the related genes25. Gene set enrichment analysis (GSEA) was performed as presented in Li et al.26. GO analysis of the most highly correlated genes was used to generate a heatmap after Spearman correlation analysis. The GO gene set was downloaded from MSigDB (https://software.broadinstitute.org/gsea/msigdb/index.jsp). ECM-related metagenes were described in the work of Rody and Jain28. Metagene expression values were obtained by quantifying the normalized mean expression values of all genes in their respective cluster28.We obtained 741 cases with somatic mutations and associated with RNAseq data from the TCGA database. We used GISTIC 2.0Twenty Paraffin-embedded human glioma tissues for validation , normal human brain and mouse retina tissues were collected for staining. We reviewed the tissue slides through the visual evaluation of haematoxylin and eosin (HE)-stained slides. The results of the morphologic identification were independently confirmed by two neuropathologists. We selected a PIEZO1 antibody  to detect PIEZO1 expression. Immunofluorescent (IF) staining of paraffin sections was performed. Briefly, specimens were blocked in blocking buffer for 60 min, the sections were incubated with primary antibody overnight at 4 °C, and then the sections incubated with the corresponding secondary antibody  at room temperature in the dark for 1 h. After washing with PBS buffer, the slides were coverslipped with Prolong Gold Antifade Reagent. All images were analysed using Image-J based on the area fraction or intensity of positive staining.https://www.r-project.org/) and ImageJ . Spearman correlation analysis was used to evaluate the correlations between continuous variables. The differences in variables between groups were assessed by Student’s t-test, one-way ANOVA, or Pearson's Chi-squared test. We used a Kaplan–Meier survival curve to determine the survival distributions and performed a log-rank test to assess the statistical significance. Univariate and multivariate Cox proportional hazard models were determined to evaluate the prognostic value of PIEZO1. Patients with missing information were excluded from the statistical analysis. All statistical tests were two-sided. A p value < 0.05 was considered significant.All statistical analyses were performed using the R programming language -oligodendroglioma , LGG-astrocytoma , LGG with wild-type IDH status (LGG IDH-wildtype), glioblastoma (GBM) with mutant IDH status (GBM-IDHmut), and GBM with wild-type IDH status (GBM-IDHwt). The expression of PIEZO1 was the highest in GBM-IDHwt and the lowest in LGG-Oligo . From CGGA, 174/188 gene sets were found to be upregulated in samples expressing PIEZO1 at high levels, among which 129 gene sets were significant at an FDR < 25%, and 82 gene sets were significantly enriched at a nominal p value < 5%. In the TCGA, 2522/3453 gene sets were upregulated in high PIEZO1 expression samples, among which 379 gene sets were significantly enriched at a nominal p value < 5%. Figure Next, we used the (c5.bp.v7.0.symbols) gene set from GSEA to identify the activated biological pathways related to ECM regulation. Several signatures were found to strongly correlate with high PIEZO1 expression samples in both CGGA and GCTA. In the CGGA, 3210/3899 gene sets were upregulated in high PIEZO1 expression samples, among which 1999 gene sets were significant at an FDR < 25%, and 1367 gene sets were significantly enriched at a nominal p value in increasing order in the CGGA dataset, cell microenvironment-related genes that were responsible for ECM organization, cell adhesion, angiogenesis, cell migration and proliferation were positively correlated with PIEZO1 expression  in the TCGA and CGGA datasets, respectively, to conduct an analysis. We used GO analysis in the DAVID Bioinformatics Resources 6.8 to investigate the functions of related genes. As the gene functions were sorted by the To investigate the expression of genes associated with PIEZO1 in the microenvironment in gliomas, we included an ECM-related gene set from the GSEA and found 110 genes that were most relevant to PIEZO1 (Pearson |R|> 0.35) in the CGGA database to draw the heatmap. Among the selected genes, 106 genes were positively correlated to PIEZO1 expression, while 4 genes showed negative correlation with PIEZO1 expression  and the integrin-encoded gene family (ITG), are known to regulate ECM remodelling and to generate the cytoskeleton network system, and both are highly expressed along with PIEZO1 Fig. E–G.Among its functions in the tumour microenvironment, PIEZO1 appears to play a particularly strong role in ECM signalling pathways to regulate tumour-associated tissue remodelling. We analysed the matrix metalloproteinase (MMP) family, tissue inhibitors of metalloproteinases (TIMP) family, mitogen-activated protein kinase (MAPK) family and phosphoinositide 3-kinase (PI3Ks) family to clarify the role of PIEZO1 in ECM signal transduction. As shown in Fig. 32. Mechanical cues play a critical role in tumour progression and in shaping the TME by regulating both tumour cells and the TME. PIEZO1 is an intrinsically mechanosensitive ion channel that can interact with many signalling pathways in multiple cancer malignancies33. However, it is unknown whether PIEZO1 regulates glioma development and whether the mechanical environment in tumours interacts with the mechanosensory function of PIEZO1 to promote malignancy in gliomas. Therefore, identification of the molecular and clinical correlations between PIEZO1 expression and glioma malignancy will facilitate the establishment of a potential therapeutic target and shed light on glioma treatment.Gliomas are the most common and lethal primary brain tumours in adults. Limited improvements in outcomes have been made despite extensive study and combination of conventional treatments. New therapeutic targets are urgently needed. Studies on microenvironment-targeted therapeutic strategies have opened a new avenue for tumour treatment4 Pa34 and that tumour stiffness correlates with aggressiveness35. These results suggest that increasing intra-tumoural solid pressure developed by high grade glioma provides mechanical cues that stimulate PIEZO1, which further plays an essential role in tumour cell proliferation and metastasis. Moreover, we also found that a high level of PIEZO1 was predictive of a poor prognosis. This is the first study to present the PIEZO1 expression pattern in gliomas based on different grading systems including 2016 WHO molecular classifications and the WHO Grade system according to the CGGA dataset and be verified by the TCGA dataset.In this study, we analysed PIEZO1 expression in glioma. High levels of PIEZO1 expression showed an association with malignant entities, including a high WHO Grade, IDH wild-type status and mesenchymal subtype gliomas. The most important common characteristic among gliomas with a high WHO grade, IDH wild-type status and mesenchymal subtype is aggressiveness. It has been reported that tissue stiffness in human low to high grade gliomas gradually increased from 100 to 1037. In addition, genomic alternations may also contribute to regulating the TME and promoting tumour cell proliferation38. Therefore, PIEZO1 appears to take part in the glioma aggressive pathophysiological process, and it can act as the essential biomarker for glioma malignancy and provides a potential therapeutic target to overcome glioma.To investigate molecular mechanism of glioma, we analysed the distinct genomic alternations in order of increasing PIEZO1 expression. The data show a positive correlation between somatic mutations and PIEZO1 expression. Tumour suppressor genes, including PTEN and NF1, were detected at high amplification peaks with high PIEZO1 expression. The oncogenic driver EGFR was found to be enriched with high expression of PIEZO1. It has been reported that tumour cell microenvironment stiffness regulates tumour development by interacting with EGFR-dependent signalling in glioma40. These results indicate that PIEZO1 regulates glioma aggressiveness through its biological function in ECM remodelling. ECM components are major molecules in the cell microenvironment36. In solid tumours, the ECM has the capacity to drive tumour aggressiveness3. It has been reported that PIEZO1 can activate integrins intracellularly41. As the key ECM adhesion receptors, integrins interact with the integrin-FAK signalling pathway to regulate cancer cell behaviour, promoting migration, proliferation and survival36. Integrin inhibitors  have been tested in clinical trials45. A randomized phase II trial showed that cilengitide monotherapy was well tolerated and noted a modest anti-tumour activity in recurrent glioblastoma45. However, a phase III trial showed that combination of cilengitide with standard temozolomide chemoradiotherapy had no benefit over conventional therapy in glioblastoma, particularly in tumours with methylated MGMT promoter42. Cilengitide as a subunit antagonist of the integrins αvβ3 and αvβ5 remains controversial42. PIEZO1 is the upstream molecule of integrins and can directly sense the mechanical changes caused by tumour tissue pressure, which makes PIEZO1 a potential biomarker as a therapeutic target.As for the biological function of PIEZO1 expression in gliomas, we found that cell microenvironment-related genes that are related to ECM organization, cell adhesion, angiogenesis, cell migration and proliferation were positively correlated with PIEZO1 expression. Additionally, two gene groups, the collagen-encoded gene family (COL) and the integrin-encoded gene family (ITG) that are known to regulate ECM remodelling and compose the cytoskeleton network system were positively correlated with PIEZO1 expression49. PIEZO1 is also involved in 2D cell migration and 3D invasion in the MCF-7 cell line, a gastric cancer cell line, and small-cell lung carcinoma cell lines49. Taken together, PIEZO1 not only regulates tumour cell proliferation but modulates the TME as well to promote tumour cell metastasis.The molecular mechanisms through which PIEZO1 regulates glioma malignancy involve multiple signalling pathways. As shown in the results, PIEZO1 expression is positively correlated with genes that are responsible for cell proliferation, tumour-associated tissue remodelling, immune response and tumourigenesis. These data indicate that PIEZO1 acts as a central functional point to form an integrated mechanical-regulation molecule network, here giving rise to the hypothesis that a feedback mechanism where the increasing tumour tissue stiffness caused by abnormal regulation of cell growth activates the PIEZO1 ion channel and multiple ECM-related pathways that are downstream, in turn modulating cell proliferation and TME remodelling to increase tissue pressure. This also explains why in high grade gliomas such as GBM, PIEZO1 expression is expressed at a high level. Meanwhile, cytoskeletal dynamics are also associated with PIEZO1 expression. A loose supportive structure due to loss of collagen in the TME provides more physical space for tumour cells. In parallel, tumours with a remodelled TME are able to compress blood supply through exerting physical forces on their microenvironment. Hypoperfusion and hypoxia lower the efficacy of tumour targeted drug delivery, promoting immune evasion and tumour metastasis. Evidence has shown that expression of PIEZO1 increased in multiple malignant diseases including bladder carcinoma, synovial sarcoma, osteosarcoma, breast cancer and gastric cancer+ and Grammostola spatulata spider toxin GsMTx4) are not PIEZO1-specific, it has been reported that the use of GsMTx4 may inhibit PIEZO1 activity in the urothelium, affect PIEZO1-dependent arterial remodelling and reduce cell death due to high strain mechanical injury52. Because PIEZO1 plays a vital role in an abundance of physiological processes, more research needs to be done to investigate tumour-specific PIEZO1 knockdown and provide a potential tumour cell and TME-targeted therapy.As expression of PIEZO1 is positively associated with gliomas, pharmacological inhibition of PIEZO1 might prove an efficacious strategy in treating these malignant gliomas. Although current identified inhibitors (ruthenium red, Gd3Our findings indicate that PIEZO1 might play a role as a biomarker in gliomas. The biological significance of PIEZO1 was explored by analysing two large sample-sized glioma cohorts. PIEZO1 expression is correlated with increasing tumour tissue stiffness as tumourigenesis proceeds, and the function of PIEZO1 aggravates tumour cell proliferation and glioma malignancy. Furthermore, the tumour-specific inhibitor of PIEZO1 needs to be addressed in future studies. However, we still need to clarify the specific molecular mechanism of PIEZO1-related mechanical–biological transduction signalling pathways and how to modulate these molecules to regulate glioma progression.In conclusion, using publicly available transcriptomic and genomic profiling data, we found that PIEZO1 was upregulated in high grade glioma and predictive of a poor prognosis. Additionally, PIEZO1 was positively correlated with microenvironment remodelling-related genes and appears to function as a central checkpoint of ECM remodelling pathways. These findings establish PIEZO1 as a novel focus to study the mechanism of glioma malignancy and as a potential therapeutic target.Supplementary Figure 1.Supplementary Figure 2.Supplementary Legends.Supplementary Table 1.Supplementary Table 2.


Ultimate Taipan is a software model checker that combines trace abstraction with abstract interpretation on path programs. In this year’s version, we replaced our abstract interpretation engine and now use a combination of multiple abstraction functions, fixpoint computation, algebraic program analysis, and SMT solving. Our new approach will allow us to integrate new techniques more easily.


The tumour microenvironment (TME) plays a pivotal role in tumour fate determination. The TME acts together with the genetic material of tumour cells to determine their initiation, metastasis and drug resistance. Stromal cells in the TME promote the growth and metastasis of tumour cells by secreting soluble molecules or exosomes. The abnormal microenvironment reduces immune surveillance and tumour killing. The TME causes low anti‐tumour drug penetration and reactivity and high drug resistance. Tumour angiogenesis and microenvironmental hypoxia limit the drug concentration within the TME and enhance the stemness of tumour cells. Therefore, modifying the TME to effectively attack tumour cells could represent a comprehensive and effective anti‐tumour strategy. Normal cells, such as stem cells and immune cells, can penetrate and disrupt the abnormal TME. Reconstruction of the TME with healthy cells is an exciting new direction for tumour treatment. We will elaborate on the mechanism of the TME to support tumours and the current cell therapies for targeting tumours and the TME—such as immune cell therapies, haematopoietic stem cell (HSC) transplantation therapies, mesenchymal stem cell (MSC) transfer and embryonic stem cell‐based microenvironment therapies—to provide novel ideas for producing breakthroughs in tumour therapy strategies. The tumour microenvironment (TME) plays a pivotal role in tumour fate determination. TME components promote the growth, metastasis, immune escape, stemness, and drug resistance of tumour cells. Therefore, modifying the TME to effectively attack tumour cells could represent a comprehensive and effective anti‐tumour strategy. Normal cells, such as stem cells and immune cells, can penetrate and disrupt the abnormal TME. Reconstruction of the TME with healthy cells is an exciting new direction for tumour treatment. Large amount of pre‐clinic and clinic study proved the quietly success in targeting angiogenesis, extracellular matrix (ECM) and cells components within TME.2The components surrounding the tumour cells constitute the functional TME in which tumour cells initiate and grow and from which they invade and metastasize. The TME is a sophisticated network that includes various tumour‐associated cells, such as cancer‐associated fibroblasts (CAFs), cancer stem cells (CSCs), MSCs, tumour‐associated immune and inflammatory cells, pluripotent stromal cells, cancer‐associated adipocytes, pericytes and endothelial cells (ECs). The tumour‐associated cells secrete tumour‐associated exosomes (TEXs) and soluble molecules, including cytokines, kinases, protein, transcription factors, growth factors, hormones and free radicals. They regulate and respond to each other, construct the scaffold of the tumour ECM, and are nourished by tumour angiogenesis, manifesting as a special niche for innutrition, acidity, hypoxia and ischaemia.2.1The tumour ECM, comprising collagen, fibronectin, laminin, vitronectin and tenascin, is secreted by cells in the TME. Via autocrine and paracrine signalling, tumour cells and microenvironment cells, especially CAFs, transform the ECM to an advantageous phenotype for tumour progression.The alteration of the orderly isotropic arrangement of the collagen, called matrix remodelling, results in tumour‐associated collagen signatures (TACS).2.2The infinite proliferation and malignant metastasis of solid tumours are inseparable from angiogenesis in the TME. Tumour angiogenesis is the formation of abnormal vascular tissue with an immature structure, abnormal function and high permeability; it contributes to abnormal haemodynamics, poor nutrient, and oxygen supply, and TME hypoxia and hypoperfusion. Hypoxia is a common condition in many solid tumours. The hypoxic TME harbours cells with a burst of proliferation activity, effective for overgrowing a mass of tumour.2.3The cells in the TME are collectively referred to as tumour‐associated stromal cells; they regulate tumour fate through complex signal communication with tumour cells. Tumour‐associated stromal cells include CAFs, tumour‐associated ECs, pericytes, CSCs, MSCs and immune cells, and other rarer stromal cell populations, such as neurons, fibrocytes, adipocytes and follicular dendritic cells, have also been observed in some tumours.2.3.1Normal fibroblasts and adipose‐derived stem cells in the TME are transformed into CAFs that overexpress α‐smooth muscle actin (α‐SMA) and fibroblast activation protein.In addition, CAFs interplay with other stromal cells to regulate TME ecology. CAFs' secretomes reprogramme cancer cells, immune cells and ECs to facilitate cancer cell invasion and metastasis. Increased glycolysis of CAFs creates acidity in TME to increase generation of myeloid‐derived suppressor cells (MDSCs), inhibit maturation of tumour‐associated dendritic cells (TADCs) and impair the activity of NK and effector T cells.2.3.2Both innate and adaptive immune cells in the TME show low tumour‐recognition and killing ability, which is a key factor contributing to tumorigenesis and progression Figure .2.l‐arginine level, and activating the IL‐10 pathway.The TME affects innate immune cells in various ways. (a) Tumour‐associated macrophages (TAMs) are mainly generated from bone marrow‐derived monocytes or tissue‐resident macrophages and are tumour‐promoting by participating in tumour angiogenesis and metastasis. TAMs are highly heterogeneity and recently identified as a spectrum concept for macrophages activation based on the stimuli and secreted cytokines and chemokines. M1 macrophages, stimulated by LPS and interferon gamma, are identified by their production of high levels of pro‐inflammatory cytokines and promotion of the Th1 response in the adaptive immune response. M2 macrophages are activated by IL‐4 or IL‐13 ligands and notably polarized into M2a , M2b , M2c (phagocytosis of apoptotic cells), M2d  subtypes, which are response for tumour progression and the main population of macrophages in TME.+CD25highFOXP3+, are induced from traditional T cells trough TGFβ1 and IL‐10 signalling by TAMs, RegDCs and other non‐immune cells. NK cells and cytotoxic T Lymphocytes (CTLs) play a key role for elimination of malignant tumour. The two cell types show metabolic interdependencies with tumour cells, which causing metabolic competition, nutrient restriction and immunosuppression.Adaptive immune cells in the TME are altered to promote tumour immune escape. The expression of the T‐cell receptor (TCR) and interleukin‐2 receptor (IL‐2R) are down‐regulated in otherwise immunocompetent CD8+ T cells, resulting in immune suppression. Stomal cells, such as BECs, pericytes, CAFs and MSCs, express PD‐L1, T‐cell immunoglobulin and mucin‐containing domain molecule 3 (TIM3) and CD95L  on their surface, and they secrete indoleamine 2,3‐dioxygenase (IDO), interleukin‐6 (IL‐6), prostaglandin E2 (PGE2) and transforming growth factor‐β (TGFβ) to induce exhausting and anergy of effector T cells.2.3.3+ T‐cell immune tolerance by up‐regulating the expression of programmed death‐ligand 1/2 and the CD28‐CTLA4 family receptor ligands B7‐H3 and B7‐H4.Tumour‐associated ECs include blood endothelial cells (BECs) and lymphatic endothelial cells (LECs). VEGFA in the microenvironment promotes the formation of new blood vessels from BECs, and VEGFC and VEGFD activate LECs to form new lymphatic vessels in the TME.2.3.4Pericytes differentiate from mesenchymal precursors and are recruited to tumours by platelet‐derived growth factor‐β (PDGFβ) gradients.2.3.5During the progression of carcinogenesis, MSCs are recruited to the TME by growth factors, cytokines and chemokines, such as IL‐6, IL‐1β, TGF‐β1, EGF, PDGF, TNF‐α and SDF‐1α, secreted by cancer cells and stomal cells.2.4Exosomes are secreted vesicle‐like membrane structures with a diameter between 30 and 100 nm. They carry a variety of proteins, lipids and nucleic acids, and contribute to intercellular communication.+CD49d+ TEXs are recruited, recognized and internalized by tumour‐associated ECs. They induce the expression of VEGF and other angiogenic proteins, such as CXCL5, MIF, and CCR1, by ECs and promote angiogenesis in the TME.+ liver cancer cells are rich in long non‐coding RNA (lncRNA) H19, which can up‐regulate the expression of VEGF, VEGF receptor and ICAM in vascular ECs, thereby promoting tumour angiogenesis and tumour cell adhesion and migration to the site of neovascularization. This finding suggests that lncRNA H19 may also be related to haematological metastasis of tumours.TEXs regulate tumour angiogenesis by activating ECs.Matrix enzymes in TEXs degrade normal ECM, promote matrix remodelling and create a pre‐metastasis microenvironment.TEXs disrupt the immune function of tumour patients and promote immune escape of tumour cells. They inhibit the anti‐tumour activities of monocytes, DCs, T cells, macrophages, and NK cells, and drive the development of MDSCs and Tregs to promote tumour immune tolerance.Manipulation of extracellular vesicles (EVs) to carry a desired cargo is a novel strategy for tumour therapy. EVs can be modified with specific receptors so as to target the cell/s of interest; this will pursue a long‐term content storage, virtue by no phenotypical alteration inside the TME. This approach is impressive and covers the current limitation for application of stem cells for cancer therapy due to encountering phenotypical alterations in the TME. Another impressive feature with EVs therapy is their stability and their capacity to cross biological barriers efficiently.3Various parts of the TME act as tumour hotbeds to promote their malignancy Figure . To aid 3.1CSCs are a relatively quiescent tumour cell subpopulation that undergoes active DNA repair, similar to normal adult stem cells. As the "foundation" of tumours, CSCs mediate therapy resistance and metastasis of tumours; they make a tumour "the endless weed under wildfire."Oxygen depletion by highly proliferative cancer cells and dysfunctional tumour angiogenesis leads to microenvironmental hypoxia. CSCs are concentrated in the hypoxic niche in which hypoxia and hypoxia‐inducible factors (HIFs), mediators of hypoxia, induce their resistance by promoting metabolic reprogramming, stem cell maintenance and tumorigenesis. Hypoxia and HIFs could maintain stemness of CSCs by induction of EMT, activation of stemness‐related signalling pathways, activation of stemness‐related genes , suppression of differentiation‐related genes and so on. CSCs in hypoxic TME uptake more glucose and reprogramme their metabolic mechanism from oxidative phosphorylation to glycolysis, which is responsible for resistance to radiation and chemotherapy and promoting DNA repair.The cellular components in the TME also play an important role in protecting CSCs. CAFs maintain the stemness of CSCs by secreting factors such as HGF and promoting EMT.However, according to the important role of tumour stem cells in tumour progression and drug resistance, targeting CSCs in TME could become a promising treatment of cancers.3.2Metastasis is a sign of tumour deterioration and progression, and a leading cause of cancer death. Tumour metastasis is multistep process regulated by many factors, but EMT is considered the initial step.EMT occurs when epithelial cells lose their unique polarity, adhesion ability, and surface expression of E‐cadherin and β‐catenin, then assume the morphology of mesenchymal cells and express mesenchymal cell markers such as α‐SMA, vimentin, N‐cadherin and SNAIL.The TME plays an important role in promoting EMT. TGFβ, EGF, VEGF, HGF and MMP family members in the TME comprise a complex network that promotes EMT. Among those factors, TGFβ is the key EMT‐promoting molecule; it regulates EMT through SMAD‐dependent or independent pathways. In the SMAD‐dependent pathway, TGFβ binds to receptors in the tumour cell membrane; they phosphorylate SMAD family members and promote SMAD2/3 dimer formation. The dimers are transported into the nucleus with the help of activated SMAD4, where they activate a series of EMT transcription factors such as SNAIL, TWIST, vimentin, fibronectin and ZEB.Cells in the TME, especially CAFs, are the main sources of EMT‐promoting molecules. CAFs participate in EMT and tumour metastasis by: (a) secreting paracrine EMT‐related factors,However, the current understanding of cellular phenotypical modification during cancer metastasis is acquiring a partial EMT, namely a hybrid E/M phenotype. This indicates that tumours cells can represent mesenchymal phenotype while simultaneously exhibiting epithelial potentials. This hybrid phenotype can be seen in collective invasion of tumour cells, and it accounts for the greatest capacity to pursue tumour metastasis.4Isolation of tumour cells by the TME results in low treatment efficiencies, low clinical response rates and drug resistance.4.1Understanding the major events occurring in the TME that support primary tumour growth and how these events impact the modulation of the environment is of utmost relevance to assist the definition of efficient therapy strategies. A good deal of current strategies was used to target TME components.For targeting tumour‐associated ECM, the inhibition of the TGF‐β signalling pathway mediated by Losartan and its analogs results in a reduced secretion of collagen I and consequently reduced ECM stiffness. However, drugs targeting MMPs, include JNJ0966, highly selective towards MMP‐9, and the antibody Fab 3369 that targets MMP‐14, have been developed. Among them, incyclinide went through several clinical trials for advanced carcinomas .4.2Even though targeting molecules inhibit tumour to a great extent, there is still some insurmountable difficulties. For example, agents that degrade and/or deconstruct ECM must be used carefully, since they may induce metastasis instead of avoiding tumour progression. We boldly assume that cell therapies may solve some of the problems of targeting drugs attributing to cell's natural advantages. (a) The cell itself has good plasticity, which provides the possibility of acting on multiple TME components at the same time to fully degrade TME, which can effectively prevent the compensatory regeneration and protection of other components when simply targeting a certain part; (b) the cell has homing properties. The "localization system" of the treatment cells has the tendency of tumour tissue and homing under the action of cytokines and chemokines, which greatly avoids the systemic side effects brought by non‐selective drugs; (c) cell has high permeability. Due to tumour stiffness and abnormal angiogenesis, the drug's permeability to TME is poor, but cells can effectively enter TME through deformation movements, secretion of matrix‐regulating enzymes, etc; (d) many cells have the ability to regulate normal tissue regeneration and repair, which can correctly repair tissue damage caused by chemotherapy drugs and avoid the adverse impact of bystander effects on normal cells Figure .4.2.1The TME regulates the differentiation and activation of immune cells to reduce their ability to recognize and eliminate tumour cells. Immune cell therapies that more accurately target tumour cells while restoring normal immune cell populations within the TME, enhancing normal immune cell function and disrupting the protective effects of the abnormal immune cell networks have become a new anti‐tumour option.A CAR is a biological element that is transgenically expressed on the surface of an immune cell membrane where it recognizes specific antigens. The extracellular structure of a CAR is a variable single‐chain fragment that recognizes a specific antigen. The intra‐membrane structure comprises a signal transduction domain; the earliest CARs used a single CD3ζ domain, but successive generations have included different numbers and types of co‐stimulatory domains to improve the cell proliferation, cytokine secretion and killing capacities of CAR‐equipped immune cells.CAR‐T cells are currently the most mature anti‐tumour immune cells. Among these, CAR‐T cells targeting CD19 have been the most successful in the clinic. A clinical trial at Memorial Sloan‐Kettering Cancer Center in 2016 (NCT01044069) showed that 91% of 32 B cell‐derived acute lymphoblastic leukaemia (B‐ALL) patients treated with CD19‐targeted CAR‐T cells achieved complete remission. Another clinical trial (NCT01626495) demonstrated that 30 patients with B‐ALL had a complete response rate of 90% after CD19 CAR‐T‐cell therapy. In a National Institutes of Health clinical trial in 21 children and adults with ALL (NCT01593696), a complete response rate of up to 70% has been reported. CD19‐specific CAR‐T cells also show limited therapeutic effects on other haematologic malignancies such as chronic lymphocytic leukaemia, lymphoma and multiple myeloma (NCT02135406); however, the response rates of these diseases are lower than that of B‐ALL.In addition to CAR‐modified T cells, CAR‐NK cells have achieved a high response rate in the treatment of myelodysplastic syndrome and acute myeloid leukaemia (AML). CAR‐NK cells for the treatment of B‐cell lymphomas (NCT01974479 and NCT03056339) and metastatic solid tumours (NCT03415100) are in clinical trials; however, they face problems such as low CAR transfection efficiency and short in vivo survival time.Exosomes secreted by CAR cells have been confirmed to carry the CAR structure. They maintain the original targeting characteristics of the parent cells in the context of lower off‐target cytotoxicity, with high efficacy and safety in preclinical experiments.4.2.2The origins of many tumours are associated with the dysplasia, depletion, and dysregulated proliferation and differentiation of normal stem cells. The existence of a normal stem cell microenvironment has become a prognostic indicator for tumour treatment.Haematopoietic stem cell transplantation (HSCT) is to completely remove the abnormal bone marrow haematopoietic stem cell (HSC) microenvironment by means of radiotherapy and chemotherapy, and then rebuild the haematopoietic microenvironment and restore the normal haematopoietic function by transplanting normal stem cells. As the only curative treatment for malignant and non‐malignant diseases of the haematopoietic system, HSCT has developed rapidly in the past 25 years.HSCT is becoming increasingly prominent in the treatment of some solid tumours, especially refractory tumours.4.2.3MSCs are non‐haematopoietic stem cells primarily found in the bone marrow. They can differentiate into adipose tissue, bone and cartilage under the appropriate conditions.The reported effects of natural MSCs on tumours have been contradictory. Some studies have shown that MSCs have anti‐tumour effects, such as inhibiting tumour cell proliferation and causing cell cycle arrest in leukaemia, bladder cancer, renal cell carcinoma, breast cancer and other tumours.MSC exosomes, engineered MSC nanoparticles (nanoghosts), and other microvesicles have been used as substitutes for MSCs due to their similar compositions and better safety profiles. Exosomes derived from MSCs can inhibit the proliferation of various tumour cells.Researchers are increasingly modifying MSCs to optimize their anti‐tumour function in 2 ways. First, they are enhancing the synthesis and release of endogenous and exogenous anti‐tumour factors from MSCs. Second, they are strengthening their homing to the TME and prolonging their activity there to achieve more efficient TME penetration and anti‐tumour effects.In order to ensure the tumour‐killing activity of MSCs and prevent them from promoting tumours, many researchers have enhanced MSC synthesis and release of IFN‐γ, which inhibits tumour cell proliferation, and tumour necrosis factor‐related apoptosis‐inducing ligand, which promotes apoptosis.In order to enhance the in vivo activity of MSCs, researchers have encapsulated MSCs with synthetic biodegradable ECM and implanted them in the brains of mice with glioblastoma. This approach resulted in dramatic shrinkage of the tumour and ensured the long‐term biological activity of the MSCs.However, MSCs remain a double‐edged sword. The stronger the effects of MSCs as drug carriers that penetrate the TME to kill tumours, the more serious the side effects caused by the drugs' non‐selective destruction of normal cells. The current drawbacks associated with MSC therapies have prompted researchers to seek safer, more accurate anti‐tumour treatment methods.4.2.4The early embryonic microenvironment has the powerful ability to repair erroneous genetic material and inhibit oncogene expression; thus, embryonic cells have innate tumour immunity characteristics. An increasing amount of research is being devoted to applying this tumour‐hostile microenvironment to tumour therapy.The embryonic microenvironment can reverse tumour fate. Lee et alIn order to apply the powerful tumour‐reverting effects of the in situ embryonic microenvironment to the practical treatment of tumours, researchers have cultured embryonic stem cells (ESCs) in vitro to establish an ESC microenvironment (ESCM) that mimics that found in embryos with remarkable results. Zhou et al injected TK suicide gene‐transfected ESCs into mice with CML via the tail vein and found that the cells restored normal blood cell counts in the mice and prolonged their survival. By timely activation of the suicide gene, the differentiated ESCs could be eliminated to avoid teratoma formation and guarantee the safety of the recipients.Other research has confirmed the tumour‐reverting effects of the ESCM. For example, decellularized matrix components generated by 3‐dimensional ESC culture can reverse metastatic breast and prostate cancers.The mechanisms by which the ESCM reverts tumours are still not completely clear. Current studies suggest that tumour reversion may be related to the PI3K/AKT, STAT3, Notch1, and other pathways, and that exosomes derived from ESCs may play an important role in their signal transduction.5The TME can mediate tumour cell immune escape, promote CSCs formation and enhance tumour metastasis ability, thereby promoting tumorigenesis and development. The distinctive features of the TME, including the abnormal haemodynamics due to neovascularization, the ECM that is difficult to penetrate, and the hypoxic state that confers resistance to oxidative damage, play important roles in the drug resistance of tumour cells. Cell therapies utilizing engineered immune cells have shown good anti‐tumour effects, but poor curative effects for solid tumours. Furthermore, the main aim of this type of cell therapy is still to kill tumour cells. Thus, the body can develop resistance to the treatment, and it is impossible to avoid the complications caused by cell killing that are also associated with traditional therapy. Malignant tumour cells are derived from normal cells and develop in the TME. Suitable microenvironments such as the early embryonic environment can revert tumour cells into normal cells. HSCT, MSC transplantation, and ESC and other stem cell therapies can reach the interior of the TME due to their strong targeting and penetrating power. By reshaping the cellular components, ECM structure and soluble factor composition to comprehensively rebuild the TME, these stem cell therapies can reverse the malignant tumour phenotype while avoid cell killing and reducing treatment‐related harm to patients. Stem therapies could, therefore, represent the most promising new direction for tumour treatment. New breakthroughs in tumour treatment await in the near future based on combining approaches to complement the strengths of individual cell therapies, optimizing the favourable characteristics of cells with engineered modifications to compensate for their shortcomings, or replacing source cells with cell‐specific microvesicles.The authors declare no conflict of interest.CYQ, SBW and JHL wrote the first draft of the manuscript. JL, TL, CYL, YRS and YRL wrote sections of the manuscript. CYQ, XS, QW participated in the discussion of the manuscript. CL, LY and ZCW provided critical revisions. All authors approved the final version of the manuscript for submission.


A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis.Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and KTwenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy.We found statistically significant correlations between functional imaging parameters and key molecular cancer markers. Numerous IHC biomarkers associated with hallmarks of cancer and prognosis have been identified. The epidermal growth factor receptor (EGFR) and Ki-67 are biomarkers associated with proliferation, the glucose transporter-1 protein (GLUT1) is associated with glucose metabolism,6 the biomarker p53 and Bcl-2 are associated with apoptosis, whereas the vascular endothelial growth factor (VEGF) is associated with angiogenesis and the biomarker carbonic anhydrase IX (CAIX) is associated with hypoxia.7 Evasion of immune destructing has been recognised as a new hallmark of cancer, and the IHC biomarker programmed death ligand 1 (PD-L1) is involved in immune escape pathways.9 All cancer-related IHC biomarkers rely on invasive tumour biopsies and in clinically practice usually on single-tumour biopsies. However, single-tumour biopsies cannot detect inter or intratumoural heterogeneity, and it is not realistic to biopsy the entire tumours to access heterogeneity nor to biopsy all lesions within a patient to assess intertumoural heterogeneity. Non-invasive modalities such as multiparametric functional imaging with positron emission tomography/magnetic resonance imaging (PET/MRI) can simultaneously provide information on FDG uptake, diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE).12 FDG is a glucose analogue, and FDG uptake is influenced by different mechanisms, but in general FDG uptake is thought of as a surrogate for glucose metabolism.13 DWI images diffusion of water and is generally thought of as a surrogate for cellularity,14 and DCE assesses microvascular parameters and is thought of as a surrogate for perfusion or hypoxia.16 As such, PET/MRI may enable imaging of several important hallmarks of cancer18 simultaneously and previous studies have investigated the association between cancer-related IHC biomarkers from single-tumour biopsies and imaging features.20 However, the association between IHC biomarkers and imaging has been limited to comparisons between patients, and association between 3D functional imaging and corresponding IHC samples within patients are lacking. Ideally, the considerable intratumoural and intertumoural variability detected on functional imaging23 may complement the information from single-tumour biopsies for understanding variations across the tumour and potentially provide guidance for a suitable biopsy site. To our knowledge, no previous studies have investigated if functional imaging depicts or correlates with the spatial variability of cancer-related IHC biomarker expression in HNSCC. If a spatial correlation between functional imaging and molecular biology can be established, it will have an impact on both diagnostic methods and treatment possibilities. The purpose of this study is to investigate if preoperative functional imaging spatially correlates with IHC variability in surgical specimens.Head and neck squamous cell carcinoma (HNSCC) is the most frequent malignancy in the head and neck area, and constitutes a heterogeneous disease both in regards to prognosis and tumour biology.Preoperative PET/MRI scan of the patientSurgery and annotation of anatomical landmarksFormalin fixation and 3D MRI scan of specimenCo-registration of specimen and patient scan based on anatomical landmarksSpecimen sectioned contiguously in tumour blocks.Six random tumour blocks selected for core biopsies and IHC stainingCorrelation between IHC biomarker expression and FDG uptake, diffusion and perfusion.Thirty-one patients with HNSCC referred for surgery were included in this prospective study. All patients had histologically proven squamous cell carcinoma, and were referred to curative treatment with surgery for recurrent or primary HNSCC. Patients with MRI contraindications  and clinically assessed tumours <1.5 cm or lymph-node metastasis <1 cm were excluded upfront. The study was approved by the local ethics committee approval number H-16049387, and the protocol published on clinicaltrials.gov (ID number NCT03160495). The workflow is explained in detail in the following list and Fig. 3. T1-weighted MRI after gadolinium contrast administration  and T2-weighted MRI were performed for anatomic localisation of the lesion, and morphometric imaging was performed with a 3D T2-weighted SPACE sequence with voxel size = 1.0 × 1.0 × 1.0 mm3. Two diffusion-weighted imaging (DWI) acquisitions were performed, one ‘standard’ DWI with a voxel size = 2.7 × 2.7 × 4.0 mm3, which has previously been described,23 and an additional DWI with a RESOLVE24 sequence and voxel size = 1.1 × 1.1 × 4.0 mm3. Dynamic contrast-enhanced (DCE) perfusion imaging utilised a 3D T1-weighted VIBE sequence with voxel size = 1.9 × 1.4 × 3.6 mm3. DCE images were analysed using Tissue 4D package (Siemens), which uses the model by Tofts et al.25 Figure 26 DWI was quantified using apparent diffusion coefficient (ADC),14 and DCE MRI was quantified using contrast transfer coefficient (Ktrans).27Patients were scanned on an integrated PET/MRI system (Siemens Biograph mMR) with a 3-T magnet using a head and neck coil. A detailed description of the imaging protocols can be seen in the Supplementary Material. Briefly, patients were instructed to fast for 6 h prior to the scan, and were not allowed to talk from 15 min before FDG injection until the scan was performed. Patients were scanned 60 min after FDG injection (4 MBq/kg). The reconstructed PET voxel size was 2.1 × 2.1 × 2.0 mmen bloc, and anatomical landmarks were marked (by J.H.R.) per operatively in collaboration with the surgeon. The anatomical landmarks consisted of the bone, teeth, large vessels or other soft tissue structures easily identified on the preoperative patient scan. After tumour resection, the specimens were immediately fixed to cork plates with intravenous plastic cannula, and if the anatomical landmarks included soft tissue they were marked with intravenous cannula as well to identify them on the subsequent specimen MRI scan. The orientation of the tumour and the marking of anatomical landmarks were performed in collaboration and in consensus with the surgeon in the operating theatre.All patients had their tumours removed 3 in a knee coil. The specimen was scanned to provide high-resolution MRI that could serve as an intermediate registration step between the patient scan (prior to surgery) and the digitalised pathology. Hence, patient MRI was registered to specimen MRI, and specimen MRI was registered to digitalised pathology. The specimens were fixated to cork plates during the formaldehyde fixation to minimise deformation in this process. The SPACE resolution employed on the specimen was enabled by the increase in signal-to-noise ratio of the knee coil.The specimen was fixed in 10% formaldehyde for 24 h, and scanned on the same PET/MRI system as the patient scan. Morphometric imaging of the specimen was acquired with a 3D T2-weighted SPACE sequence similar to the in vivo sequence, but with voxel size = 0.5 × 0.5 × 0.5 mmThe patient PET/MRI images were co-registered to the pathologic specimens by rigidly co-registering the 3D anatomical MRI scan of the specimen with high-resolution isotropic voxels after fixation to the patient scan in a two-step process. The first step was performed in the program register in the MINC Tool kit , and included a point-by-point based alignment of the anatomical landmarks  between the patient SPACE scan and the specimen scan. The second step was performed in the software package Mirada XD General Oncology review  that provides means of defining a transformation that relies on the visual examination of the registration. Examples are given in the Supplement Material   Fig. , and eacit) Fig. . The digSix tumour blocks from each specimen were selected for further analysis. One block was selected from the area with the highest FDG uptake, and five blocks were selected at random within the tumour area as described in the Supplementary Material. A 3-mm wide core biopsy was performed and used to construct tissue microarray (TMA) blocks. In all, 4-µm sections from each TMA block were stained with H&E. The core biopsies were repeated up to three times if they failed due to technical reasons in the preparation of the TMA blocks. Importantly, the position of the core biopsy is known, but randomly chosen to ensure an appropriate variation across the tumour. Using the co-registration, the IHC biomarker expression can be spatially correlated to the imaging biomarkers.28 The specific antibodies are described in the Supplementary Material. Appropriate positive controls were used, and negative control sections were incubated identically, except for the primary antibody, which was replaced by normal rabbit serum/mouse IgG. All TMA blocks were scanned at 40 × (0.23 µm/pixel) resolution with NanoZoomer XR (Hamamatsu) slide scanner.Immunohistochemistry was performed on the Ventana Benchmark Ultra , Dako Omnis  and Autostainer Link 48  platforms. The following IHC biomarkers were used: p40, p53, EGFR, Ki-67, Glut1, VEGF, Bcl-2, CAIX and PD-L1. The specific selection of IHC biomarkers was predefined and based on their association with hallmarks of cancer , radiotherapy resistance (CAIX and EGFR) or FDG accumulation (GLUT1). P40 is a marker for squamous cell carcinoma and used with H&E in assessment of tumour cells and to estimate the tumour cell count (TCC) in each core biopsy using the Open Source Digital Pathology software program QuPath.Percentage of tumour in the core biopsy was assessed to evaluate the quality of the core, and percentage of vital tumour cells (VTC) compared with all cells in the core was reported as well as TCC. All IHC stains were scored manually as tumour proportion score (TPS) i.e., percentage of positive tumour cells in the core biopsy. Any expected  cell staining was visually detected, and intensity stronger than background was registered as positive. The pathologist was blinded to the information on the core biopsies  during the assessment process.A region of interest (ROI) corresponding to the site and size of the core biopsy (3-mm diameter) was used to extract quantitative parameters from the three imaging modalities. The 3-mm ROI corresponds to approximately one imaging voxel, and to address a potential concern that this voxel size can be sensitive to noise, analyses were also performed by dilating the original 3-mm ROI in all dimensions to get twice the volume.29 the partial correlations are univariate with a random offset for each lesion. Confidence intervals were constructed using Bootstrapping. A Bonferroni correction was used to allow for multiple comparisons. All statistical analyses were performed in the statistical software R (version 3.6.0) and a p-value<0.05 was considered statistically significant. It is of clinical relevance to know if an image-guided biopsy is more representative than a random biopsy. It is nontrivial, however, to define ‘more representative' for most of the markers studied. But two scenarios are particularly interesting: (1) PD-L1 expression is used to guide the prescription of immune checkpoint inhibitors, although the cut-off values are a matter of discussion.30 We investigated if the highest PD-L1 expression would more likely be observed with an FDG-guided tumour biopsy than a random biopsy in the tumour; (2) similarly, for VTC we tested if the core biopsy with highest FDG uptake was more likely to harbour the highest VTC proportion than a random core biopsy. Binomial statistics was used for these analyses. Due to ties among the PD-L1 or VTC scores, the random biopsy had a higher than 1/6 chance to hit the highest PD-L1/VTC uptake, so the null hypothesis was adjusted to the random probability accounting for ties.Partial correlation was used to analyse the association between pathology assessed with IHC biomarkers and multiparametric imaging assessed with FDG-PET, DWI and DCE using the package ppcor of R (version 3.6.0),In total, 31 patients were included in the study, but the data from three patients had to be excluded. The first due to technical reasons, and the second patient withdrew the consent. The third patient was excluded from the analysis because the specimen fragmented during the histological processing making it impossible to fuse the digitised pathology with imaging. Twenty-eight patients with a total of 33 lesions were available for further analysis. Two patients had five tumour blocks, and one patient only had four tumour blocks yielding 194 tumour blocks available for core biopsies. Two core biopsies were not accessible, despite repeated attempts leaving 192 core biopsies eligible for full analysis. One scan was interrupted on patient request, and only anatomical and PET imaging was available for this patient (two lesions), and in one scan, the entire lymph-node metastasis was not included in the field of view in the DWI scan, leaving 175 core biopsies available for the correlation analysis between the IHC biomarkers and the DWI scans. In four lesions, the DCE scan failed and could not be recovered, leaving 159 core biopsies available for correlation analysis between IHC biomarkers and the DCE scans. Patient and tumour characteristics can be seen in Table 3 in average (range 1.2–8.2).Figure mean and ADCmean were used in the correlation analyses. For DCE perfusion, the most frequent measure reported in the literature is the transfer coefficient Ktrans15, and this was used in the subsequent correlation analyses.Figure Figure Figure trans correlated positively with p53. However, despite being significant, all correlations were weak at best. After correction for multiple comparisons with the Bonferroni correction, the correlation between FDG uptake and VTC was significant as were the correlations between diffusion (ADC) and Ki-67, PD-L1 and Bcl-2.Table Correlation plots for the significant correlations, in order of PET-positive tumour volume, can be assessed in Supplementary Fig. trans did not correlate significantly with PD-L1. The results can be seen in the Supplementary Material Table The sensitivity analysis using the larger ROI with twice the volume of the 3-mm ROI showed the same results for FDG uptake (SUV) and diffusion of water (ADC), but Kp = 0.72). For VTC, 10 of the 33  lesions with highest FDG uptake had highest VTC expression compared with 25% with a random biopsy (p = 0.55).We tested whether the core biopsy with highest FDG uptake was more likely to have the highest PD-L1 expression or highest VTC compared with a random biopsy. In 14 of the 33 lesions , the core biopsy with highest FDG uptake had the highest PD-L1 expression compared with 39% when the biopsy was performed random  can be assessed, and cellular viability is required for the expression of CAIX or any of the ICH biomarkers investigated here. On the other hand, all core biopsies were performed from areas with representative tumour tissue. The significant correlations between FDG uptake and percentage of VTC and tumour cell count emphasise that although FDG is a broad tracer for glucose metabolism it does seem to reflect tumour burden, metabolism and is associated with hypoxia (CAIX). Besides surgery, radiotherapy is an important treatment modality for HNSCC patients; however, hypoxic tumours are more resistant to radiotherapy. A correlation between a non-invasive hypoxic imaging modality and a biologic hypoxic biomarker such as CAIX could be used both as a possible prognosticator and as a predictor for early treatment response during radiotherapy.38 The biomarker p53 was heterogeneously expressed  have more capillary permeability (high Ktrans). The proliferation IHC marker Ki-67 correlated negatively with ADC and illustrates that areas with low diffusion of water have a higher proliferation index compared with areas with high diffusion, likely reflecting the increased cellularity resulting from high proliferation  and the negative spatial correlation between Bcl-2 and ADC. However, Bcl-2 was homogenously expressed in all cores  on one hand, and ADC  on the other is interesting, but suggests that the two imaging modalities may convey complementary tissue characterisation, leading to the hypothesis that FDG describes the metabolism and tumour microenvironment (hypoxia), whereas ADC seems to correlate with more specific malignant characteristics. In this study, it is not evident that DCE provides any additional information not available from FDG-PET or DWI.The negative spatial correlation between the immune checkpoint inhibitor PD-L1 and ADC and positive spatial correlation between PD-L1 and KThe prospective design, logistic setup with 3D-morphometric MRI of the specimen on the same scanner, the repeated results from the sensitivity analysis with different ROI sizes and the relatively large patient population with multiple biopsies are strengths in this study. Both patients with newly diagnosed disease and patients with recurrence were included, and the patients had different tumour subsites within the head and neck region. This diversity was obviously a study design choice to allow sufficient accrual, but it is also a weakness, as site-specific correlations could be speculated to exist, but vanish in the diversity of patients. However, as demonstrated by the weak correlations and observed confidence intervals evident from Table In conclusion, the statistically significant spatial correlations between functional imaging parameters and key molecular cancer markers is encouraging. However, the modest correlation challenges the direct clinical applicability and it indicates that further efforts are needed to elucidate exactly how functional imaging parameters can be used to quantify intratumoural heterogeneity.Supplemental material


Inflammation caused by the hyper-activation of innate immunity due to genetic factors occasionally leads to inflammatory keratinization diseases of the skin. Such inflammatory keratinization diseases with genetic autoinflammatory pathomechanisms are called “autoinflammatory keratinization diseases” (AiKDs) . AiKDs aIL36RN mutations , pityriasis rubra pilaris (PRP), and familial keratosis lichenoides chronica (KLC) , 2. It rutations . GPP shoentified . This obenotypes . IL36RN equently . In addiequently  reported present . Overall present . Antigen alleles . In factCARD14 pathway and the IL36RN pathway are associated not only with GPP, but also with psoriasis vulgaris. GPP and psoriasis vulgaris are thought to belong to the same disease entity on a spectrum. Indeed, CARD14 mutations are associated with both psoriasis vulgaris and GPP, with the strongest CARD14 activity causing GPP   . The AP1 (AP1S3) . In thisNCSTN, PSENEN, and PSEN1 coding the γ-secretase complex show hyperkeratotic hair follicle epithelia in the infundibular region, resulting in the occlusion of hair follicles. Such occlusion is considered to be an essential part of the pathogenic mechanism behind HS  encoding the γ-secretase complex have been reported in HS , especially familial cases, be included in AiKDs. Mutations in the γ-secretase complex genes have been identified in patients and families with HS . HS patiehind HS . The pluehind HS . Furtherehind HS , 24. It ehind HS –29. HS aehind HS , 27. In ed in HS . γ-secreed in HS . Notch ied in HS . Thus, rdel mice .MVK, one of the mevalonate pathway genes, were detected as a cause of disseminated superficial actinic porokeratosis  have been identified as causative of porokeratosis (MVK, is also causative of hyperimmunoglobulinemia D and periodic fever syndrome, an established autoinflammatory syndrome (MVK cause the more severe, systemic disease, hyperimmunoglobulinemia D and periodic fever syndrome, and heterozygous mutations in MVK lead to porokeratosis (Porokeratosis encompasses diverse hyperkeratotic lesions consisting of single or multiple atrophic macules or plaques demarcated by hyperkeratotic ridges. Vertical columns of parakeratotic cells called “cornoid lamellae” are histological findings characteristic of porokeratosis . Porokereratosis . To dateeratosis . The meveratosis . Isopreneratosis . Isopreneratosis . It was eratosis . We speceratosis . Indeed,syndrome . Bialleleratosis .Porokeratosis patients with causative mevalonate pathway gene mutations have the mutations as heterozygous germline mutations. In skin lesions of porokeratosis, the expression of mutant alleles is higher than that of wild-type alleles, and the predominant expression of mutant alleles is considered to be a trigger for the formation of porokeratotic lesions . As a caConcerning treatments for AiKDs, based on the autoinflammatory pathogenic mechanisms mentioned above, therapeutic strategies targeting molecules working in autoinflammatory cascades of pathomechanisms in AiKDs, such as cytokines, their receptors, and signal molecules, are expected to bring promising innovative therapies for various diseases included in AiKDs. Indeed, various treatments against pathogenic mechanisms are sufficiently effective for a number of AiKDs.CARD14 mutations (NLRP1 mutations, the IL-1 receptor antagonist anakinra was reported to be effective in cases of NLRP1-associated autoinflammation with arthritis and dyskeratosis (Successful treatments with granulocyte and monocyte adsorption apheresis , anakinrutations . Concerneratosis . As for eratosis .The concept of AiKDs has ushered in a new era in inflammatory keratinization disorders. We assume that, in the near future, a growing number of inflammatory keratinization diseases may be recognized to be AiKDs, along with an advanced knowledge of novel pathomechanisms working in inflammatory keratinization disorders. The recognition of innate causative/predisposing issues and the precise assessment of their roles in disease etiology from the perspective of AiKDs promise to bring innovations that will afford more precise, targeted, causal therapies for various AiKDs.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Schistosoma japonicum (S. japonicum) eggs can limit the progression of liver fibrosis. However, the detailed molecular mechanisms are yet unclear. Extracellular vesicles (EVs) contain a selection of miRNAs for long-distance exchange of information and act as an important pathway for host-parasite communication. This study aimed to explore the potential role of S. japonicum egg-derived EVs and its key miRNA in liver fibrosis. Herein, we found that S. japonicum egg-derived EVs can inhibit the activation of hepatic stellate cells, which is mediated via the high expression of Sja-miR-71a. Sja-miR-71a in EVs attenuates the pathological progression and liver fibrosis in S. japonicum infection. Sja-miR-71a inhibiting TGF-β1/SMAD and interleukin (IL)-13/STAT6 pathways via directly targeting semaphorin 4D (Sema4D). In addition, Sja-miR-71a can also suppress liver fibrosis by regulating Th1/Th2/Th17 and Treg balance. This study contributes to further understanding of the molecular mechanisms underlying Schistosoma-host interactions, and Sema4D may be a potential target for schistosomiasis liver fibrosis treatment.Schistosomiasis is characterized by liver fibrosis, and studies have indicated that  Schistosoma species and is a serious parasitic disease throughout the world’s tropical regions. Schistosoma japonicum (S. japonicum) is widely distributed in the Philippines, Indonesia and the South of China [S. japonicum worm produces numerous eggs that are transported to the liver. Subsequently, eggs present in the liver elicit host immune responses including granulomatous inflammation and fibrotic reactions. Schistosomiasis is characterized by liver fibrosis and the host’s fibrotic reactions can accelerate the death of the eggs that eventually calcify. Recent studies have shown that S. japonicum and Schistosoma mansoni (S. mansoni) eggs suppress the activation of hepatic stellate cells (HSCs) and lead to the transcriptional downregulation of fibrosis-associated genes [S. japonicum eggs can inhibit HSC activation, induce senescence of activated HSCs and facilitate HSCs apoptosis to control the progression of hepatic fibrosis [Schistosomiasis is caused by of China . The femed genes ,3. Consifibrosis –6. HowevS. japonicum worms mediate M1 type immune-activity of macrophages [S. japonicum and S. mansoni worms may play important roles in Schistosoma–host interactions [S. japonicum [S. mansoni worms derived EVs enclosed miRNAs modulate host T helper cell differentiation [Extracellular vesicles (EVs) contain large quantities of a selection of proteins, lipids and nucleic acids for long-distance exchange of information ,8. EVs srophages . Subsequractions –19. Zhu aponicum . Meninghntiation . Among tntiation ,23.S. japonicum infection [S. japonicum in liver fibrosis.miRNAs are small non-coding RNAs that negatively regulate gene expression at the post-transcriptional or translational level via complementary binding to the 3′-untranslated region (UTR) –26. Recenfection –29. HoweS. japonicum egg-derived EVs and its key miRNA in liver fibrosis to further understand the molecular mechanisms underlying Schistosoma–host interactions. Interestingly, in this study, we showed that S. japonicum eggs secreted EVs that can inhibit liver fibrosis in S. japonicum infection via high expression of Sja-miR-71a.Therefore, we aimed to analyse the role of S. japonicum cercariae that were shed from lab-infected snails (Oncomelania hupensis), obtained from the National Institute of Parasitic Disease, Chinese Centre for Disease Control and Prevention. All animal experiments were approved by the Sun Yat-sen University Committee for Animal Research and conformed to the Guidelines for the Care and Use of Laboratory Animals of the National Institute of Health in China.Male BALB/c mice were purchased from Guangdong Medical Laboratory Animal Centre. To induce infection, mice were exposed percutaneously to S. japonicum and healthy controls in Nanchang, Jiangxi, China. All human serum was obtained with informed consent and approved by the Ethics Committee of Zhongshan School of Medicine, Sun Yat-sen University (2019 no. 54).All human serum came from patients infected with S. japonicum EVs, S. japonicum eggs and worms were collected from infected mice at 45 days post-infection. EVs in eggs were analysed by transmission electron microscopy. EVs were purified by differential centrifugation and following validation by electron microscopy and Nanoparticle Tracking Analyses (NTA). For additional details, see supplementary information.To prepare S. japonicum egg-derived EVs and S. japonicum adult-derived EVs were stained with DiR . Mice were injected via the tail vein with DiR-labelled EVs. Anaesthetized mice were placed into the In-Vivo Imaging System  at 2, 4, 6, 8, 24, and 48 h after EVs injection. At the end of the last total body scan 48 h post-injection, mice were euthanized, and organs were harvested and placed into the In-Vivo Imaging System for ex vivo imaging.S. japonicum egg-derived EVs for 1 h, and a confocal analysis was carried out to evaluate EVs internalization. DAPI was used to detect nuclei, Alexa Fluor Phalloidin-FITC was used to label actin.The human liver stellate cell line (LX2)  was incubated with PKH26-labelled For EVs and Recombinant Human Semaphorin 4D (Sema4D)  treatment, LX2 cells were exposed to recombinant TGF-β1  or EVs. For miRNA mimic and siRNA treatment, cells were incubated in serum-free medium for starvation overnight. Cells were then stimulated with miRNA mimic and siRNA using RNAiMAX . For additional details, see supplementary information.−ΔΔCT method. For additional details, see supplementary information.Total RNA was harvested using TRIzol according to manufacturer’s instructions. The expression of target mRNA and miRNA was determined using the SYBR Green Master Mix kit . GAPDH, β-actin or U6 snRNA was used as an internal control, and the fold change was calculated by the 2Total RNA was isolated from EVs and subjected to quantitative and qualitative analyses to ensure the use of qualified samples for sequencing and analysis. For additional details, see supplementary information.Liver tissues and LX2 cells were homogenized with RIPA lysis buffer in the presence of freshly added protease and phosphatase inhibitors . Lysates were subjected to 10% sodium dodecyl-polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride blotting membrane. The membranes were then immunoblotted with antibodies. For additional details, see supplementary information.Liver tissues from the infected mice were weighed and digested overnight in 4% potassium hydroxide, released eggs were counted under a dissecting microscope. The index of liver and spleen was calculated based on the following formula: total weight of mouse liver or spleen (mg)/total weight of mouse body (g). The area of egg granulomas was measured based on H&E-stained sections by using the Image-Pro Plus 6.0 software. The area of each granuloma in section was measured. Fibrosis was observed using liver sections stained with Masson’s trichrome staining, and the integrated optical density (IOD) was analysed by Image-Pro Plus 6.0. Serum alanine aminotransferase (ALT) levels were detected by KingMed Diagnostics .9 genome copies/200 µl). Ninety-six hours after transduction Sja-miR-71a levels were quantified by qRT-PCR. For mice transduction, mice were injected via the tail vein with rAAV (1.5 × 1011 v.g./mouse) 10 days after infection with S. japonicum. The green fluorescent protein (GFP) expression of rAAV in mice was observed using in vivo imaging system and fluorescence microscopy.HBAAV2/9-Sja-miR-71a-GFP and HBAAV2/9-GFP viral particles were purchased from Hanbio Biotechnology Co., Ltd. LX2 cells were transfected with rAAV , IL-4 and IL-17A antibodies. For additional details, see supplementary information.Renilla luciferase activity.Cells were transfected with the Sema4D UTR reporter plasmid together with Sja-miR-71a mimic or control mimic for 48 h using RNAiMAX . The Luciferase Assay Reagent  was added to the cells, and luciferase activities were detected using the Infinite F500 Multimarker Analyser  according to the manufacturer’s instructions. Luciferase activity was normalized to t-test. Multiple comparisons between more than two groups were analysed by one-way ANOVA test or Kruskal–Wallis test (non-parametric). The value of P < 0.05 was considered statistically significant.Results are expressed as mean ± SD values. The data of both groups were analysed using unpaired two-sample S. japonicum eggs release EVs, we first conducted electron microscopy studies and identified the presence of EVs in S. japonicum eggs rphology , arrows)rphology ).Figure S. japonicum egg-derived EVs on S. japonicum–host interactions, we evaluated whether S. japonicum egg-derived EVs can reach the host organs to perform their functions. Our data showed that S. japonicum egg-derived EVs can reach host organs including the heart, brain, liver, spleen, kidney, lung and thymus ). We hypothesized that the eggs located in the centre of the granuloma secrete EVs enter the cytosol of HSCs and regulate activation of HSCs and thereby regulate hepatic fibrosis. As shown in S. japonicum egg-derived EVs can be internalized by HSC and reduced the α-SMA and Collagen I expression of TGF-β1-treated human LX2 cells , suggesS. japonicum egg-derived EVs, we assessed the expression profiles of miRNA in the S. japonicum egg-derived EVs. We compared the differences of miRNA expression profiles in S. japonicum egg-derived EVs and S. japonicum adult-derived EVs ). Among the miRNAs of S. japonicum egg-derived EVs, Sja-miR-71a was the most highly expressed, and its expression was 270 times greater than that in S. japonicum adult EVs , Supplementary Table 1).To identify the components responsible for the inhibit HSCs activation effect of S. japonicum spawn in large numbers) after infection ). We then determined Sja-miR-71a expression in serum EVs of patients with schistosomiasis japonicum. Interestingly, Sja-miR-71a expression can be detected in serum EVs of patients . Both S-miR-71a ,d). CollS. japonicum egg-derived EVs was involved in the progression of hepatic schistosomiasis, HBAAV2/9-Sja-miR-71a was successful constructed ). Mice were infected with S. japonicum and administered with the HBAAV2/9- Sja-miR-71a ). In addition, liver sections and qRT-PCR reflected the highly hepatic tropism of HBAAV2/9-Sja-miR-71a ). We observed that hepatosplenomegaly was markedly reduced after HBAAV2/9-Sja-miR-71a transfection, and the liver and spleen indices significantly reduced after HBAAV2/9-Sja-miR-71a treatment ).To investigate if Sja-miR-71a in S. japonicum infection.Interestingly, H&E staining of liver sections showed that compared with those in the control group, egg granulomas, per cent of egg granuloma areas, and area of a single granuloma in the HBAAV2/9-Sja-miR-71a group were significantly shrunk ,c). TherTo further evaluate the molecular mechanisms of Sja-miR-71a in liver fibrosis, we aimed to identify direct target genes that might be controlled by Sja-miR-71a expression. Three databases  were used to identify potential Sja-miR-71a targets. Venn diagram showing there are nine potential target genes overlap in three database predictions ). Among S. japonicum egg-derived EVs carried Sja-miR-71a to the HSCs, and TGF-β-mediated HSCs highly expressed Sema4D can be downregulated by S. japonicum egg-derived EVs ). Taken together, these experiments showed that Sema4D was markedly increased after infection with S. japonicum, and Sja-miR-71a could inhibit Sema4D expression by directly interacting with its 3′-UTR.To verify whether Sema4D is a direct target of Sja-miR-71a, we generated a luciferase reporter plasmid containing the 3′-UTR of Sema4D flanking the putative Sja-miR-71a binding sites. The dual-luciferase reporter assay revealed that compared to NC mimic-transfected cells, Sja-miR-71a significantly reduced the luciferase activity of the Sema4D construct ). We fouived EVs ). To furived EVs ,g). NextS. japonicum and could be reduced by treated with HBAAV2/9-Sja-miR-71a  revealed that Sema4D in HSCs was significantly increased in mice livers after infection with -miR-71a , arrows)-miR-71a , Supplem-miR-71a , arrows)-miR-71a ). We the-miR-71a ,f, α-SMAand CD72 ,f). Our −/− mouse model of experimental asthma [Sema4D can directly increase TGF-β1 expression, and IL-13 and TGF-β1 expression was decreased in a Sema4D l asthma ,32. In al asthma ,34. Thesin vitro. Interestingly, transfection with Sja-miR-71a mimic decreased the expression of TGF-β-mediated p-SMAD 2/3 and SMAD4 . Furtnd SMAD4 ,f). Thus+CD4+ cells around the granuloma of S. japonicum . Therefore, we assessed the changes in Th1, Th2, Th17 and Treg subset levels in livers and spleens by flow cytometry. We observed that the percentages of CD3e+CD4+IFN-γ+ (Th1), CD3e+CD4+IL-4+ (Th2) and CD3e+CD4+IL-17A+ (Th17) cells in the livers of HBAAV2/9-Sja-miR-71a-treated S. japonicum-infected mice were significantly lower (P < 0.05) than those in the livers of S. japonicum-infection control groups (+CD4+CD25+Foxp3+ cells in both livers and spleens of HBAAV2/9-Sja-miR-71a-treated S. japonicum-infected mice were significantly higher (P < 0.05) than those in the S. japonicum-infection control groups (+CD4+ Foxp3+T-bet+ (T-bet+Treg) in livers and spleens of HBAAV2/9-Sja-miR-71a-treated S. japonicum-infected mice were higher (P < 0.05) than those in S. japonicum-infection control groups . We tl groups ,b). T-bel groups ,b).FigurS. japonicum infection [Th2 cytokines are associated with persistent liver fibrosis in human nfection , and Th1nfection . Howevernfection . Thus, wS. japonicum egg-derived EVs in schistosomiasis-induced liver fibrosis. We showed that S. japonicum egg-derived EVs can inhibit the activation of HSCs, and this inhibition is mediated via the high expression of Sja-miR-71a. Sja-miR-71a attenuates the pathological progression of liver fibrosis in S. japonicum infection. Sja-miR-71a suppresses liver fibrosis by Sema4D/TGF-β1 axis and Sema4D/IL-13 axis. In addition, Sja-miR-71a suppression of liver fibrosis is partly mediated by regulating Th1, Th2, Th17 and Treg balance via inhibiting Sema4D (The present study provides evidence with a functional role of Sja-miR-71a in g Sema4D .Figure 9Schistosoma-host interactions, the host immune system works towards binding and accelerating the death of S. japonicum eggs by fibrotic reactions; accordingly, the S. japonicum eggs have evolved several ways to inhibit host fibrosis. Recent studies have shown that S. japonicum eggs and soluble egg antigens suppress the activation of HSCs to limit the progression of liver fibrosis [S. japonicum egg EVs by ultracentrifugation and found that they can inhibit the activation of HSCs. However, the results of the study by Xing He et al. showed that schistosome egg-derived EVs promoted activation of HSCs [S. japonicum egg-derived EVs in HSC inhibition, we sequenced and analysed the miRNAs of S. japonicum egg-derived EVs. We found that Sja-miR-71a is the most highly expressed miRNA.In fibrosis ,4–6,40. fibrosis . We isol of HSCs . We thou of HSCs ,44. EVs  of HSCs . To bettet al. and Nowacki et al. showed S. mansoni EVs contain sma-miR-71a [et al. showed fhe–miR-71a was expressed in Fasciola hepatica EVs [S. japonicum [in vitro. In addition, our findings demonstrate that Sja-miR-71a attenuates the pathological progression of and liver fibrosis in S. japonicum infection in vivo.Samoil -miR-71a ,46, and tica EVs . A previaponicum . Howeverin vitro and in vivo upon Sja-miR-71a overexpression. Sema4D binds with two receptors – Plexin B1 and CD72 [To further evaluate the molecular mechanisms of Sja-miR-71a in liver fibrosis, we aimed to identify direct target genes that might be controlled by Sja-miR-71a and identified Sema4D as a direct target of Sja-miR-71a. We found Sema4D in serum of schistosomiasis japonicum patients were increased. Interestingly, Sema4D expression was down-regulated and CD72 . In line−/− mouse model of experimental asthma [in vitro. IL-13 induces TGF-β1 production, a potent regulator of extracellular matrix formation [in vivo and in vitro. Thus, we conclude that Sja-miR-71a suppresses liver fibrosis by inhibiting the Sema4D/TGF-β1 and Sema4D/IL-13 axes.Studies have shown that Sema4D can directly increase TGF-β1; IL-13 and TGF-β1 were decreased in a Sema4Dl asthma ,32. In al asthma ,34,49. Tl asthma . p-SMAD l asthma . SMAD4, l asthma . We founormation . IL-13 aormation . JAK1 wa+CD4+ cells around the granuloma of S. japonicum. Therefore, we assessed the changes in Th1, Th2, Th17 and Treg subset levels in livers and spleens. Our results showed that Th1, Th2, and Th17 subset levels in the livers and spleens of HBAAV2/9-Sja-miR-71a-treated S. japonicum-infected mice were lower than control groups. Treg subset levels in both livers and spleens of HBAAV2/9-Sja-miR-71a-treated S. japonicum-infected mice were increased compared with the levels in S. japonicum-infection control groups. A previous study showed that T cell proliferation was downregulated in OVA323–339-restimulated Sema4D−/− cell cultures, and Treg numbers were increased in the spleens of Sema4D−/− mice [In the immune system, Sema4D is constitutively expressed on T cells and is crucially involved in T-cell activation . We founS. japonicum infection [S. japonicum infection [+Treg cells in the livers and spleens of HBAAV2/9-Sja-miR-71a-treated S. japonicum-infected mice were increased. Thus, we concluded that Sja-miR-71a-based suppression of liver fibrosis is partly mediated by regulating the Th1/Th2/Th17/Treg balance by inhibition of Sema4D.Treg cells regulate the balance of Th1/Th2 responses in the process of nfection . Th17 hanfection . In addinfection ,58, and nfection . Howevernfection . T-bet anfection . InteresS. japonicum egg-derived EVs suppresses liver fibrosis in schistosomiasis by directly targeting Sema4D. We believe these results contribute to further understanding the molecular mechanisms underlying Schistosoma–host interactions, and Sema4D may be a potential target for schistosomiasis liver fibrosis treatment.In summary, our data show that Sja-miR-71a in Click here for additional data file.