Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide.Immunotherapy has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual differences.Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival rates.Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately considered.AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy.METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways.Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells.The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related features.Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model.Finally,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene signature.RESULTS The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell communication.The effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltration analysis,survival analysis,and expression analysis.CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment,providing insights for personalized treatment strategies.

Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches

Colorectal cancer(CRC)belongs to the class of significantly malignant tumors found in humans.Recently,dysregulated fatty acid metabolism(FAM)has been a topic of attention due to its modulation in cancer,specifically CRC.However,the regulatory FAM pathways in CRC require comprehensive elucidation.Methods:The clinical and gene expression data of 175 fatty acid metabolic genes(FAMGs)linked with colon adenocarcinoma(COAD)and normal cornerstone genes were gathered through The Cancer Genome Atlas(TCGA)-COAD corroborating with the Molecular Signature Database v7.2(MSigDB).Initially,crucial prognostic genes were selected by uni-and multi-variate Cox proportional regression analyses;then,depending upon these identified signature genes and clinical variables,a nomogram was generated.Lastly,to assess tumor immune characteristics,concomitant evaluation of tumor immune evasion/risk scoring were elucidated.Results:A 8-gene signature,including ACBD4,ACOX1,CD36,CPT2,ELOVL3,ELOVL6,ENO3,and SUCLG2,was generated,and depending upon this,CRC patients were categorized within high-risk(H-R)and low-risk(L-R)cohorts.Furthermore,risk and age-based nomograms indicated moderate discrimination and good calibration.The data confirmed that the 8-gene model efficiently predicted CRC patients’prognosis.Moreover,according to the conjoint analysis of tumor immune evasion and the risk scorings,the H-R cohort had an immunosuppressive tumor microenvironment,which caused a substandard prognosis.Conclusion:This investigation established a FAMGs-based prognostic model with substantially high predictive value,providing the possibility for improved individualized treatment for CRC individuals.

Integrative multiomics analysis identifies a metastasis-related gene signature and the potential oncogenic role of EZR in breast cancer

Distant metastasis is a major cause of increased mortality in breast cancer patients,but the mechanisms underlying breast cancer metastasis remain poorly understood.In this study,we aimed to identify a metastasis-related gene(MRG)signature for predicting progression in breast cancer.By screening using three regression analysis methods,a 9-gene signature(NOTCH1,PTP4A3,MMP13,MACC1,EZR,NEDD9,PIK3CA,F2RL1 and CCR7)was constructed based on an MRG set in the BRCA cohort from TCGA.This signature exhibited strong robustness,and its generalizability was verified in the Metabric and GEO cohorts.Of the nine MRGs,EZR is an oncogenic gene with a well-documented role in cell adhesion and cell migration,but it has rarely been investigated in breast cancer.Based on a search of different databases,EZR was found to be significantly more highly expressed in both breast cancer cells and breast cancer tissue.EZR knockdown significantly inhibited cell proliferation,invasion,chemoresistance and EMT in breast cancer.Mechanistically,RhoA activation assays confirmed that EZR knockdown inhibited the activity of RhoA,Rac1 and Cdc42.In summary,we identified a nine-MRG signature that can be used as an efficient prognostic indicator for breast cancer patients,and owing to its involvement in regulating breast cancer metastasis,EZR might serve as a therapeutic target.

WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)is a common immunogenic malignant tumor.Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC,the 5-year survival rate of patients is still very low.The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies.We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes.Weighted gene coexpression network analysis(WGCNA),univariate and multivariate COX regression and the LASSO COX algorithm are employed to identify prognostic-related genes and construct multi-gene signature-based risk model and nomogram for survival prediction.Support vector machine(SVM)based Cibersort’s deconvolution algorithm and gene set enrichment analysis(GSEA)is used to evaluate the changes in tumor immune microenvironment and pathway differences.The study found the favorable prognostic performance of miR-93-3p and identified 389 prognostic-related target genes.The risk model based on a novel 5-gene signature(cct5,cdk4,cenpa,dtnbp1 and flvcr1)was developed and has prominent prognostic significance in the training cohort(P<0.0001)and validation cohort(P=0.0016).The nomogram constructed by combining the gene signature and the AJCC stage further improves the survival prediction ability of the gene signature.The infiltration level of multiple immune cells(especially T cells,B cells and macrophages)were positively correlated with the expression of prognostic signature.In addition,we found that gene markers of T cells and B cells is monitored and regulated by prognostic signature.Meanwhile,several GSEA pathways related to the immune system are enriched in the high-risk group.In general,we integrated the WGCNA,LASSO COX and SVM algorithms to develop and verify 5-gene signatures and nomograms related to immune infiltration to improve the survival prediction of patients.

Comprehensive analysis reveals an arachidonic acid metabolism-related gene signature in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma(PDAC)is highly heterogeneous,making its prognosis prediction difficult.The arachidonic acid(AA)cascade is involved in carcinogenesis.Therefore,the metabolic enzymes of the AA cascade consist of lipoxygenases(LOXs),phospholipase A2s(PLA2s),and cyclooxygenases(COXs)along with their metabolic products,including leukotrienes.Nevertheless,the prognostic potential of AA metabolism-associated PDAC has not been explored.Herein,the mRNA expression patterns and the matching clinical information of individuals with PDAC were abstracted from online data resources.We employed the LASSO Cox regression model to develop a multigene clinical signature in the TCGA queue.The GEO queue and the ICGC queue were employed as the validation queue.There was differential expression of a significant number of AA metabolism-associated genes(56.8%)between PDAC and neighboring nonmalignant tissues in the TCGA queue.Univariate Cox regression demonstrated that 13 of the differentially expressed genes(DEGs)were linked to overall survival(OS)(p<0.05).A 6-gene clinical signature was developed for stratifying the PDAC patients into two risk groups,with the high-risk group patients exhibiting remarkably lower OS than the low-risk group patients(p<0.001 in the TCGA data set and the ICGC queue,and p=0.001 in the GEO data set).The multivariate Cox data revealed the risk score as an independent OS predictor(HR>1,p<0.01).The receiver operating characteristic(ROC)curve verified the predictive potential of our signature.The expression and alteration of the six genes in PDAC were also validated using online databases.Functional analyses demonstrated that immune-linked cascades were enriched,and the immune status was remarkably different between the high-and low-risk groups.In summary,an AA metabolism-associated clinical gene signature can be applied for prognostic estimation in PDAC.

Development and validation of an epithelial–mesenchymal transition-related gene signature for predicting prognosis

BACKGROUND Currently,there are many therapeutic methods for lung adenocarcinoma(LUAD),but the 5-year survival rate is still only 15%at later stages.Epithelial–mesenchymal transition(EMT)has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors.However,the role of EMT in the occurrence and development of LUAD remains unclear.AIM To further elucidate the value of EMT-related genes in LUAD prognosis.METHODS Univariate,least absolute shrinkage and selection operator,and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis.The risk model was evaluated by Kaplan–Meier survival analysis,principal component analysis,and functional enrichment analysis and was used for nomogram construction.The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model.RESULTS Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival(OS)by using univariate Cox regression analysis(log2FC≥1,false discovery rate<0.001).A prognostic signature of 7EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores.Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group(P<0.05).Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS(HR>1,P<0.05).The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis(all area under the curves>0.5).CONCLUSION The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND Breast cancer(BC),a leading malignant disease,affects women all over the world.Cancer associated fibroblasts(CAFs)stimulate epithelial-mesenchymal transition,and induce chemoresistance and immunosuppression.AIM To establish a CAFs-associated prognostic signature to improve BC patient out-come estimation.METHODS We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas(TCGA)databases,and 3661 BC samples from the The Gene Expression Omnibus.CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm.CAF-associated gene identification was done by weighted gene co-expression network analysis.A CAF risk signature was established via univariate,least absolute shrinkage and selection operator regression,and mul-tivariate Cox regression analyses.The receiver operating characteristic(ROC)and Kaplan-Meier curves were employed to evaluate the predictability of the model.Subsequently,a nomogram was developed with the risk score and patient clinical signature.Using Spearman's correlations analysis,the relationship between CAF risk score and gene set enrichment scores were examined.Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS Employing an 8-gene(IL18,MYD88,GLIPR1,TNN,BHLHE41,DNAJB5,FKBP14,and XG)signature,we attemp-ted to estimate BC patient prognosis.Based on our analysis,high-risk patients exhibited worse outcomes than low-risk patients.Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis.ROC analysis exhibited satisfactory nomogram predictability.The area under the curve showed 0.805 at 3 years,and 0.801 at 5 years in the TCGA cohort.We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes.CAF risk score was significantly correlated with most hallmark gene sets.Finally,the prognostic signature were further validated by qRT-PCR.CONCLUSION We introduced a newly-discovered CAFs-associated gene signature,which can be employed to estimate BC patient outcomes conveniently and accurately.

Analysis of gene expression profile for identification of novel gene signatures during dengue infection

Background:Dengue is a major arthropod-borne viral disease spreading rapidly across the globe.The absence of vaccines and inadequate vector control measures leads to further expansion of dengue in many regions globally.Hence,the identification of genes involved in the pathogenesis of dengue will help to understand the molecular basis of the disease and the genes responsible for the disease progression.Methods:In the present study,a meta-analysis was carried out using dengue gene expression data obtained from Gene Expression Omnibus repository.The differentially expressed genes such as CCNB1 and CCNB2(G2/mitotic-specific cyclin-B2 and B1)were upregulated in dengue fever to control(DF-CO)and severe dengue(dengue hemorrhagic fever[DHF])to control(DHF-CO)were identified as key genes for controlling the major pathways(cell cycle,oocyte meiosis,p53 signaling pathway,cellular senescence and progesterone-mediated oocyte mat-uration).Similarly,interferon alpha-inducible(IFI27)genes,type-I and type-III interferon(IFN)signaling genes(STAT1 and STAT2),B cell activation and survival genes(TNFSF13B,TNFRSF17)and toll like receptor(TLR7)genes were differentially up activated during DF-CO and DHF-CO.Followed by,Cytoscape was used to identify the immune system process and topological analysis.Results:The results showed that the top differentially expressed genes under the statistical significance p<0.001,which is majorly involved in Kyoto Encyclopedia of Genes and Genomes orthology K05868 and K21770 with gene names CCNB1 and CCNB2.In addition to this,the immune system profile showed up-regulation of IL12A,CXCR3,TNFSF13B,IFI27,TNFRSF17,STAT,STAT2,and TLR7 genes in DF-CO and DHF-CO act as immunological signatures for inducing the immune response towards dengue infection.Conclusions:The current study could aid in understanding of molecular pathogenesis,genes and correspond-ing pathway upon dengue infection,and could facilitate for identification of novel drug targets and prognostic markers.

Development and validation of novel inflammatory response-related gene signature for sepsis prognosis

Due to the low specificity and sensitivity of biomarkers in sepsis diagnostics,the prognosis of sepsis patient outcomes still relies on the assessment of clinical symptoms.Inflammatory response is crucial to sepsis onset and progression;however,the significance of inflammatory response-related genes(IRRGs)in sepsis prognosis is uncertain.This study developed an IRRG-based signature for sepsis prognosis and immunological function.The Gene Expression Omnibus(GEO)database was retrieved for two sepsis microarray datasets,GSE64457 and GSE69528,followed by gene set enrichment analysis(GSEA)comparing sepsis and healthy samples.A predictive signature for IRRGs was created using least absolute shrinkage and selection operator(LASSO).To confirm the efficacy and reliability of the new prognostic signature,Cox regression,Kaplan-Meier(K-M)survival,and receiver operating characteristic(ROC)curve analyses were performed.Subsequently,we employed the GSE95233 dataset to independently validate the prognostic signature.A single-sample GSEA(ssGSEA)was conducted to quantify the immune cell enrichment score and immune-related pathway activity.We found that more gene sets were enriched in the inflammatory response in sepsis patient samples than in healthy patient samples,as determined by GSEA.The signature of nine IRRGs permitted the patients to be classified into two risk categories.Patients in the low-risk group showed significantly better 28-d survival than those in the high-risk group.ROC curve analysis corroborated the predictive capacity of the signature,with the area under the curve(AUC)for 28-d survival reaching 0.866.Meanwhile,the ss GSEA showed that the two risk groups had different immune states.The validation set and external dataset showed that the signature was clinically predictive.In conclusion,a signature consisting of nine IRRGs can be utilized to predict prognosis and influence the immunological status of sepsis patients.Thus,intervention based on these IRRGs may become a therapeutic option in the future.

A novel gene signature based on five immune checkpoint genes predicts the survival of glioma

Background:Glioma is the most common and fatal type of nerve neoplasm in the central nervous system.Several biomarkers have been considered for prognosis prediction,which is not accurate enough.We aimed to carry out a gene signature related to the expression of immune checkpoints which was enough for its performance in prediction.Methods:Gene expression of immune checkpoints in TGGA database was filtrated.The 5 selected genes underwent verification by COX and Lasso-COX regression.Next,the selected genes were included to build a novel signature for further analysis.Results:Patients were sub-grouped into high and low risk according to the novel signature.Immune response,clinicopathologic characters,and survival showed significant differences between those 2 groups.Terms including“naive,”“effector,”and“IL-4”were screened out by GSEA.The results showed strong relevance between the signature and immune response.Conclusions:We constructed a gene signature with 5 immune checkpoints.The signature predicted survival effectively.The novel signature performed more functional than previous biomarkers.

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)patients.METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)cohort.Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic factors.iCEMIGE was built upon the sparse representation learning technique.The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS alone.Nomogram models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA cohort.RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs.CMPS,GEPS,and MAPS were found to be significantly independently associated with OS.We then established an iCEMIGE scoring system for risk stratification of BC patients.The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular subtype.Importantly,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS alone.CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.

High Accuracy Gene Signature for Chemosensitivity Prediction in Breast Cancer

Neoadjuvant chemotherapy for breast cancer patients with large tumor size is a necessary treatment.After this treatment patients who achieve a pathologic Complete Response（p CR） usually have a favorable prognosis than those without. Therefore, p CR is now considered as the best prognosticator for patients with neoadjuvant chemotherapy. However, not all patients can benefit from this treatment. As a result, we need to find a way to predict what kind of patients can induce p CR. Various gene signatures of chemosensitivity in breast cancer have been identified, from which such predictors can be built. Nevertheless, many of them have their prediction accuracy around 80%. As such, identifying gene signatures that could be employed to build high accuracy predictors is a prerequisite for their clinical tests and applications. Furthermore, to elucidate the importance of each individual gene in a signature is another pressing need before such signature could be tested in clinical settings. In this study, Genetic Algorithm（GA） and Sparse Logistic Regression（SLR） along with t-test were employed to identify one signature. It had 28 probe sets selected by GA from the top 65 probe sets that were highly overexpressed between p CR and Residual Disease（RD） and was used to build an SLR predictor of p CR（SLR-28）. This predictor tested on a training set（n = 81） and validation set（n = 52） had very precise predictions measured by accuracy,specificity, sensitivity, positive predictive value, and negative predictive value with their corresponding P value all zero. Furthermore, this predictor discovered 12 important genes in the 28 probe set signature. Our findings also demonstrated that the most discriminative genes measured by SLR as a group selected by GA were not necessarily those with the smallest P values by t-test as individual genes, highlighting the ability of GA to capture the interacting genes in p CR prediction as multivariate techniques. Our gene signature produced superior performance over a signature found in one previous study with prediction accuracy 92% vs 76%, demonstrating the potential of GA and SLR in identifying robust gene signatures in chemo response prediction in breast cancer.

A bendamustine resistance gene signature in diffuse large B-cell lymphoma and multiple myeloma

Aim:Bendamustine is primarily used for treatment of indolent lymphomas but has shown efficacy in some patients with diffuse large B-cell lymphoma(DLBCL)and multiple myeloma(MM).Molecular-based patient stratification for identification of resistant patients,who will benefit from alternative treatments,is important.The aim of this study was to develop a resistance gene signature(REGS)from bendamustine dose-response assays in cultures of DLBCL and MM cell lines,enabling prediction of bendamustine response in DLBCL and MM patients.Methods:Bendamustine response was determined in 14 DLBCL and 11 MM cell lines.Using baseline gene expression profiles and degree of growth inhibition after bendamustine exposure,a bendamustine REGS was developed and examined for the risk stratification potential in DLBCL(n=971)and MM(n=1,126)patients divided into prognostic subtypes.Results:Bendamustine resistance significantly correlated with resistance to cyclophosphamide in DLBCL and melphalan in MM cell lines.The bendamustine REGS showed significantly lower bendamustine resistance probabilities in DLBCL patients with GCB subtype tumors and in tumors of the differentiation dependent centrocyte and plasmablast subtypes.In MM patients,pre-BII classified tumors displayed high bendamustine resistance probabilities and the plasma cell subtype had lower bendamustine resistance probability than memory cells.Furthermore,tumors belonging to the 4p14,MAF,and D2 TC subclasses consistently displayed high bendamustine resistance probabilities.Conclusion:Significant differences in predicted response to bendamustine were found in molecular subtypes of DLBCL and MM,encouraging validation in prospective bendamustine-treated cohorts with available gene expression profiles and follow-up data.

Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment

Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients,with evidence suggesting exercise may reduce mortality risks and aid neural regeneration.The role of the small ubiquitin-like modifier(SUMO)protein,especially post-exercise,in cancer progression,is gaining attention,as are the potential anti-cancer effects of SUMOylation.We used machine learning to create the exercise and SUMO-related gene signature(ESLRS).This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers.We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers,specifically highlighting how murine double minute 2(MDM2),a component of the ESLRS,can be targeted by nutlin-3.This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation.Using comprehensive CRISPR screening,we validated the effects of specific ESLRS genes on low-grade glioma progression.We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation.Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway.Its efficacy decreased with MDM2 overexpression,and this was reversed by Nutlin-3a or exercise.Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation.Notably,both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells.These results suggest the potential for Nutlin-3a,an MDM2 inhibitor,with physical exercise as a therapeutic approach for glioma management.Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise,natural products,and immune regulation in cancer treatment.

Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer

BACKGROUND Pancreatic cancer is a highly heterogeneous disease,making prognosis prediction challenging.Altered energy metabolism to satisfy uncontrolled proliferation and metastasis has become one of the most important markers of tumors.However,the specific regulatory mechanism and its effect on prognosis have not been fully elucidated.AIM To construct a prognostic polygene signature of differentially expressed genes(DEGs)related to lipid metabolism.METHODS First,9 tissue samples from patients with pancreatic cancer were collected and divided into a cancer group and a para-cancer group.All patient samples were subjected to metabolomics analysis based on liquid tandem chromatography quadrupole time of flight mass spectrometry.Then,mRNA expression profiles and corresponding clinical data of pancreatic cancer were downloaded from a public database.Least absolute shrinkage and selection operator Cox regression analysis was used to construct a multigene model for The Cancer Genome Atlas.RESULTS Principal component analysis and orthogonal projections to latent structuresdiscriminant analysis(OPLS-DA)based on lipid metabolomics analysis showed a clear distribution in different regions.A Euclidean distance matrix was used to calculate the quantitative value of differential metabolites.The permutation test of the OPLS-DA model for tumor tissue and paracancerous tissue indicated that the established model was consistent with the actual condition based on sample data.A bar plot showed significantly higher levels of the lipid metabolites phosphatidy-lcholine(PC),phosphatidyl ethanolamine(PE),phosphatidylethanol(PEtOH),phosphatidylmethanol(PMeOH),phosphatidylserine(PS)and diacylglyceryl trimethylhomoserine(DGTS)in tumor tissues than in paracancerous tissues.According to bubble plots,PC,PE,PEtOH,PMeOH,PS and DGTS were significantly higher in tumor tissues than in paracancerous tissues.In total,12.3%(25/197)of genes related to lipid metabolism were differentially expressed between tumor tissues and adjacent paracancerous tissues.Six DEGs correlated with overall survival in univariate Cox regression analysis(P<0.05),and a 4-gene signature model was developed to divide patients into two risk groups,with patients in the high-risk group having significantly lower overall survival than those in the low-risk group(P<0.05).ROC curve analysis confirmed the predictive power of the model.CONCLUSION This novel model comprising 4 lipid metabolism-related genes might assist clinicians in the prognostic evaluation of patients with pancreatic cancer.

Pyroptosis-related genes play a significant role in the prognosis of gastric cancer

BACKGROUND Pyroptosis is an inflammatory form of programmed cell death,which has been shown to be related to the prognosis of many tumors.However,its role in gastric cancer(GC)is not fully understood.AIM To evaluate the expression of pyroptosis-related genes in GC and its correlation with prognosis.METHODS We constructed prognostic multigene markers of differentially expressed genes associated with pyroptosis by least absolute contraction and selection operator Cox regression.The risk model was analyzed by Kaplan-Meier curve,two-sided log-rank test and functional enrichment analysis.RESULTS Sixty-three pyroptosis-related genes were differentially expressed in tumor tissues and adjacent nontumor tissues.Based on these differentially expressed genes,5 gene signature were constructed and all GC patients were classified into two risk groups.Kaplan-Meier survival curve showed that the overall survival(OS)of patients in the high-risk group was significantly lower than that of the low-risk group.Multivariate Cox regression analyses showed that the risk score was an independent risk factor for OS.Receiver operating characteristic curve analysis confirmed the predictive ability of the model.External validation indicated increased OS in the low-risk group.The immune function and immune cell scores of the high-risk group were generally higher than those of the low-risk group.CONCLUSION Pyroptosis-related genes play a significant role in tumor immune microenvironment.This novel model,which contains 5 pyroptosis-related genes,is an independent predicting factor for OS in GC patients,and may help to evaluate the prognosis of GC.

Development of fibrotic gene signature and construction of a prognostic model in melanoma

Purpose Skin cutaneous melanoma(SKCM)is a malignant tumor responsible for over 75%of skin cancer deaths,the relationship between fibrosis and cancer has been increasingly appreciated.The aim of this study is to investigate the fibrotic gene signature(FGS)in melanoma and construct a prognostic model based on FGS.Methods SKCM-related datasets were obtained from the Gene Expression Omnibus(GEO)database and The Cancer Genome Atlas(TCGA)database.By weighted gene co-expression network analysis(WGCNA)of the TCGA-SKCM cohort and GSE65904 cohort,core modules and central genes highly associated with fibrotic features were identified and intersecting genes were defined as fibrotic gene signature(FGS).The least absolute shrinkage and selection operator(LASSO)regression analysis and the Akaike information criterion(AIC)method were conducted to construct a prognostic model based on the FGS gene set.The fibrotic gene signature enrichment score(FGES)and fibrotic gene signature risk score(FGRS)were used to analyze immune infiltration.For FGRS,the correlation between clinical characteristics and the expression of immune checkpoint genes between different risk groups was also analyzed in depth.Results A total of 301 genes were defined as FGS,and a robust eight-gene prediction model was constructed based on FGS,these 8 genes are SV2A,HEYL,OLFML2A,PROX1,ACOX2,PRRX1,PHACTR1 and LHX6.On the basis of the model,a nomogram consisting of FGRS could accurately predict prognosis.In addition,patients in the high-risk group showed immunosuppression,while patients in the low-risk group may benefit more from immunotherapy.However,there was no significant difference between the immune infiltration of different FGES groups.Conclusion In this study,taken together,we developed a fibrotic gene signature in melanoma,and construct an eight-gene prognostic model based on the FGS to provide a reference for prognosis estimation and treatment selection for melanoma patients.

Integrative analysis of platelet-related genes for the prognosis of esophageal cancer

BACKGROUND Every year,esophageal cancer is responsible for 509000 deaths and around 572000 new cases worldwide.Although esophageal cancer treatment options have advanced,patients still have a dismal 5-year survival rate.AIM To investigate the relationship between genes associated to platelets and the prognosis of esophageal cancer.METHODS We searched differentially expressed genes for changes between 151 tumor tissues and 653 normal,healthy tissues using the“limma”package.To develop a prediction model of platelet-related genes,a univariate Cox regression analysis and least absolute shrinkage and selection operator Cox regression analysis were carried out.Based on a median risk score,patients were divided into high-risk and low-risk categories.A nomogram was created to predict the 1-,2-,and 3-year overall survival(OS)of esophageal cancer patients using four platelet-related gene signatures,TNM stages,and pathological type.Additionally,the concordance index,receiver operating characteristic curve,and calibration curve were used to validate the nomogram.RESULTS The prognosis of esophageal cancer was associated to APOOL,EP300,PLA2G6,and VAMP7 according to univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis.Patients with esophageal cancer at high risk had substantially shorter OS than those with cancer at low risk,according to a Kaplan-Meier analysis(P<0.05).TNM stage(hazard ratio:2.187,95%confidence interval:1.242-3.852,P=0.007)in both univariate and multivariate Cox regression and risk score were independently correlated with OS(hazard ratio:2.451,95%confidence interval:1.599-3.756,P<0.001).CONCLUSION A survival risk score model and independent prognostic variables for esophageal cancer have been developed using APOOL,EP300,PLA2G6,and VAMP7.OS for esophageal cancer might be predicted using the nomogram based on TNM stage,pathological type,and risk score.The nomogram demonstrated strong predictive ability,as shown by the concordance index,receiver operating characteristic curve,and calibration curve.

Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease

Inflammatory bowel disease(IBD)is a chronic inflammatory condition caused by multiple genetic and environmental factors.Numerous genes are implicated in the etiology of IBD,but the diagnosis of IBD is challenging.Here,XGBoost,a machine learning prediction model,has been used to distinguish IBD from healthy cases following elaborative feature selection.Using combined unsupervised clustering analysis and the XGBoost feature selection method,we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy.The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system.The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status.Therefore,this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.

Cellular senescence throws new insights into patient classification and pharmacological interventions for clinical management of hepatocellular carcinoma

BACKGROUND Cellular senescence,a state of stable growth arrest,is intertwined with human cancers.However,characterization of cellular senescence-associated phenotypes in hepatocellular carcinoma(HCC)remains unexplored.AIM To address this issue,we delineated cellular senescence landscape across HCC.METHODS We enrolled two HCC datasets,TCGA-LIHC and International Cancer Genome Consortium(ICGC).Unsupervised clustering was executed to probe tumor heterogeneity based upon cellular senescence genes.Least absolute shrinkage and selection operator algorithm were utilized to define a cellular senescence-relevant scoring system.TRNP1 expression was measured in HCCs and normal tissues through immunohistochemistry,immunoblotting and quantitative real-time polymerase chain reaction.The influence of TMF-regulated nuclear protein(TRNP)1 on HCC senescence and growth was proven via a series of experiments.RESULTS TCGA-LIHC patients were classified as three cellular senescence subtypes,named C1–3.The robustness and reproducibility of these subtypes were proven in the ICGC cohort.C2 had the worst overall survival,C1 the next,and C3 the best.C2 presented the highest levels of immune checkpoints,abundance of immune cells,and immunogenetic indicators.Thus,C2 might possibly respond to immunotherapy.C2 had the lowest somatic mutation rate,while C1 presented the highest copy number variations.A cellular senescence-relevant gene signature was generated,which can predict patient survival,and chemo-or immunotherapeutic response.Experimentally,it was proven that TRNP1 presented the remarkable upregulation in HCCs.TRNP1 knockdown induced apoptosis and senescence of HCC cells and attenuated tumor growth.CONCLUSION These findings provide a systematic framework for assessing cellular senescence in HCC,which decode the tumor heterogeneity and tailor the pharmacological interventions to improve clinical management.