5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND Lung adenocarcinoma(LUAD)is the most common non-small-cell lung cancer,with a high incidence and a poor prognosis.AIM To construct effective predictive models to evaluate the prognosis of LUAD patients.METHODS In this study,we thoroughly mined LUAD genomic data from the Gene Expression Omnibus(GEO)(GSE43458,GSE32863,and GSE27262)and the Cancer Genome Atlas(TCGA)datasets,including 698 LUAD and 172 healthy(or adjacent normal)lung tissue samples.Univariate regression and LASSO regression analyses were used to screen differentially expressed genes(DEGs)related to patient prognosis,and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model.Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.RESULTS A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets,and 5 DEGs(TCN1,CENPF,MAOB,CRTAC1 and PLEK2)were screened out by multivariate Cox regression analysis,indicating that the prognostic risk model could be used as an independent prognostic factor(Hazard ratio=1.520,P<0.001).Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity(Area under the curve=0.754,0.737).Combining genetic models and clinical prognostic factors,nomograms can also predict overall survival more effectively.CONCLUSION A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma,which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Investigating the Immunogenic Cell Death‑Dependent Subtypes and Prognostic Signature of Triple‑Negative Breast Cancer

Recently,immunotherapy has emerged as a promising and efective method for treating triple-negative breast cancer(TNBC).However,challenges still persist.Immunogenic cell death(ICD)is considered a prospective treatment and potential combinational treatment strategy as it induces an anti-tumor immune response by presenting the antigenic epitopes of dead cells.Nevertheless,the ICD process in TNBC and its impact on disease progression and the response to immunotherapy are not well understood.In this study,we observed dysregulation of the ICD process and verifed the altered expression of prognostic ICD genes in TNBC through quantitative real-time polymerase chain reaction(qRT-PCR)analysis.To investigate the potential role of the ICD process in TNBC progression,we determined the ICD-dependent subtypes,and two were identifed.Analysis of their distinct tumor immune microenvironment(TIME)and cancer hallmark features revealed that Cluster 1 and 2 corresponded to the immune“cold”and“hot”phenotypes,respectively.In addition,we constructed the prognostic signature ICD score of TNBC patients and demonstrated its clinical independence and generalizability.The ICD score could also serve as a potential biomarker for immune checkpoint blockade and may aid in the identifcation of targeted efective agents for individualized clinical strategies.

Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

Background and Aims:The immune system plays vital roles in hepatocellular carcinoma(HCC)initiation and progression.The present study aimed to construct an immune-gene related prognostic signature(IRPS)for predicting the prognosis of HCC patients.Methods:Gene expression data were retrieved from The Cancer Genome Atlas database.The IRPS was established via least absolute shrinkage and selection operator(LASSO)and multivariate Cox regression analysis.The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium(ICGC)dataset.Results:A total of 62 genes were identified as candidate immune-related prognostic genes.According to the results of Lasso and multivariate Cox regression analysis,we established an IRPS and confirmed its stability and reliability in the ICGC dataset.The IRPS was significantly associated with advanced clinicopathological characteristics.Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients.The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration.Furthermore,a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions:The IRPS was effective for predicting prognosis of HCC patients,which might serve as novel prognostic and therapeutic biomarkers for HCC.

A novel prognostic target-gene signature and nomogram based on an integrated bioinformatics analysis in hepatocellular carcinoma

There is currently no effective solution to the problem of poor prognosis and recurrence of HCC.The technology of immunotherapy and prognosis of genetic material has made continuous progress in recent years.In the study,a 5-gene signature was established for the prognosis of HCC through biological information,and the immune infiltration of HCC patients was studied.After studied HCC patients’immune infiltration,the paper screened the differential target genes of miR-126-3p in HCC downloaded from TCGA database,and uses WGCNA method to select the modular genes highly relevant to M2 macrophage.Then we use LASSO and COX regression analysis technology to establish the 5-gene signature.The nomogram is established by combining the prognostic score and clinical phenotype.Cibersort was empolyed to observe the immune infiltration in HCC patients.We revealed the biological pathways of HCC-related genes through GSEA and Metascape.The bioinformatics analysis of 2495 differential target genes finally constructed a 5-gene signature with a reliable prognostic ability(CDCA8,SLC41A3,PPM1G,TCOF1,GRPEL2).The combination of prognostic score and AJCC_Stage resulted in a more reliable prognosis ability.At the same time,10 immune cells that are differentially expressed in HCC patients were also found.8 GSEA pathways related to the prognosis were found.In the study,a reliable 5-gene signature was established based on the differential target gene of miR-126-3p to study the immune infiltration in HCC patients.It provides help for HCC-related prognosis research and immunotherapy.

Transient receptor potential channels as predictive marker and potential indicator of chemoresistance in colon cancer

Transient receptor potential(TRP)channels are strongly associated with colon cancer development and progression.This study leveraged a multivariate Cox regression model on publicly available datasets to construct a TRP channels-associated gene signature,with further validation of signature in real world samples from our hospital treated patient samples.Kaplan-Meier(K-M)survival analysis and receiver operating characteristic(ROC)curves were employed to evaluate this gene signature’s predictive accuracy and robustness in both training and testing cohorts,respectively.Additionally,the study utilized the CIBERSORT algorithm and single-sample gene set enrichment analysis to explore the signature’s immune infiltration landscape and underlying functional implications.The support vector machine algorithm was applied to evaluate the signature’s potential in predicting chemotherapy outcomes.The findings unveiled a novel three TRP channels-related gene signature(MCOLN1,TRPM5,and TRPV4)in colon adenocarcinoma(COAD).The ROC and K-M survival curves in the training dataset(AUC=0.761;p=1.58e-05)and testing dataset(AUC=0.699;p=0.004)showed the signature’s robust predictive capability for the overall survival of COAD patients.Analysis of the immune infiltration landscape associated with the signature revealed higher immune infiltration,especially an increased presence of M2 macrophages,in high-risk group patients compared to their low-risk counterparts.High-risk score patients also exhibited potential responsiveness to immune checkpoint inhibitor therapy,evident through increased CD86 and PD-1 expression profiles.Moreover,the TRPM5 gene within the signature was highly expressed in the chemoresistance group(p=0.00095)and associated with poor prognosis(p=0.036)in COAD patients,highlighting its role as a hub gene of chemoresistance.Ultimately,this signature emerged as an independent prognosis factor for COAD patients(p=6.48e-06)and expression of model gene are validated by public data and real-world patients.Overall,this bioinformatics study provides valuable insights into the prognostic implications and potential chemotherapy resistance mechanisms associated with TRPs-related genes in colon cancer.

Prediction of prognosis,immune escape and drug sensitivity of lung adenocarcinoma based on Cuproptosis-related LncRNA

Background:Lung Adenocarcinoma(LUAD)is the leading cause of death from lung cancer.Cuproptosis is the latest discovered way of programmed cell death,and Cuproptosis-Related Gene(CRG)is associated with the risk of LUAD.At present,there are few research of LUAD and Cuproptosis focuses on Long non-coding RNA(LncRNA).As genomics advances,LncRNA emerges as a potential target for understanding tumor progression and prognosis,offering prospects for biological targeted therapy.Therefore,this study provides new biomarkers and therapeutic targets for LUAD from the perspective of LncRNA.Methods:Gene expression,clinical outcome and gene mutation data of LUAD patients were downloaded from TCGA database.Spearman correlation was used to analyze the correlation between LncRNA and CRG.Univariate Cox,multivariate Cox and LASSO Cox regression analysis were used to construct a prognostic model of Cuproptosis-LncRNAs.GO and KEGG enrichment and immune function analysis were performed on differentially expressed genes between different risk groups.Then,immune escape analysis was performed on LUAD patients with different TIDE score.Finally,drug sensitivity analysis was performed on these differentially expressed genes.Results:A total of 2244 Cuproptosis-LncRNAs were found.Through the application of univariate Cox regression analysis,multivariate Cox regression analysis,and LASSO Cox regression analysis,a prognostic model was developed,integrating 15 Cuproptosis-LncRNAs to assess the risk of mortality.Following that,the model underwent assessment through risk score analysis,Kaplan-Meier survival analysis,risk distribution,and evaluation of survival outcomes.The results revealed an AUC value of 0.755 for the model,surpassing the AUC of other clinical pathological features.The results of KEGG analysis showed that the differentially expressed genes in different model groups were mainly involved in Amoebiasis,Fat digestion and absorption,and other signaling pathways.The results of TMB showed that the prognostic model of TMB combined with risk score could well evaluate the prognosis of patients.The TIDE scores did not exhibit a notable distinction between the two risk models.Analysis of drug sensitivity revealed that individuals in the low-risk category demonstrated greater responsiveness to 5-Fluorouracil,Axitinib,Bexarotene,and other drugs compared to those in the high-risk group.Conclusion:Our research offers a valuable reference for predicting the prognosis of LUAD,contributing to a better understanding of the future elucidation of the process and mechanism of Cuproptosis-LncRNAs in LUAD.

Construction of prognostic model of cervical cancer based on necroptosis-related lncRNAs

Background:Long non-coding RNAs(lncRNAs)can influence the necroptosis process,which is essential in malignant tumors.But no studies have looked at the predictive value of necroptosis-related lncRNAs in cervical cancer(CC).Using necroptosis-related lncRNAs,we developed a prediction signature to predict the prognosis of CC patients.Method:We gathered the RNA-seq and related clinical data for patients with CC from the Cancer Genome Atlas(TCGA)database.To identify lncRNAs linked to necroptosis,we then conducted univariate and multivariate Cox regression analyses.Co-expression network analysis,least absolute shrinkage selection operator(LASSO)regression analysis and multivariate Cox regression analyses were subsequently used to further filter necroptosis-related lncRNAs signature and construct a predictive model.Finally,we examined the medication sensitivity between the two risk groups.We performed a single-sample gene set enrichment analysis(ssGSEA)to investigate the association between the predictive signature and the tumor immune microenvironment.Result:Six necroptosis-related lncRNAs(AL021807.1,AC026803.2,AC015819.1,AC233728.1,AL158166.1,NKILA)that are independently connected to the overall survival(OS)time of CC patients make up the signature we created.For predicting the 1-,3-,and 5-year survival rates,the areas under the receiver operating characteristic(ROC)curve(AUC)were 0.72,0.791,and 0.808.It was determined that the risk score model was a separate prognostic component.The Kaplan-Meier analysis revealed that the prognosis for CC patients in the high-risk category was worse.Low-risk CC patients had more active immune systems and responded better to PD1/L1 immunotherapy.Conclusion:The signature based on necroptosis-related lncRNAs is a reliable biomarker,which is more likely to independently predict the prognosis of patients with CC and offer a foundation for the pathogenesis of necroptosis-related lncRNAs in CC.It can also be utilized to direct the tailored therapy of CC patients.

A novel prognosis signature based on two cuprotosis-related genes for kidney renal clear cell carcinoma

Background:Cuprotosis is a newly discovered Copper-dependence form of cell death.Cuprotosis-related genes(CRGs)regulating mitochondrial metabolism and protein lipoylation suggest the critical roles cuprotosis for in cancer.However,the prognostic value of CRGs in the highly immunogenic cancer type,kidney renal clear cell carcinoma(KIRC)needs to be further studied.Herein,we aim to identify novel prognostic genes and construct a CRGs prognostic signature for KIRC.Methods:We downloaded the mRNA sequencing data from the Cancer Genome Atlas,differentially expressed CRGs were screened out and their bio-function was elucidated.Then we used Cox regression analysis to establish a prediction model of CRGs.Subsequently,a prognostic scoring model based on the regression coefficients of the screened out CRGs and their corresponding mRNA expressions were constructed and validated.Results:Seven differentially expressed CRGs were screened.A two-gene model was built to separate samples into high-risk and low-risk groups.Overall survival was lower in the high-risk group than in the low-risk group(p<0.05).The receiver operating characteristic curve showed a good diagnostic efficiency of the signature.We verified this prognostic model in the Cancer Genome Atlas test cohorts.The risk score was identified as an independent prognostic factor via multivariate Cox regression.Moreover,the nomogram was used to predict 1-/3-/5-year OS of KIRC patients.Furthermore,risk score has a very significant effect on the infiltration of immune cells and the expression of immune checkpoints.Conclusions:To conclude,we constructed a novel prognostic signature based on CRGs.Targeting cuprotosis may represent a promising approach for the treatment of KIRC.

A model based on eight iron metabolism-related genes accurately predicts acute myeloid leukemia prognosis

Purpose:Iron metabolism maintains the balance between iron absorption and excretion.Abnormal iron metabolism can cause numerous diseases,including tumor.This study determined the iron metabolism-related genes(IMRGs)signature that can predict the prognosis of acute myeloid leukemia(AML).The roles of these genes in the immune microenvironment were also explored.Methods:A total of 514 IMRGs were downloaded from the Molecular Characteristics Database(MSigDB).IMRGs related to AML prognosis were identified using Cox regression and LASSO analyses and were used to construct the risk score model.AML patients were stratified into high-risk groups(cluster 1)and low-risk groups(cluster 2)based on the mean value of the risk score.The accuracy and prognosis prediction potential of the risk-score model was evaluated using Kaplan-Meier and receiver operating characteristics analysis.The stromal score,immune scores,and immune cells infiltrated in AML samples were estimated using CIBERSORT,MCPcountre,and Xcell algorithms.The role of immune checkpoint genes in the AML microenvironment and the prognostic value of the IMRGs were also evaluated.Results:An AML prognosis prediction model was established based on the eight most critical IMRGs.Further analyses revealed that the model could accurately predict AML prognosis.The expression of IMRGs correlated with the infiltration of several immune cells and could influence response to certain chemotherapy drugs and immunotherapy.Conclusion:A model based on IMRGs can accurately predict the overall survival and disease-free survival of AML patients.

Comprehensive analysis of the role of immune-related PANoptosis lncRNA model in renal clear cell carcinoma based on RNA transcriptome and single-cell sequencing

The high immune infiltration and heterogeneity of the microenvironment in clear cell renal cell carcinoma(ccRCC)result in the variability of prognosis and clinical response.While PANoptosis has strong immunogenicity and is worthy of further study.In this study,data from The Cancer Genome Atlas database was used to obtain immune-related PANoptosis lncRNAs with prognostic value.Subsequently,the role of these lncRNAs in cancer immunity,progression and the therapeutic response was analyzed,and a new prediction model was constructed.Additionally,we further explored the biological value of PANoptosis-related lncRNAs using single-cell data from the Gene Expression Omnibus database.PANoptosis-associated lncRNAs were significantly associated with clinical outcome,immune infiltration,antigen presentation and treatment response in ccRCC.Notably,the risk model,which is based on these immune-related PANoptosis lncRNAs,showed good predictive performance.Subsequent studies on LINC00944 and LINC02611 revealed their high expression in ccRCC and significant correlation with cancer cell migration and invasion.Single-cell sequencing further validated these results and revealed the potential association between LINC00944 and T-cell infiltration and programmed cell death.In conclusion,this study identified the role of immune-related PANoptosis lncRNAs in ccRCC and provided a new risk stratification approach.Furthermore,it highlights the potential of LINC00944 as a prognostic biomarker.

Assessment of gastric cancer prognosis,immune infiltration based on cuproptosis-related LncRNAs and prediction of traditional Chinese medicine

Objective:Constructing a prognostic model for gastric cancer(GC)based on cuproptosisrelated LncRNAs(CRLs)and predict the traditional Chinese medicine that regulate cuproptosis-related genes(CRGs).Methods:Clinical data and RNA-seq of 443 GC cases were obtained from The Cancer Genome Atlas(TCGA)database,and CRLs were screened by Pearson analysis,Cox regression,and least absolute shrinkage and selection operator(LASSO)regression to construct a risk model to predict GC prognosis,and the nomogram was constructed by combining risk scores and clinical characteristics.The accuracy of the model was validated by the receiver operating characteristic curve,Kaplan-Meier curves and C-index.To assess the correlation of risk scores with immune infiltration,immune checkpoint gene expression and chemotherapy/targeted agents.The Coremine Medical database was applied to predict potential traditional Chinese medicine that regulate CRGs.Results:Risk models for GC were constructed based on the risk scores of seven CRLs(AP001107.9,VCAN-AS1,AC016394.2,LINC02675,AC100814.1,HAGLR,and LINC01094).The AUC of the risk model predicting 1-,3-,and 5-year survival in GC patients was 0.720,0.682,and 0.711,and its prognostic value was better than age,Grade classification,and TNM stage.The AUC of the risk model combining age and TNM stage to predict 1-year survival in GC patients was 0.793.The risk score correlated with the degree of enrichment of immune cells such as tumorinfiltrating lymphocytes and regulatory T cells and the expression of 22 immune checkpoint genes such as LAG3,ICOS,CD28,NRP1 and the sensitivity of 13 chemotherapeutic/targeted agents.There are 58 traditional Chinese medicine with potential regulatory effects on CRGs,mainly for clearing heat and detoxing,promoting blood circulation and relieving pain,which are mainly attributed to the liver,spleen and lung meridians.Spirulina and osthole have potential regulatory effects on FDX1,a key gene in the death mechanism of cuproptosis.Conclusions:A risk signature constructed based on seven CRLs could assess the prognosis and immunity of GC,and Spirulina and Serpentine may have important regulatory efficacy on the mechanism of copper cuproptosis.

Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma

Objective:Through integrated bioinformatics analysis,the goal of this work was to find new,characterised N7-methylguanosine modification-related long non-coding RNAs(m7G-lncRNAs)that might be used to predict the prognosis of laryngeal squamous cell carcinoma(LSCC).Methods:The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database&sanitised.Then,using co-expression analysis of m7G-associated mRNAs&lncRNAs&differential expression analysis(DEA)among LSCC&normal sample categories,we discovered lncRNAs that were connected to m7G.The prognosis prediction model was built for the training category using univariate&multivariate COX regression&LASSO regression analyses,&the model’s efficacy was checked against the test category data.In addition,we conducted DEA of prognostic m7G-lncRNAs among LSCC&normal sample categories&compiled a list of co-expression networks&the structure of prognosis m7G-lncRNAs.To compare the prognoses for individuals with LSCC in the high-&low-risk categories in the prognosis prediction model,survival and risk assessments were also carried out.Finally,we created a nomogram to accurately forecast the outcomes of LSCC patients&created receiver operating characteristic(ROC)curves to assess the prognosis prediction model’s predictive capability.Results:Using co-expression network analysis&differential expression analysis,we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs,respectively.We then constructed a prognosis prediction model for six m7G-lncRNAs(FLG−AS1,RHOA−IT1,AC020913.3,AC027307.2,AC010973.2 and AC010789.1),identified 32 DEPm7G-lncRNAs,analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs,and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients.By displaying ROC curves and a nomogram,we finally checked the prognosis prediction model's accuracy.Conclusion:By creating novel predictive lncRNA signatures for clinical diagnosis&therapy,our findings will contribute to understanding the pathogenetic process of LSCC.

Single-cell transcriptome analysis reveals a cancer-associated fibroblast marker gene signature in hepatocellular carcinoma that predicts prognosis

Background and aims:Hepatocellular carcinoma(HCC)is one of the leading causes of cancer death.Multi-pathway combination therapy is used to treat HCC,and immunotherapy is also a routine part of treatment.As a major component of the tumor microenvironment(TME),cancer-associated fibroblasts(CAFs)actively participate in cancer progression through complex functions.However,because CAFs dynamically change during cancer development,most of the current treatment strategies targeting CAFs fail.We created a prognostic CAF marker gene signature(CAFMGS)to investigate the utility of CAFs as a prognostic factor and therapeutic target.Methods:Gene Expression Omnibus(GEO)single-cell RNA sequencing(Sc-RNA-seq)data were analyzed to identify CAF marker genes in HCC.The Cancer Genome Atlas(TCGA)database was used as a training cohort to construct the CAFMGS model and the International Cancer Genome Consortium(ICGC)dataset was used to validate the CAFMGS.Results:Marker genes in the CAFMGS model were(0.0001-SPP1),(0.0084-VCX3A),(0.0015-HMGA1),(0.0082-PLOD2),and(0.0075-CACYBP).The CAFMGS_score was separated into high-risk and low-risk groups based on the median of the patients’OS.Univariate and multivariate analyses confirmed that CAFMGS_score was an independent prognostic factor in the training group.CAFMGS_score was a more accurate prognostic indicator compared with clinicopathological score and tumor mutational burden score.Conclusion:CAFMGS offers a fresh perspective on stromal cell marker genes in HCC prognosis and expands our knowledge of CAF heterogeneity and functional diversity,perhaps paving the way for CAF-targeted immunotherapy in HCC patients.

Single-cell atlas reveals a distinct immune profile fostered by T cell-B cell crosstalk in triple negative breast cancer

Background:Characterizing the unique immune microenvironment of each tumor is of great importance for better predicting prognosis and guiding cancer immunotherapy.However,the unique features of the immune microenvironment of triple negative breast cancer(TNBC)compared with other subtypes of breast cancer remain elusive.Therefore,we aimed to depict and compare the immune landscape among TNBC,human epidermal growth factor receptor 2-positive(HER2^(+))breast cancer,and luminal-like breast cancer.Methods:Single-cell RNA sequencing(scRNA-seq)was performed on CD45^(+)immune cells isolated from human normal breast tissues and primary breast tumors of various subtypes.By analyzing the scRNA-seq data,immune cell clusters were identified and their proportions as well as transcriptome features were compared among TNBC,human HER2^(+)breast cancer,and luminal-like breast cancer.Pseudotime and cell-cell communication analyses were also conducted to characterize the immune microenvironment.Results:ScRNA-seq data of 117,958 immune cells were obtained and 31 immune clusters were identified.A unique immunosuppressive microenvironment in TNBC was decoded as compared to that in HER2^(+)or luminal-like breast cancer,which was characterized by higher proportions of regulatory T cells(Tregs)and exhausted CD8+T cells and accompanied by more abundant plasma cells.Tregs and exhausted CD8+T cells in TNBC exhibited increased immunosuppression signature and dysfunctional scores.Pseudotime analyses showed that B cells tended to differentiate to plasma cells in TNBC.Cell-cell communication analyses indicated that these unique features are fostered by the diversified T cell-B cell crosstalk in TNBC.Based on the T cell-B cell crosstalk,a prognostic signaturewas established that could effectively predict the prognosis status for patients with TNBC.Additionally,it was found that TNBC had a higher proportion of cytotoxic natural killer(NK)cells,whereas HER2^(+)or luminal-like breast cancer lost this feature,suggesting thatHER2^(+)or luminal-like breast cancer,but not TNBC,may benefit from NK-based immunotherapy.Conclusions:This study identified a distinct immune feature fostered by T cell-B cell crosstalk in TNBC,which provides better prognostic information and effective therapeutic targets for breast cancer.