Construction and immunotherapy analysis of hepatocellular carcinoma prognostic model based on membrane tension-related genes

Construction and immunotherapy analysis of hepatocellular carcinoma prognostic model based on membrane tension-related genes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Construction and immunotherapy analysis of hepatocellular carcinoma prognostic model based on membrane tension-related genes pengfei zhu, Zijuan Zhu, Zheling Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4735703/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background： The membrane of tumor epithelial cells is more flexible than normal cells, and higher membrane tension can effectively inhibit the migration and invasion of tumor cells. Innovative therapies targeting the physical characteristics of tumor cells are worthy of attention. To investigate the prognostic value of membrane tension-related genes (MTGRs) in hepatocellular carcinoma (HCC) and its relationship with immunotherapy. Method: We obtained RNA-seq data and clinical characteristics data for HCC from The Cancer Genome Atlas (TCGA) database, the (International Cancer Genome Consortium) ICGC database and GEO database. Combined with univariate Cox regression analysis and LASSO (least absolute shrinkage and selection operator) regression analyses, 3-MTRGs risk model was established. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve were used to verify the model. The Nomogram model was constructed by combining the risk score and clinical characteristics, and its performance was evaluated by calibration curves. We conducted gene differential analysis and functional enrichment analysis on high- and low-risk groups, identifying relevant molecular pathways. Additionally, we analyzed the differences between the two groups in terms of immune cell infiltration, immune-related pathways, and immunotherapy. In addition, we analyzed single-cell sequencing data of HCC patients from the GEO database to study cellular infiltration in the tumor microenvironment and the distribution of model genes across different cell types. Finally, we validated the expression differences of model genes between HCC tissues and normal tissues using the GEO database (GSE121248 and GSE45267). Results 3-MTRGs (CFL1, CRTC2, SRGAP2) were involved in the model construction, and the prognosis of patients in the low-risk group was better than that in the high-risk group. Kaplan-Meier survival curve and ROC curve illustrated that the model had reliable predictive value. Enrichment analysis showed that high-risk groups were mainly concentrated in the pathways related to TUMOR CELL CYCLE and ECM RECEPTOR INTERACTION. Immuno-correlation analysis of the two groups showed that the high-risk group was associated with immune escape. High-risk HCC patients exhibited notable sensitivity to chemotherapy drugs such as 5 - Fluorouracil, Dasatinib, Osimertinib and Paclitaxel. External data sets showed that the model genes were highly expressed in HCC tissues. Conclusion We selected three MTRGs ( CFL1, CRTC2 and SRGAP2) as prognostic indicators of HCC and established a Nomogram model to predict the prognosis and efficacy of immunotherapy in HCC patients. Biological sciences/Computational biology and bioinformatics Biological sciences/Biophysics/Membrane biophysics Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Cancer models Biological sciences/Cancer/Gastrointestinal cancer Biological sciences/Cancer/Tumour biomarkers Membrane tension Hepatocellular carcinoma Prognostic model Biomarkers Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Hepatocellular carcinoma (HCC) remains a challenging malignancy, ranking sixth in incidence and third in mortality among all tumors 1 . Currently, treatment strategies for HCC include surgical resection, liver transplantation, radiofrequency ablation, chemotherapy, targeted and immunotherapy 2–4 . However, the prognosis for HCC remains poor, with a 5-year survival rate of 22% 5 . Existing prognostic systems are inadequate in providing accurate prognostic predictions for HCC patients. Therefore, it is important to search for new molecular targets that can best evaluate survival prognosis and new therapeutic approaches for HCC patients. The physical characteristics of the tumor itself deserve our attention, which is related to the invasion and metastasis of the tumor and the immune escape of the tumor cells 6,7 . Tumor physical properties include increased tumor solid stress, increased matrix stiffness, changes in fluid shear forces, and changes in tissue microstructure from subcellular to tissue level 7,8 . Cells can perceive physical mechanical stimulation and transform it into biochemical signals, activate a series of cell responses, and affect tumor cell function 9–11 . Membrane tension (MT) has also been extensively studied in the field of biomechanics. Epithelial cells maintain higher plasma membrane tension than metastatic cells, which effectively inhibits the migration and invasion of tumor cells by counteracting membrane curvature sensing/production of BAR family proteins 12 . In addition, tumor cell membrane tension regulation processes, such as cell growth, migration, differentiation, etc., require higher energy, and cell matrix stiffness promotes actin recombination to enhance the glycolysis process 13 . Tang Li et al. found that by consuming the cholesterol on the surface of tumor cells, the cancer cells were hardened, thus enhancing the immunotherapy of the tumor 14 . Furthermore, recent studies have shown that Osr2 acts as a biomechanical checkpoint, induced by a combination of TCR and mechanical force signaling, leading to CD8 + T cell depletion in the rigid tumor microenvironment, improving the efficacy of T-cell-based solid tumor immunotherapies 15 . The function of Membrane tension-related genes (MCRGs) in the prognosis of HCC patients is rarely reported. In this study, we investigated the expression and prognostic value of MTRGs in HCC patients. A risk model based on selected MTRGs to predict the prognosis of HCC patients showed promising results, providing a potential biomarker for the treatment of HCC patients 2. Materials and methods 2.1. Sample collection and processing We obtained RNA expression, clinical data and Somatic mutation data from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ), which contained 371 LIHC and 50 normal tissue samples. Additionally, we obtained 240 samples were from ICGC-LIRI-JP cohort ( https://dcc.icgc.org/projects/LIRI-JP ) as testing set. Copy number variant (CNV) data associated with HCC was downloaded from the University of California Santa Cruz (UCSC) Genomic Bioinformatics database. We obtained HCC datasets from the GEO database (GSE121248,GSE45267) to verify the differences in the expression of model genes in tumor tissues and normal tissues. Finally, we identified 84 MTRGs from prior study 16–21 . 2.2. Construction and validation of prognostic risk score model with membrane tension-related genes Wilcoxon signed-rank test was used to analyze gene differential expression between tumor tissue and normal tissue, and the genes with |log FC|> 1 and FDR < 0.05 was considered as significant DEGs. Then, 22 MTRGs were obtained after the intersection of all DEGs and 84 MTRGs. Univariate Cox regression analysis was conducted utilizing the R package "survival" to identify MTRGs correlated with the prognosis of HCC patients. We utilized LASSO Cox regression analysis to build a 3-gene signature. The MT-related risk scores of each HCC patient were calculated according to the mRNA expression of the model gene, and HCC patients were divided into high and low risk groups according to the median of their scores. Kaplan-Meier survival curves and ROC curves of the two groups were drawn by R software to evaluate the model. At the same time, the ICGC-HCC dataset was external validation. R-packages "limma" was used for principal component analysis (PCA) to detect differences in gene expression patterns between high and low risk groups. The R package "ggplot2" and "scatterplot3d" were used for visualization processing.MT-related risk scores were combined with patients' basic clinical characteristics as predictors. Univariate and multivariate COX regression analyses were performed to evaluate their independence. The ROC curve was drawn by R package "timeROC" to verify the accuracy of the model. 2.3. Nomogram A nomogram was developed incorporating the risk score along with additional clinical parameters to estimate the 1-, 3-, and 5-year overall survival probabilities for patients with HCC. The nomogram's performance was assessed through ROC curves, C-index, and calibration curves. Furthermore, decision curve analysis (DCA) was employed to evaluate the clinical net benefit of the nomogram compared to utilizing clinical features alone. 2.4. GO, KEGG analysis and GSEA analysis The DEGs between the high- and low-risk groups were identified by wilcoxon signed-rank test and the R-package "clusterProfiler" 22 , "org.Hs.eg.Db" and "enrichplot" were utilized for conducting Gene Ontology (GO) 23 and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses 24 . Gene set enrichment analysis (GSEA) and visualization were performed using the gene sets (c2.cp.kegg_legacy.v2023.2.Hs.symbols.gmt) and R packages "org.Hs.eg.db", "enrichplot", and "clusterprofiler" 22 . 2.5. Assessment of the TME, Immune Cell Infiltration, and Immunotherapy Response To investigate immune infiltration between high - and low-risk groups of HCC patients, R-package "gsva" 25 and "GSEABase" were used to assess the differences in immune cells and immune-related functions among different risk groups. Furthermore, the "estimate" R package was used to calculate the immune score, matrix score and ESTIMATE score of HCC patients to evaluate their correlation with the risk score of HCC patients 26 . TIDE ( http://tide.dfci.harvard.edu/ ) algorithm to estimate the two groups for the reactivity of immunotherapy. 2.6. CNV and mutation analysis The CNV frequency of MTRGs were analyzed by R software, and the chromosomal localization of CNV mutations of MTRGs were visualized by R package "RCircos". The R package "maftools" was applied to calculate the somatic mutation count and Tumor mutational burden (TMB) value of each HCC patient, and all samples were divided into high TMB group and low TMB group 27 . Wilcoxon test were performed to compare and visualize somatic mutations and TMB levels between the high and low TMB groups. We conducted Kaplan-Meier analysis to compare the overall survival (OS) between the two groups. Similarly, we categorized HCC patients into four groups based on their TMB and risk scores, and then compared the OS among these four groups 27 . 2.7. Chemotherapy response We employed the "oncoPredict" 28 R software package to compute the half inhibitory concentration (IC50) values of therapeutic drugs for tumors and assess the variance in efficacy between high and low-risk groups. 2.8 scRNA-seq analysis We visualized the expression patterns and distribution of signature genes within the GSE125449 dataset by generating Unified Streaming Approximate Projection (UMAP) plots from the TISCH ( http://tisch1.comp-genomics.org/ ) database. 2.9. The expression of model genes was verified in GEO data set In order to verify the accuracy of the model we established, we obtained relevant HCC samples and normal tissue samples from the GEO database and compared the expression differences of model genes between the samples by the R-package "limma". 2.10 Statistical analyses In this study, data analysis and visualization were conducted using R software (version 4.2.2). We developed a prognostic risk scoring model through univariate Cox regression analysis and Lasso regression analysis. The Wilcoxon test was employed for comparing classified and non-normally distributed variables, whereas normally distributed variables were compared using the t-test. Significance was determined at a threshold of P < 0.05. 3. Results 3.1. Genetic profile of MTRGs in HCC (Fig. 1 ) 3.2. Construction and validation of prognostic risk score model with membrane tension-related genes The heatmap exhibits the top 50 genes with the most significant differences between the tumor and normal groups. ( Fig. 2 A ). 22 MTRGs were identified to analysis, intersected by 5980 DEGs and 84 MTGRs ( Fig. 2 B ) . Univariate Cox regression was used to screen 9 MTRGs (CTNNBIP1, CHMP4B, CRTC2, CFL1, DNM2, FNBP1L, PACSIN1, SH3GL1, and SRGAP2) associated with prognosis in the training set and their high expression is associated with poor prognosis of HCC patients ( Fig. 2 C ). We utilized LASSO Cox regression analysis to establish 3-MTRGs signature. Risk Score = (SRGAP2 * 0.252) + (CRTC2 * 0.083) + (CFL1*0.471) ( Fig. 2 D ) . 3.3. Verification and evaluation of the model With the TCGA cohort as the training set, we categorized HCC patients into high and low-risk groups based on the median risk value. Additionally, PCA analysis validated the division of HCC patients into high and low-risk groups ( Fig. 3 A ) . In addition, the same trend was observed in the ICGC cohort ( Fig. 3 B ) . The results showed that patients in the high-risk group had a poorer prognosis and higher expression of the model gene ( Fig. 3 A ) . The same conclusion was obtained in ICGC cohort ( Fig. 3 A ) . Next, the risk scoring formula was used to analyze the distribution of membrane tension related risk scores, survival status and expression of prognostic model genes between the high and low risk groups ( Fig. 3 C-D ) . The reliability of the ROC curve detection model to estimate the prognosis of HCC patients, and the AUC of this model at 1, 3 and 4 ( 5 ) years were > 0.60 ( Fig. 4 A-B ) , indicating that this model has a good prognostic effect on HCC patients. The C-index curve showed that the model predicted survival of HCC patients better than other basic clinical traits ( Fig. 4 C ) . Univariate Cox regression analysis showed that clinical stage and MT-related risk scores were closely related to the prognosis of patients ( Fig. 4 D ) . Multivariate COX regression analyses showed that MT-related risk scores were independent predictors of HCC patients ( Fig. 4 E ) . 3.4. Nomogram survival prediction model In this study, we established a Nomogram chart for predicting the 1-year, 3-year and 5-year survival probability of HCC patients, and calibration curve indicated the predicted results were reliable ( Fig. 5 A-B ) . The DCA decision curve showed that the model predicted survival of HCC patients better than other basic clinical traits (Fig. 5 C). 3.5. Enrichment analysis of prognostic model Prognostic models were used to further analyze the DEGs (|logFC|> 1 and FDR < 0.05) in gene function and signal transduction between the high-risk groups, the DEGs between the two groups were identified by wilcoxon signed-rank test for GO enrichment analysis. The results showed that the biological process (BP) items were mainly concentrated in regulation of cell-cell adhesion, leukocyte migration, regulation of T cell activation, regulation of leukocyte proliferation, the cell components (CC) mainly concentrated in collagen − containing extracellular matrix, microtubule, spindle, chromosomal region and the molecular functions (MF) mainly concentrated in actin binding, tubulin binding, microtubule binding, extracellular matrix structural constituent (Fig. 6 A). KEGG analysis revealed DEGs mainly concentrated in Cell cycle, ECM − receptor interaction, Cell adhesion molecules, Phagosome, Protein digestion and absorption, and etc (Fig. 6 B). GESA analysis show that the signaling pathways related to tumorigenesis and cell membrane tension are significantly enriched in the high risk group of HCC patients, including Cell Cycle, Cytokine-Cytokine Receptor Interaction, Hematopoietic Cell Lineage, Leishmania Infection (Fig. 6 C); Patients classified in the low-risk group exhibited significant enrichment in pathways associated with metabolism, including Butanoate Metabolism, Glycine Serine And Threonine Metabolism, Primary Bile Acid Biosynthesis, Tryptophan Metabolism And Tyrosine Metabolism (Fig. 6 D). 3.6 The role of membrane tension-related risk scores in the immune microenvironment The ssGSEA was used to compare the differences in immune cell infiltration and immune-related signaling pathways between the two groups. Among the 22 types of immune cells, 11 types of immune cells were significantly upregulated in the high-risk group: Activated.CD4.T Cell, Activated Dendritic Cells, Immature B Cells ,Immature Dendritic Cells, MDSC, Natural Killer Cell, Plasmacytoid Dendritic Cell, Regulatory T Cells (Tregs), T Follicular Helper Cell and Type2 T Helper Cell; 2 types of immune cells were significantly upregulated in the low-risk group: Eosinophil and Neutrophil (Fig. 7 A). Of the 13 immune-related pathways, 9 were significantly upregulated in the high-risk group: APC cell co-stimulation, checkpoint, human leukocyte antigen (HLA), MHC Class I, parainflammation, etc; One was significantly upregulated in the low-risk group: type II IFN response (Fig. 7 B). Moreover, our analysis of immune checkpoint gene (ICG) expression revealed upregulation of several ICGs, such as OSR2, HAVCR2, TIGIT, PDCD1, LAG3, CTLA4, and CD274, in the high-risk group ( Fig. 7 C ) . These findings suggest that high-risk patients are more inclined towards an immunosuppressive tumor microenvironment phenotype. It is worth noting that recent studies have shown that Osr2, as a biomechanical checkpoint, can accelerate the depletion of CD8 + T cells in tumors, and Osr2 gene deletion can enhance anti-tumor immune efficacy 15 . The ESTIMATE algorithm showed the high-risk group tended to have higher immuneScore and ESTIMATEScore (Fig. 7 D). Upon evaluating the response to immunotherapy across both groups, we observed higher TIDE scores in the high-risk group compared to the low-risk group. This suggests that patients categorized in the high-risk group have an increased probability of immune evasion (Fig. 7 E). In addition, non-responders had higher risk-score compared to responders (Fig. 7 F). Figure 7 G shows a comparison of response to immunotherapy in the high-low risk group (57% vs 28%). In summary, individuals classified as high-risk tend to exhibit immunosuppressive tumor microenvironments and demonstrate reduced sensitivity to immunotherapy. 3.7 Genetic variation and mutation of membrane tension-related genes in HCC Chromosomal localization analysis showed that cell MT-related factors were located at the locations of CNV mutations in chromosomes (Fig. 8 A). In addition, CNV mutations were found to be prevalent in MTRGs, most of which had extensive CNV amplification, among which CRTC2 and SRGAP2 in prognostic models had CNV amplification, while CFL1 had CNV deletion (Fig. 8 B). Among 368 HCC patients, the incidence of gene mutation was 7.88% (29/368). FLNA has the highest mutation rate, followed by DNM2 and CRTC2. Missense mutation is the most common type of mutation (Fig. 8 C). The relationship between MT-related risk score and mutation status was then evaluated using somatic mutation data. It was found that the mutation rate in the high-risk group was higher than that in the low-risk group. TP53 mutations had the highest frequency (42%) in the high-risk group and CTNNB1 mutations had the highest frequency (26%) in the low-risk group (Fig. 8 D-E). The Tumor mutation burden (TMB) showed no difference between two groups (P = 0.71) (Fig. 8 F). Combined with the synergistic effect of TMB and MT-related risk score on prognosis, stratification prognostic analysis found that patients with low cell membrane tension associated risk score and low TMB combination (L-TMB + low-risk) showed the greatest survival advantage (Fig. 8 G-H). These data suggest that the combination of cell membrane tension-related risk scores and TMB can further improve patient prognosis. 3.8 Prediction of drug response Figure 9 depicts the outcomes of the drug sensitivity analysis, wherein potential chemotherapy agents were identified based on differences in IC50 measurements between the low-risk and high-risk groups. High-risk subtype of HCC patients for 5 - Fluorouracil, Dasatinib, Osimertinib, Paclitaxel, Afatinib significantly sensitive to chemotherapy drugs (P < 0.05). While low-risk groups were sensitive to Sorafenib, Cytarabine, Dihydrorotenone, Axitinib and Mirin (P < 0.05) (Fig. 9 ). 3.9 Exploring the Relationship Between Membrane Tension and Immune-Related Prognostic Signature Across Single-Cell Properties Recently, single-cell sequencing has emerged as a pivotal tool for uncovering cellular diversity and distinctions. In our quest to delve deeper into the functionality of prognostic genes within the tumor microenvironment, we accessed data from the TISCH database (GSE140228) and employed UMAP visualization to delineate 12 distinct cell clusters. Each cluster was annotated based on its characteristic genes, denoted as B, CD4Tconv, CD8T, CD8Tex, DC, ILC, mast, mono/macro, NK, plasma, tprolif, and Tregs. Upon examining the distribution of prognostic marker genes across these clusters, SRGAP2 predominantly appeared in DC and mono/macro clusters, CFL1 was detected across nearly all clusters except plasma, while CRTC2 exhibited lower abundance across all 12 cell clusters (Fig. 10 ). 3.10 The expression of MTRGs was verified in GEO data set We downloaded the two datasets GSE121248 and GSE45267 from the GEO database. In the GSE121248 dataset, there were 70 HCC tissues and 37 normal tissues. The GSE45267 dataset included 46 HCC tissues and 41 normal tissues. The "limma" package in R was used to analyze the difference in expression of model genes (CRTC2, CFL1, SRGAP2) between HCC tissues and non-tumor tissues. The results showed that the expression of model genes in HCC tissues was higher than that in normal liver tissues in the two datasets (Fig. 11 ). Discussion More and more researches began to pay attention to the mechanical environment of tumor, and the relationship between cell mechanics and tumor gradually became a new research hotspot 29–32 . The change of MT may be the root cause of the decrease of cell hardness, which is related to the softening, proliferation and metastasis of cancer cells 12,33 . The metastasis of malignant cells is usually accompanied by changes in cell mechanical properties. A range of evidence show that tumor cells can promote metastasis and alter cell metabolism by reducing MT 12,34,35 . In our study, we investigated the differential expression of MTGRs in HCC patients and identified MTRGs associated with HCC prognosis. Using Lasso Cox analysis, we successfully identified 3 MTRGs (CRTC2, CFL1, SRGAP2) as key genes in a prognostic model and established a prognostic model to classify HCC patients into high-risk and low-risk groups. Cofilin1(CFL1) is an actin-binding protein involved in tissue development, cytoskeleton remodeling and homeostasis by regulating cell membrane tension 36 . Studies have found that CFL1 is highly expressed in various tumor tissues such as pancreatic cancer 37 , triple negative breast cancer (TNBC) 38 and colorectal cancer 39 , and plays a regulatory role in triggering tumor cell transformation, enhancing the ability of cancer cell metastasis, cell division and drug resistance 40 . The Cofilin-1 protein regulates actin dynamics by facilitating actin treadmilling, thus stimulating membrane protrusion and promoting cell migration and invasion 41 . Cofilin-1 signaling can promote actin cytoskeletal recombination and intercellular adhesion regulation mediating epithelial mesenchymal transformation 41 . It had confirmed that CFL1 activates the PLD1/AKT signaling pathway to increase proliferation, migration, invasion and ECM of HCC 42 . CRTC2 is a member of the CREB-regulated transcription coactivator (CRTC) family. It augments CREB transcriptional activity by interacting with the leucine zipper DNA-binding domain of CREB 43 . The CREB/CRTC2 complex directly regulates the expression of many key genes involved in proliferation and apoptosis 44 . In the liver, CRTC2 exhibits high expression levels, leading to the activation of the CREB pathway. This activation, particularly under fasting conditions, triggers the conversion of glycogen stored in the liver into glucose 45,46 . Recently, numerous studies have explored the potential role of CRTC2 in malignancies, including lymphomas 47 , lung 48 , colorectal 49 , and liver cancers 50 . The research found that CRTC2 is overexpressed in HCC and it drives the malignant phenotype of HCC by activating the Wnt/β-catenin pathway 50 . Slit-Roundabout (Robo) GTPase activating protein 2 (SRGAP2), belonging to the BDP family, plays a role in inducing outward curvature of cell membranes. Its function involves the interaction with membranes to alter their shape, consequently influencing actin dynamics and thereby controlling processes such as cell migration and differentiation 51 . As a force transduction protein across cell membranes and cytoskeleton, SRGAP2 is involved in malignant cell migration and cancer processes and the high expression of SRGAP2 promotes the migration of HCC cells 52,53 . It had shown that SDC4-PKCα polarization leads to an intracellular tension gradient of SRGAP2, showing extracellular and intracellular physicochemical integration, which is essential for sustained cell migration and cancer progression in rigid substrates 53 . Our research found that high-risk group had a worse prognosis than the low-risk group, and the feasibility of the model was confirmed by external ICGC data sets. ROC method proved that risk scores as a prognostic factor has high sensitivity and specificity. Additionally, our results highlighted its significance as a primary independent prognostic risk factor within clinical variables. Our findings reveal a robust distribution of samples into two distinct subtypes, demonstrating significant correlations with survival outcomes. GSEA analysis showed that Cell Cycle, Cytokine-Cytokine Receptor Interaction and Hematopoietic Cell Lineage were activated in high-risk HCC patients, inducing poor prognosis for high-risk patients. While, the low-risk group was mainly enriched in the metabolism-related pathways.Our results showed significant differences between the two groups in the proportion of certain immune cells in the immune microenvironment, such as activated B cells, CD4 + T cells, MDSC, etc. Patients in the high-risk group had higher TIDE and higher ICGs gene expression compared with those in the low-risk group, suggesting a higher immune escape and immunosuppressive microenvironment. Our study showed that there were high mutations in high-risk groups, and patients with high tumor mutation load had poor prognosis. Combined with the synergistic effect of TMB and cell membrane tension-related risk score on prognosis, stratified prognosis analysis found that, Patients with a low cell membrane tension-associated risk score and a combination of low TMB (L-TMB + low-risk) showed the greatest survival advantage. These data suggest that the combination of cell membrane tension-related risk scores and TMB can further improve patient prognosis. Membrane tension has been associated with the antitumor effects of various chemotherapy and targeted agents, including 5-Fluorouracil, Dasatinib, Osimertinib, Paclitaxel, Afatinib, Sorafenib, Cytarabine, Dihydrorotenone, Axitinib, etc. Finally, we obtained HCC samples and normal tissue samples from the GEO database (GSE121248 and GSE45267) and compared the differences in the expression of model genes. The findings indicated elevated expression levels of model genes in hepatocellular carcinoma (HCC) tissues compared to normal tissues across both datasets. Conclusion Following thorough analyses and validation procedures, our study successfully demonstrated the predictive capability of a prognostic model reliant on the expression levels of three key MTRGs (SRGAP2, CRTC2, CFL1) in assessing the prognosis of hepatocellular carcinoma (HCC) patients. Furthermore, we delved into the immune-level implications of these MTRGs, elucidating their significant roles in the context of HCC. Declarations Acknowledgements The TCGA, ICGC ,GEO, TISCH and TIDE database is gratefully acknowledged for the availability of the raw research data. Author contributions Pengfei Zhu proposed the idea, analyzed data and wrote the original draft. Zijuan Zhu is responsible for data screening and drawing pictures. Zheling Chen revised the manuscript. Funding Not applicable. Data availability All data or codes during the study are available on request from the corresponding author. 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Neuron 91, 356–369, doi: 10.1016/j.neuron.2016.06.013 (2016). Li, Y. et al. Identification of SRGAP2 as a potential oncogene and a prognostic biomarker in hepatocellular carcinoma. Life Sci 277, 119592, doi: 10.1016/j.lfs.2021.119592 (2021). Li, C. et al. Stiff matrix induced srGAP2 tension gradients control migration direction in triple-negative breast cancer. Theranostics 13, 59–76, doi: 10.7150/thno.77313 (2023). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4735703","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":335704065,"identity":"2120c705-349f-4078-8df6-8b72c5b26d32","order_by":0,"name":"pengfei zhu","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College)","correspondingAuthor":false,"prefix":"","firstName":"pengfei","middleName":"","lastName":"zhu","suffix":""},{"id":335704066,"identity":"b7a39b7c-c6a8-4cd5-a6a9-835c768a57df","order_by":1,"name":"Zijuan Zhu","email":"","orcid":"","institution":"the Second Affiliated Hospital of Bengbu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zijuan","middleName":"","lastName":"Zhu","suffix":""},{"id":335704067,"identity":"d34aef1b-f4d3-4555-81ef-c003f277a8ba","order_by":2,"name":"Zheling Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACCSBmbGCQYQNSjyFCCcRp4QFqYTYmTQuQYpMmSgv/7OZjD3/usOHhk26/Vl2YY8fAz55jwPBzBx5L7hxLN5A8k8bDJnOm7PbMbckMkj1vDBh7z+DWYiCRYyZh2HaYh00iJ+0277YDDAY3cgyYGdvwacn/JpHY9h+spRikxZ6wlhw2iYNtB4Ba0o8xg22RIKBF4kaamWRjWzLIFmZp3m3JPBJnnhUc7MWjhX9G8jPJn212cvIz0h9+5t1mJ8ffnrzxwU88WpAAjwGYBBEHiNLAwMD+gEiFo2AUjIJRMNIAAI1tSFnEmbLGAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College)","correspondingAuthor":true,"prefix":"","firstName":"Zheling","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-07-13 15:17:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4735703/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4735703/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62654796,"identity":"0f7a17a5-762a-4096-bfc5-81748d7a154b","added_by":"auto","created_at":"2024-08-17 01:29:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":721420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe research flowchart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/f996f4c610cce8dc349cfb43.png"},{"id":62654084,"identity":"41ecf16d-5223-47d3-bc05-cb8e4f485180","added_by":"auto","created_at":"2024-08-17 01:21:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":759348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening membrane tension-related genes (MTRGs)for prognostic models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Top 50 genes with the most significant differences between the tumor tissue and normal tissue. (B) 22 MTRGs, intersected by 5980 DEGs and 84 MTGRs. (C) Forest plot of univariate Cox analysis showing the 9 MTRGs significantly associated with OS in HCC patients (P \u0026lt;0.05). (D) 3-MTGRs risk score model were established by LASSO Cox regression analysis.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/07b5bb0301d7c0b3c08b91c9.png"},{"id":62655495,"identity":"368eec05-756c-437b-aedc-20521b546bcd","added_by":"auto","created_at":"2024-08-17 01:37:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA and Kaplan-Meier of high-low risk group, distribution of risk score, survival state and expression of model genes in high-low risk group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA diagram and Kaplan-Meier survival curve for the TCGA cohort. (B) PCA diagram and Kaplan-Meier survival curve for the ICGC cohort. (C) MTRGs related risk score, survival status and the expression of 3 prognostic model genes in TCGA cohort. (D) MTRGs related risk score, survival status and the expression of 3 prognostic model genes in ICGC cohort.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/96bd5cc4226e942ae6249b72.png"},{"id":62654075,"identity":"e3ae3532-f726-4495-850c-228f436eb544","added_by":"auto","created_at":"2024-08-17 01:21:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) ROC curve for training set (TCGA cohort). (B) ROC curve for testing set (ICGC cohort). (C) C-index curve. (D) Univariate Cox analysis. (E) Multivariate COX analyses.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/253052f85b6411b636a54f1f.png"},{"id":62654076,"identity":"db125d64-70a6-4a2d-8b2b-b9f35b58d591","added_by":"auto","created_at":"2024-08-17 01:21:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and verification of Nomogram model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The nomogram to predict 1-, 3-, and 5-years’ survival in HCC patients. (B) Calibration curve of the nomogram. (C) Decision curve of the nomogram.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/37a93e8194bcd88a324234c9.png"},{"id":62654078,"identity":"4c9738a4-cbea-4308-9d39-50c3b55c30a5","added_by":"auto","created_at":"2024-08-17 01:21:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":179762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) GO enrichment analysis. (B) KEGG enrichment analysis. (C) GSEA analysis of high-risk group. (D) GSEA analysis of low-risk group.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/81571b216d8c26dba38ae742.png"},{"id":62654795,"identity":"32d5b281-9fb9-404a-8bf6-3e106c0d026f","added_by":"auto","created_at":"2024-08-17 01:29:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":122380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune correlation of high-risk group and low-risk group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Immune cell infiltration in high-risk group and low-risk group. (B) Differences in immune-related signaling pathways between high-group and low-risk group. (C) Differences in TIDE scores between high-risk group and low-risk group (*p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001). (D) Stromal scores, immune scores and ESTIMATE scores for high-risk group and low-risk group. (E) Comparison of TIDE scores between high and low risk groups. (F) Comparison of Risk scores between responder and non-responder groups. (G) Comparison of response to immunotherapy between the high-low risk group\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/74afcb106357a7e0cf42dde2.png"},{"id":62654079,"identity":"b7159df8-4ffc-491e-abbd-fa5679d03c8a","added_by":"auto","created_at":"2024-08-17 01:21:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":210624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic variation of MT-related genes in HCC and TMB Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) CNV chromosome localization of membrane tension-related genes in HCC patients. (B) Membrane tension-related genes CNV profile of related genes in HCC patients. (C) Mutations of 22 membrane tension-related genes in HCC patients. (D) Somatic cell mutations in high-risk group. (E) Somatic cell mutations in low-risk group. (F) Relationship between TMB and cell membrane tension-related risk score. (G) Kaplan-Meier curve shows TMB is associated with cell membrane tension risk score survival. (H) Survival analysis combining TMB and membrane tension-related risk score.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/5f5430f2cbf0330e5097a980.png"},{"id":62655497,"identity":"0b37ddde-f5ca-4dd5-9e7f-788a4586426c","added_by":"auto","created_at":"2024-08-17 01:37:07","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":100413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis between high and low risk groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-risk subtype of HCC patients for 5 - Fluorouracil, Dasatinib, Osimertinib, Paclitaxel, Afatinib significantly sensitive to chemotherapy drugs and low-risk groups were sensitive to Sorafenib, Cytarabine, Dihydrorotenone, Axitinib and Mirin.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/0d066d6eea5c4507697589d2.png"},{"id":62655671,"identity":"41fad6b7-1858-418d-9cbd-026355dde97d","added_by":"auto","created_at":"2024-08-17 01:45:07","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":409238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of the prognostic signature with single-cell clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualizing the UMAP Representation of 12 Primary Cell Clusters within the LIHC Tumor Microenvironment, Alongside the Spatial Distribution of Prognostic Genes Among Cell Clusters.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/775cdf73483fcc0f007654c9.png"},{"id":62655672,"identity":"20a4fa41-fd15-4715-9ed8-c0f4999ff29c","added_by":"auto","created_at":"2024-08-17 01:45:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":53967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression difference of model genes in liver cancer tissues and normal tissues in the two datasets (*p \u0026lt; 0.05; **p \u0026lt; 0.01; ****p \u0026lt; 0.0001)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/d2e93e3d0e1cb601fba6e2cf.png"},{"id":63010187,"identity":"12f17642-403a-4c9d-9803-fff1aa949365","added_by":"auto","created_at":"2024-08-22 05:32:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2920328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4735703/v1/93900af8-3558-4711-b2ba-87fc5641cbe2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and immunotherapy analysis of hepatocellular carcinoma prognostic model based on membrane tension-related genes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) remains a challenging malignancy, ranking sixth in incidence and third in mortality among all tumors\u003csup\u003e1\u003c/sup\u003e. Currently, treatment strategies for HCC include surgical resection, liver transplantation, radiofrequency ablation, chemotherapy, targeted and immunotherapy\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e. However, the prognosis for HCC remains poor, with a 5-year survival rate of 22%\u003csup\u003e5\u003c/sup\u003e. Existing prognostic systems are inadequate in providing accurate prognostic predictions for HCC patients. Therefore, it is important to search for new molecular targets that can best evaluate survival prognosis and new therapeutic approaches for HCC patients. The physical characteristics of the tumor itself deserve our attention, which is related to the invasion and metastasis of the tumor and the immune escape of the tumor cells\u003csup\u003e6,7\u003c/sup\u003e. Tumor physical properties include increased tumor solid stress, increased matrix stiffness, changes in fluid shear forces, and changes in tissue microstructure from subcellular to tissue level\u003csup\u003e7,8\u003c/sup\u003e. Cells can perceive physical mechanical stimulation and transform it into biochemical signals, activate a series of cell responses, and affect tumor cell function\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. Membrane tension (MT) has also been extensively studied in the field of biomechanics. Epithelial cells maintain higher plasma membrane tension than metastatic cells, which effectively inhibits the migration and invasion of tumor cells by counteracting membrane curvature sensing/production of BAR family proteins\u003csup\u003e12\u003c/sup\u003e. In addition, tumor cell membrane tension regulation processes, such as cell growth, migration, differentiation, etc., require higher energy, and cell matrix stiffness promotes actin recombination to enhance the glycolysis process\u003csup\u003e13\u003c/sup\u003e. Tang Li et al. found that by consuming the cholesterol on the surface of tumor cells, the cancer cells were hardened, thus enhancing the immunotherapy of the tumor\u003csup\u003e14\u003c/sup\u003e. Furthermore, recent studies have shown that Osr2 acts as a biomechanical checkpoint, induced by a combination of TCR and mechanical force signaling, leading to CD8\u0026thinsp;+\u0026thinsp;T cell depletion in the rigid tumor microenvironment, improving the efficacy of T-cell-based solid tumor immunotherapies\u003csup\u003e15\u003c/sup\u003e. The function of Membrane tension-related genes (MCRGs) in the prognosis of HCC patients is rarely reported.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the expression and prognostic value of MTRGs in HCC patients. A risk model based on selected MTRGs to predict the prognosis of HCC patients showed promising results, providing a potential biomarker for the treatment of HCC patients\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sample collection and processing\u003c/h2\u003e \u003cp\u003eWe obtained RNA expression, clinical data and Somatic mutation data from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contained 371 LIHC and 50 normal tissue samples. Additionally, we obtained 240 samples were from ICGC-LIRI-JP cohort (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dcc.icgc.org/projects/LIRI-JP\u003c/span\u003e\u003cspan address=\"https://dcc.icgc.org/projects/LIRI-JP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as testing set. Copy number variant (CNV) data associated with HCC was downloaded from the University of California Santa Cruz (UCSC) Genomic Bioinformatics database. We obtained HCC datasets from the GEO database (GSE121248,GSE45267) to verify the differences in the expression of model genes in tumor tissues and normal tissues. Finally, we identified 84 MTRGs from prior study\u003csup\u003e16\u0026ndash;21\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Construction and validation of prognostic risk score model with membrane tension-related genes\u003c/h2\u003e \u003cp\u003eWilcoxon signed-rank test was used to analyze gene differential expression between tumor tissue and normal tissue, and the genes with |log FC|\u0026gt; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant DEGs. Then, 22 MTRGs were obtained after the intersection of all DEGs and 84 MTRGs. Univariate Cox regression analysis was conducted utilizing the R package \"survival\" to identify MTRGs correlated with the prognosis of HCC patients. We utilized LASSO Cox regression analysis to build a 3-gene signature. The MT-related risk scores of each HCC patient were calculated according to the mRNA expression of the model gene, and HCC patients were divided into high and low risk groups according to the median of their scores. Kaplan-Meier survival curves and ROC curves of the two groups were drawn by R software to evaluate the model. At the same time, the ICGC-HCC dataset was external validation. R-packages \"limma\" was used for principal component analysis (PCA) to detect differences in gene expression patterns between high and low risk groups. The R package \"ggplot2\" and \"scatterplot3d\" were used for visualization processing.MT-related risk scores were combined with patients' basic clinical characteristics as predictors. Univariate and multivariate COX regression analyses were performed to evaluate their independence. The ROC curve was drawn by R package \"timeROC\" to verify the accuracy of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Nomogram\u003c/h2\u003e \u003cp\u003eA nomogram was developed incorporating the risk score along with additional clinical parameters to estimate the 1-, 3-, and 5-year overall survival probabilities for patients with HCC. The nomogram's performance was assessed through ROC curves, C-index, and calibration curves. Furthermore, decision curve analysis (DCA) was employed to evaluate the clinical net benefit of the nomogram compared to utilizing clinical features alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. GO, KEGG analysis and GSEA analysis\u003c/h2\u003e \u003cp\u003eThe DEGs between the high- and low-risk groups were identified by wilcoxon signed-rank test and the R-package \"clusterProfiler\"\u003csup\u003e22\u003c/sup\u003e, \"org.Hs.eg.Db\" and \"enrichplot\" were utilized for conducting Gene Ontology (GO)\u003csup\u003e23\u003c/sup\u003e and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses\u003csup\u003e24\u003c/sup\u003e. Gene set enrichment analysis (GSEA) and visualization were performed using the gene sets (c2.cp.kegg_legacy.v2023.2.Hs.symbols.gmt) and R packages \"org.Hs.eg.db\", \"enrichplot\", and \"clusterprofiler\"\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Assessment of the TME, Immune Cell Infiltration, and Immunotherapy Response\u003c/h2\u003e \u003cp\u003eTo investigate immune infiltration between high - and low-risk groups of HCC patients, R-package \"gsva\"\u003csup\u003e25\u003c/sup\u003e and \"GSEABase\" were used to assess the differences in immune cells and immune-related functions among different risk groups. Furthermore, the \"estimate\" R package was used to calculate the immune score, matrix score and ESTIMATE score of HCC patients to evaluate their correlation with the risk score of HCC patients\u003csup\u003e26\u003c/sup\u003e. TIDE ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) algorithm to estimate the two groups for the reactivity of immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. CNV and mutation analysis\u003c/h2\u003e \u003cp\u003eThe CNV frequency of MTRGs were analyzed by R software, and the chromosomal localization of CNV mutations of MTRGs were visualized by R package \"RCircos\". The R package \"maftools\" was applied to calculate the somatic mutation count and Tumor mutational burden (TMB) value of each HCC patient, and all samples were divided into high TMB group and low TMB group\u003csup\u003e27\u003c/sup\u003e. Wilcoxon test were performed to compare and visualize somatic mutations and TMB levels between the high and low TMB groups. We conducted Kaplan-Meier analysis to compare the overall survival (OS) between the two groups. Similarly, we categorized HCC patients into four groups based on their TMB and risk scores, and then compared the OS among these four groups\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Chemotherapy response\u003c/h2\u003e \u003cp\u003eWe employed the \"oncoPredict\"\u003csup\u003e28\u003c/sup\u003e R software package to compute the half inhibitory concentration (IC50) values of therapeutic drugs for tumors and assess the variance in efficacy between high and low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 scRNA-seq analysis\u003c/h2\u003e \u003cp\u003eWe visualized the expression patterns and distribution of signature genes within the GSE125449 dataset by generating Unified Streaming Approximate Projection (UMAP) plots from the TISCH (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch1.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://tisch1.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. The expression of model genes was verified in GEO data set\u003c/h2\u003e \u003cp\u003eIn order to verify the accuracy of the model we established, we obtained relevant HCC samples and normal tissue samples from the GEO database and compared the expression differences of model genes between the samples by the R-package \"limma\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analyses\u003c/h2\u003e \u003cp\u003eIn this study, data analysis and visualization were conducted using R software (version 4.2.2). We developed a prognostic risk scoring model through univariate Cox regression analysis and Lasso regression analysis. The Wilcoxon test was employed for comparing classified and non-normally distributed variables, whereas normally distributed variables were compared using the t-test. Significance was determined at a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1. Genetic profile of MTRGs in HCC (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Construction and validation of prognostic risk score model with membrane tension-related genes\u003c/h2\u003e\n \u003cp\u003eThe heatmap exhibits the top 50 genes with the most significant differences between the tumor and normal groups. \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cstrong\u003e).\u003c/strong\u003e 22 MTRGs were identified to analysis, intersected by 5980 DEGs and 84 MTGRs \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. Univariate Cox regression was used to screen 9 MTRGs (CTNNBIP1, CHMP4B, CRTC2, CFL1, DNM2, FNBP1L, PACSIN1, SH3GL1, and SRGAP2) associated with prognosis in the training set and their high expression is associated with poor prognosis of HCC patients \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cstrong\u003e).\u003c/strong\u003e We utilized LASSO Cox regression analysis to establish 3-MTRGs signature. Risk Score = (SRGAP2 * 0.252) + (CRTC2 * 0.083) + (CFL1*0.471) \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Verification and evaluation of the model\u003c/h2\u003e\n \u003cp\u003eWith the TCGA cohort as the training set, we categorized HCC patients into high and low-risk groups based on the median risk value. Additionally, PCA analysis validated the division of HCC patients into high and low-risk groups \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. In addition, the same trend was observed in the ICGC cohort \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. The results showed that patients in the high-risk group had a poorer prognosis and higher expression of the model gene \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. The same conclusion was obtained in ICGC cohort \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. Next, the risk scoring formula was used to analyze the distribution of membrane tension related risk scores, survival status and expression of prognostic model genes between the high and low risk groups \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC-D\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eThe reliability of the ROC curve detection model to estimate the prognosis of HCC patients, and the AUC of this model at 1, 3 and 4 (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e) years were \u0026gt;\u0026thinsp;0.60 \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-B\u003cstrong\u003e)\u003c/strong\u003e, indicating that this model has a good prognostic effect on HCC patients. The C-index curve showed that the model predicted survival of HCC patients better than other basic clinical traits \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. Univariate Cox regression analysis showed that clinical stage and MT-related risk scores were closely related to the prognosis of patients \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cstrong\u003e)\u003c/strong\u003e. Multivariate COX regression analyses showed that MT-related risk scores were independent predictors of HCC patients \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Nomogram survival prediction model\u003c/h2\u003e\n \u003cp\u003eIn this study, we established a Nomogram chart for predicting the 1-year, 3-year and 5-year survival probability of HCC patients, and calibration curve indicated the predicted results were reliable \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-B\u003cstrong\u003e)\u003c/strong\u003e. The DCA decision curve showed that the model predicted survival of HCC patients better than other basic clinical traits (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Enrichment analysis of prognostic model\u003c/h2\u003e\n \u003cp\u003ePrognostic models were used to further analyze the DEGs (|logFC|\u0026gt; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in gene function and signal transduction between the high-risk groups, the DEGs between the two groups were identified by wilcoxon signed-rank test for GO enrichment analysis. The results showed that the biological process (BP) items were mainly concentrated in regulation of cell-cell adhesion, leukocyte migration, regulation of T cell activation, regulation of leukocyte proliferation, the cell components (CC) mainly concentrated in collagen\u0026thinsp;\u0026minus;\u0026thinsp;containing extracellular matrix, microtubule, spindle, chromosomal region and the molecular functions (MF) mainly concentrated in actin binding, tubulin binding, microtubule binding, extracellular matrix structural constituent (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). KEGG analysis revealed DEGs mainly concentrated in Cell cycle, ECM\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction, Cell adhesion molecules, Phagosome, Protein digestion and absorption, and etc (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). GESA analysis show that the signaling pathways related to tumorigenesis and cell membrane tension are significantly enriched in the high risk group of HCC patients, including Cell Cycle, Cytokine-Cytokine Receptor Interaction, Hematopoietic Cell Lineage, Leishmania Infection (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC); Patients classified in the low-risk group exhibited significant enrichment in pathways associated with metabolism, including Butanoate Metabolism, Glycine Serine And Threonine Metabolism, Primary Bile Acid Biosynthesis, Tryptophan Metabolism And Tyrosine Metabolism (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 The role of membrane tension-related risk scores in the immune microenvironment\u003c/h2\u003e\n \u003cp\u003eThe ssGSEA was used to compare the differences in immune cell infiltration and immune-related signaling pathways between the two groups. Among the 22 types of immune cells, 11 types of immune cells were significantly upregulated in the high-risk group: Activated.CD4.T Cell, Activated Dendritic Cells, Immature B Cells ,Immature Dendritic Cells, MDSC, Natural Killer Cell, Plasmacytoid Dendritic Cell, Regulatory T Cells (Tregs), T Follicular Helper Cell and Type2 T Helper Cell; 2 types of immune cells were significantly upregulated in the low-risk group: Eosinophil and Neutrophil (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Of the 13 immune-related pathways, 9 were significantly upregulated in the high-risk group: APC cell co-stimulation, checkpoint, human leukocyte antigen (HLA), MHC Class I, parainflammation, etc; One was significantly upregulated in the low-risk group: type II IFN response (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). Moreover, our analysis of immune checkpoint gene (ICG) expression revealed upregulation of several ICGs, such as OSR2, HAVCR2, TIGIT, PDCD1, LAG3, CTLA4, and CD274, in the high-risk group \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. These findings suggest that high-risk patients are more inclined towards an immunosuppressive tumor microenvironment phenotype. It is worth noting that recent studies have shown that Osr2, as a biomechanical checkpoint, can accelerate the depletion of CD8\u0026thinsp;+\u0026thinsp;T cells in tumors, and Osr2 gene deletion can enhance anti-tumor immune efficacy\u003csup\u003e15\u003c/sup\u003e. The ESTIMATE algorithm showed the high-risk group tended to have higher immuneScore and ESTIMATEScore (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD). Upon evaluating the response to immunotherapy across both groups, we observed higher TIDE scores in the high-risk group compared to the low-risk group. This suggests that patients categorized in the high-risk group have an increased probability of immune evasion (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE). In addition, non-responders had higher risk-score compared to responders (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF). Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eG shows a comparison of response to immunotherapy in the high-low risk group (57% vs 28%). In summary, individuals classified as high-risk tend to exhibit immunosuppressive tumor microenvironments and demonstrate reduced sensitivity to immunotherapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Genetic variation and mutation of membrane tension-related genes in HCC\u003c/h2\u003e\n \u003cp\u003eChromosomal localization analysis showed that cell MT-related factors were located at the locations of CNV mutations in chromosomes (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). In addition, CNV mutations were found to be prevalent in MTRGs, most of which had extensive CNV amplification, among which CRTC2 and SRGAP2 in prognostic models had CNV amplification, while CFL1 had CNV deletion (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB). Among 368 HCC patients, the incidence of gene mutation was 7.88% (29/368). FLNA has the highest mutation rate, followed by DNM2 and CRTC2. Missense mutation is the most common type of mutation (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC). The relationship between MT-related risk score and mutation status was then evaluated using somatic mutation data. It was found that the mutation rate in the high-risk group was higher than that in the low-risk group. TP53 mutations had the highest frequency (42%) in the high-risk group and CTNNB1 mutations had the highest frequency (26%) in the low-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD-E). The Tumor mutation burden (TMB) showed no difference between two groups (P\u0026thinsp;=\u0026thinsp;0.71) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eF). Combined with the synergistic effect of TMB and MT-related risk score on prognosis, stratification prognostic analysis found that patients with low cell membrane tension associated risk score and low TMB combination (L-TMB\u0026thinsp;+\u0026thinsp;low-risk) showed the greatest survival advantage (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eG-H). These data suggest that the combination of cell membrane tension-related risk scores and TMB can further improve patient prognosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Prediction of drug response\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e depicts the outcomes of the drug sensitivity analysis, wherein potential chemotherapy agents were identified based on differences in IC50 measurements between the low-risk and high-risk groups. High-risk subtype of HCC patients for 5 - Fluorouracil, Dasatinib, Osimertinib, Paclitaxel, Afatinib significantly sensitive to chemotherapy drugs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While low-risk groups were sensitive to Sorafenib, Cytarabine, Dihydrorotenone, Axitinib and Mirin (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9 Exploring the Relationship Between Membrane Tension and Immune-Related Prognostic Signature Across Single-Cell Properties\u003c/h2\u003e\n \u003cp\u003eRecently, single-cell sequencing has emerged as a pivotal tool for uncovering cellular diversity and distinctions. In our quest to delve deeper into the functionality of prognostic genes within the tumor microenvironment, we accessed data from the TISCH database (GSE140228) and employed UMAP visualization to delineate 12 distinct cell clusters. Each cluster was annotated based on its characteristic genes, denoted as B, CD4Tconv, CD8T, CD8Tex, DC, ILC, mast, mono/macro, NK, plasma, tprolif, and Tregs. Upon examining the distribution of prognostic marker genes across these clusters, SRGAP2 predominantly appeared in DC and mono/macro clusters, CFL1 was detected across nearly all clusters except plasma, while CRTC2 exhibited lower abundance across all 12 cell clusters (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.10 The expression of MTRGs was verified in GEO data set\u003c/h2\u003e\n \u003cp\u003eWe downloaded the two datasets GSE121248 and GSE45267 from the GEO database. In the GSE121248 dataset, there were 70 HCC tissues and 37 normal tissues. The GSE45267 dataset included 46 HCC tissues and 41 normal tissues. The \u0026quot;limma\u0026quot; package in R was used to analyze the difference in expression of model genes (CRTC2, CFL1, SRGAP2) between HCC tissues and non-tumor tissues. The results showed that the expression of model genes in HCC tissues was higher than that in normal liver tissues in the two datasets (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMore and more researches began to pay attention to the mechanical environment of tumor, and the relationship between cell mechanics and tumor gradually became a new research hotspot\u003csup\u003e29\u0026ndash;32\u003c/sup\u003e. The change of MT may be the root cause of the decrease of cell hardness, which is related to the softening, proliferation and metastasis of cancer cells\u003csup\u003e12,33\u003c/sup\u003e. The metastasis of malignant cells is usually accompanied by changes in cell mechanical properties. A range of evidence show that tumor cells can promote metastasis and alter cell metabolism by reducing MT\u003csup\u003e12,34,35\u003c/sup\u003e. In our study, we investigated the differential expression of MTGRs in HCC patients and identified MTRGs associated with HCC prognosis. Using Lasso Cox analysis, we successfully identified 3 MTRGs (CRTC2, CFL1, SRGAP2) as key genes in a prognostic model and established a prognostic model to classify HCC patients into high-risk and low-risk groups.\u003c/p\u003e \u003cp\u003eCofilin1(CFL1) is an actin-binding protein involved in tissue development, cytoskeleton remodeling and homeostasis by regulating cell membrane tension\u003csup\u003e36\u003c/sup\u003e. Studies have found that CFL1 is highly expressed in various tumor tissues such as pancreatic cancer\u003csup\u003e37\u003c/sup\u003e, triple negative breast cancer (TNBC)\u003csup\u003e38\u003c/sup\u003e and colorectal cancer\u003csup\u003e39\u003c/sup\u003e, and plays a regulatory role in triggering tumor cell transformation, enhancing the ability of cancer cell metastasis, cell division and drug resistance\u003csup\u003e40\u003c/sup\u003e. The Cofilin-1 protein regulates actin dynamics by facilitating actin treadmilling, thus stimulating membrane protrusion and promoting cell migration and invasion\u003csup\u003e41\u003c/sup\u003e. Cofilin-1 signaling can promote actin cytoskeletal recombination and intercellular adhesion regulation mediating epithelial mesenchymal transformation\u003csup\u003e41\u003c/sup\u003e. It had confirmed that CFL1 activates the PLD1/AKT signaling pathway to increase proliferation, migration, invasion and ECM of HCC\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCRTC2 is a member of the CREB-regulated transcription coactivator (CRTC) family. It augments CREB transcriptional activity by interacting with the leucine zipper DNA-binding domain of CREB\u003csup\u003e43\u003c/sup\u003e. The CREB/CRTC2 complex directly regulates the expression of many key genes involved in proliferation and apoptosis\u003csup\u003e44\u003c/sup\u003e. In the liver, CRTC2 exhibits high expression levels, leading to the activation of the CREB pathway. This activation, particularly under fasting conditions, triggers the conversion of glycogen stored in the liver into glucose\u003csup\u003e45,46\u003c/sup\u003e. Recently, numerous studies have explored the potential role of CRTC2 in malignancies, including lymphomas\u003csup\u003e47\u003c/sup\u003e, lung\u003csup\u003e48\u003c/sup\u003e, colorectal\u003csup\u003e49\u003c/sup\u003e, and liver cancers\u003csup\u003e50\u003c/sup\u003e. The research found that CRTC2 is overexpressed in HCC and it drives the malignant phenotype of HCC by activating the Wnt/β-catenin pathway\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSlit-Roundabout (Robo) GTPase activating protein 2 (SRGAP2), belonging to the BDP family, plays a role in inducing outward curvature of cell membranes. Its function involves the interaction with membranes to alter their shape, consequently influencing actin dynamics and thereby controlling processes such as cell migration and differentiation\u003csup\u003e51\u003c/sup\u003e. As a force transduction protein across cell membranes and cytoskeleton, SRGAP2 is involved in malignant cell migration and cancer processes and the high expression of SRGAP2 promotes the migration of HCC cells\u003csup\u003e52,53\u003c/sup\u003e. It had shown that SDC4-PKCα polarization leads to an intracellular tension gradient of SRGAP2, showing extracellular and intracellular physicochemical integration, which is essential for sustained cell migration and cancer progression in rigid substrates\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur research found that high-risk group had a worse prognosis than the low-risk group, and the feasibility of the model was confirmed by external ICGC data sets. ROC method proved that risk scores as a prognostic factor has high sensitivity and specificity. Additionally, our results highlighted its significance as a primary independent prognostic risk factor within clinical variables. Our findings reveal a robust distribution of samples into two distinct subtypes, demonstrating significant correlations with survival outcomes. GSEA analysis showed that Cell Cycle, Cytokine-Cytokine Receptor Interaction and Hematopoietic Cell Lineage were activated in high-risk HCC patients, inducing poor prognosis for high-risk patients. While, the low-risk group was mainly enriched in the metabolism-related pathways.Our results showed significant differences between the two groups in the proportion of certain immune cells in the immune microenvironment, such as activated B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, MDSC, etc. Patients in the high-risk group had higher TIDE and higher ICGs gene expression compared with those in the low-risk group, suggesting a higher immune escape and immunosuppressive microenvironment. Our study showed that there were high mutations in high-risk groups, and patients with high tumor mutation load had poor prognosis. Combined with the synergistic effect of TMB and cell membrane tension-related risk score on prognosis, stratified prognosis analysis found that, Patients with a low cell membrane tension-associated risk score and a combination of low TMB (L-TMB\u0026thinsp;+\u0026thinsp;low-risk) showed the greatest survival advantage. These data suggest that the combination of cell membrane tension-related risk scores and TMB can further improve patient prognosis. Membrane tension has been associated with the antitumor effects of various chemotherapy and targeted agents, including 5-Fluorouracil, Dasatinib, Osimertinib, Paclitaxel, Afatinib, Sorafenib, Cytarabine, Dihydrorotenone, Axitinib, etc. Finally, we obtained HCC samples and normal tissue samples from the GEO database (GSE121248 and GSE45267) and compared the differences in the expression of model genes. The findings indicated elevated expression levels of model genes in hepatocellular carcinoma (HCC) tissues compared to normal tissues across both datasets.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFollowing thorough analyses and validation procedures, our study successfully demonstrated the predictive capability of a prognostic model reliant on the expression levels of three key MTRGs (SRGAP2, CRTC2, CFL1) in assessing the prognosis of hepatocellular carcinoma (HCC) patients. Furthermore, we delved into the immune-level implications of these MTRGs, elucidating their significant roles in the context of HCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TCGA, ICGC ,GEO, TISCH and TIDE database is gratefully acknowledged for the availability of the raw research data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePengfei Zhu proposed the idea, analyzed data and wrote the original draft. Zijuan Zhu is responsible for data screening and drawing pictures.\u0026nbsp;Zheling Chen revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data or codes during the study are available on request from the corresponding author. The RNA-seq data of TCGA_LIHC, ICGC_ LIRI_JP, GSE121248 and GSE45267 were separately from https://portal.gdc.cancer.gov/, https://daco.icgc.org/, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121248 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45267.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H. \u003cem\u003eet al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Theranostics 13, 59\u0026ndash;76, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/thno.77313\u003c/span\u003e\u003cspan address=\"10.7150/thno.77313\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Membrane tension, Hepatocellular carcinoma, Prognostic model, Biomarkers, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4735703/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4735703/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe membrane of tumor epithelial cells is more flexible than normal cells, and higher membrane tension can effectively inhibit the migration and invasion of tumor cells. Innovative therapies targeting the physical characteristics of tumor cells are worthy of attention. To investigate the prognostic value of membrane tension-related genes (MTGRs) in hepatocellular carcinoma (HCC) and its relationship with immunotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained RNA-seq data and clinical characteristics data for HCC from The Cancer Genome Atlas (TCGA) database, the (International Cancer Genome Consortium) ICGC database and GEO database. Combined with univariate Cox regression analysis and LASSO (least absolute shrinkage and selection operator) regression analyses, 3-MTRGs risk model was established. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve were used to verify the model. The Nomogram model was constructed by combining the risk score and clinical characteristics, and its performance was evaluated by calibration curves. We conducted gene differential analysis and functional enrichment analysis on high- and low-risk groups, identifying relevant molecular pathways. Additionally, we analyzed the differences between the two groups in terms of immune cell infiltration, immune-related pathways, and immunotherapy. In addition, we analyzed single-cell sequencing data of HCC patients from the GEO database to study cellular infiltration in the tumor microenvironment and the distribution of model genes across different cell types. Finally, we validated the expression differences of model genes between HCC tissues and normal tissues using the GEO database (GSE121248 and GSE45267).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3-MTRGs (CFL1, CRTC2, SRGAP2) were involved in the model construction, and the prognosis of patients in the low-risk group was better than that in the high-risk group. Kaplan-Meier survival curve and ROC curve illustrated that the model had reliable predictive value. Enrichment analysis showed that high-risk groups were mainly concentrated in the pathways related to TUMOR CELL CYCLE and ECM RECEPTOR INTERACTION. Immuno-correlation analysis of the two groups showed that the high-risk group was associated with immune escape. High-risk HCC patients exhibited notable sensitivity to chemotherapy drugs such as 5 - Fluorouracil, Dasatinib, Osimertinib and Paclitaxel. External data sets showed that the model genes were highly expressed in HCC tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected three MTRGs ( CFL1, CRTC2 and SRGAP2) as prognostic indicators of HCC and established a Nomogram model to predict the prognosis and efficacy of immunotherapy in HCC patients.\u003c/p\u003e","manuscriptTitle":"Construction and immunotherapy analysis of hepatocellular carcinoma prognostic model based on membrane tension-related genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:21:03","doi":"10.21203/rs.3.rs-4735703/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c0cacb8-f6af-47c8-ba6d-a56168416041","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35552474,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":35552475,"name":"Biological sciences/Biophysics/Membrane biophysics"},{"id":35552476,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":35552477,"name":"Biological sciences/Cancer/Cancer models"},{"id":35552478,"name":"Biological sciences/Cancer/Gastrointestinal cancer"},{"id":35552479,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2024-10-10T08:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 01:21:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4735703","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4735703","identity":"rs-4735703","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}