Multigene Signatures Research Articles

Identification of a novel gene signature with regard to ferroptosis, prognosis prediction, and immune microenvironment in osteosarcoma.

Ferroptosis is a novel form of non-apoptotic cell death that mainly results from the iron-dependent lethal accumulation of lipid peroxidation products. Here, we defined differentially expressed genes between control and RSL3-treated osteosarcoma cells as ferroptosis-associated genes (FAGs). These FAGs were then subjected to weighted gene correlation network analysis (WGCNA), and we found that the turquoise module, containing 71 FAGs, was markedly related to the patient's vital status. After that, FAGs in the turquoise module were utilized to construct a prognostic multigene (COL5A2, HOXB4, and UNC5B) signature for risk stratification in osteosarcoma. Validation in internal and external cohorts indicated the accuracy and clinical applicability of this signature in predicting the prognosis of patients with osteosarcoma. Univariate and multivariate Cox regression analyses suggested that the signature-derived risk score is an independent indicator of patient prognosis. Immunological analysis indicated that significant variations in stromal and ESTIMATE scores, as well as tumor purity, were found when the high- and low-risk groups were compared. Regarding immune cell infiltration, the proportion of activated CD4 memory T cells was significantly lower in the high-risk group than that in the low-risk group. The ssGSEA results suggested that CD8+ T, Tfh, and Th1 cell scores were consistently lower in the high-risk group than those in the low-risk group. In terms of immune-related activities, the high-risk group had considerably lower scores for promoting inflammation, T-cell co-inhibition, and T-cell co-stimulation than the low-risk group, indicating the differential immunological state of the high- and low-risk groups. Of the three FAGs included in the signature, the expression of COL5A2, HOXB4, and UNC5B was higher in the high-risk groups, and the expression of COL5A2 and UNC5B was negatively associated with patient prognosis. Additionally, the mRNA levels of COL5A2 and HOXB4 were lower and those of UNC5B were higher in RSL3-treated cells than in control cells. In all, we systematically analyzed the transcriptional changes of osteosarcoma cells induced by RSL3 and constructed a novel three-gene signature with regard to ferroptosis, prognosis prediction, and immune microenvironment. We also identified COL5A2, HOXB4, and UNC5B as potential therapeutic targets and important regulators of ferroptosis in osteosarcoma.

Construction of mRNA prognosis signature associated with differentially expressed genes in early stage of stomach adenocarcinomas based on TCGA and GEO datasets

BackgroundStomach adenocarcinomas (STAD) are the most common malignancy of the human digestive system and represent the fourth leading cause of cancer-related deaths. As early-stage STAD are generally mild or asymptomatic, patients with advanced STAD have short overall survival. Early diagnosis of STAD has a considerable influence on clinical outcomes.MethodsThe mRNA expression data and clinical indicators of STAD and normal tissues were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The gene expression differences were analyzed by R packages, and gene function enrichment analysis was performed. Kaplan–Meier method and univariate Cox proportional risk regression analysis were used to screen differential expressed genes (DEGs) related to survival of STAD patients. Multivariate Cox proportional risk regression analysis was used to further screen and determine the prognostic DEGs in STAD patients, and to construct a multigene prognostic prediction signature. The accuracy of predictive signature was tested by receiver operating characteristic (ROC) curve software package, and the nomogram of patients with STAD was drawn. Cox regression was used to investigate the correlation between multigene prognostic signature and clinical factors. The predictive performance of this model was compared with two other models proposed in previous studies using KM survival analysis, ROC curve analysis, Harrell consistency index and decision curve analysis (DCA). qRT-PCR and Western blot were used to verify the expression levels of prognostic genes. The pathways and functions of possible involvement of features were predicted using the GSEA method.ResultsA total of 569 early-stage specific DEGs were retrieved from TCGA-STAD dataset, including 229 up-regulated genes and 340 down-regulated genes. Enrichment analysis showed that the early-stage specific DEGs were associated with cytokine–cytokine receptor interaction, neuroactive ligand–receptor interaction, and calcium signaling pathway. Multiple Cox regression algorithm was used to identify 10 early-stage specific DEGs associated with overall survival (P < 0.01) of STAD patients, and a multi-mRNA prognosis signature was established. The patients were divided into high-risk group and low-risk group according to the risk score. In the training set, the prognostic signature was positively correlated with tumor size and stage (P < 0.05), survival curve (P < 0.001) and time-dependent ROC (AUC = 0.625). In the training dataset and test dataset, the both signatures had good predictive efficiencies. Cox regression and DCA analysis revealed that the prognostic signature was an independent factor and had a better predict effect than the conventional TNM stage classification method and the earlier published biomarkers on the prognosis of STAD patients.ConclusionIn this study, based on the early-stage specifically expressed genes, the prognostic signature constructed through TCGA and GEO datasets may become an indicator for clinical prognosis assessment of STAD and a new strategy for targeted therapy in the future.

A pyroptosis-associated gene risk model for predicting the prognosis of triple-negative breast cancer.

BackgroundPyroptosis is a novel identified form of inflammatory cell death that is important in the development and progression of various diseases, including malignancies. However, the relationship between pyroptosis and triple-negative breast cancer (TNBC) is still unclear. Therefore, we started to investigate the potential prognostic value of pyroptosis-associated genes in TNBC.MethodsThirty-three genes associated with pyroptosis were extracted from previous publications, 30 of which were identified in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. On the basis of the 30 pyroptosis-related genes, patients with TNBC were divided into three subtypes through unsupervised cluster analysis. The prognostic value of each pyroptosis-associated gene was assessed, and six genes were selected by univariate and LASSO Cox regression analysis to establish a multigene signature. According to the median value of risk score, patients with TNBC in the training and validation cohorts were separated to high- and low-risk sets. The enrichment analysis was conducted on the differentially expressed genes (DEGs) of the two risk sets using R clusterProfiler package. Moreover, the ESTIMATE score and immune cell infiltration were calculated by the ESTIMATE and CIBERSORT methods. After that, the correlation among pyroptosis-associated risk score and the expression of immune checkpoint-associated genes as well as anti-cancer drugs sensitivities were further analyzed.ResultsIn the training and validation cohorts, patients with TNBC in the high-risk set were found in a lower survival rate than those in the low-risk set. Combined with the clinical characteristics, the pyroptosis-related risk score was identified as an independent risk factor for the prognosis of patients with TNBC. The enrichment analysis indicated that the DEGs between the two risk groups were mainly enriched by immune responses and activities. In addition, patients with TNBC in the low-risk set were found to have a higher value of ESTIMATE score and a higher rate of immune cell infiltration. Finally, the expression levels of five genes [programmed cell death protein 1 (PD-1); cytotoxic t-lymphocyte antigen-4 (CTLA4); lymphocyte activation gene 3 (LAG3); T cell immunoreceptor with Ig and ITIM domains (TIGIT)] associated with immune checkpoint inhibitors were identified to be higher in the low-risk sets. The sensitivities of some anti-cancer drugs commonly used in breast cancer were found closely related to the pyroptosis-associated risk model.ConclusionThe pyproptosis-associated risk model plays a vital role in the tumor immunity of TNBC and can be applied to be a prognostic predictor of patients with TNBC. Our discovery will provide novel insight for TNBC immunotherapies.

Application of Cytochrome C-Related Genes in Prognosis and Treatment Prediction of Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of nonsmall cell lung cancer. Cytochrome c (Cyt c), which is produced from mitochondria, interacts with a protein called Apaf-1 to form the heptameric apoptosome. This heptameric apoptosome then activates the caspase cascade, which ultimately results in the execution of apoptosis. The purpose of our research was to discover a new prognostic model that is based on cytochrome c-related genes (CCRGs) for LUAD patients. Through LASSO regression analysis conducted on the LUAD datasets included in the TCGA datasets, a CCRGs signature was created. The diagnostic accuracy of the multigene signature was verified by an independent source using the GSE31210 and GSE72094 datasets. The GO and KEGG enrichment analysis were performed. In this study, there were 159 differentially expressed CCRGs in the TCGA dataset, while there were 68 differentially expressed CCRGs in the GSE31210 dataset. Additionally, there were 57 genes that overlapped across the two datasets. Using LASSO and Cox regression analysis, a signature consisting of 12 differentially expressed CCRGs was developed from the total of 57 such genes. On the basis of their risk ratings, patients were categorized into high-risk and low-risk categories, with low-risk patients having lower risk scores and a greater likelihood of surviving the disease. Univariate and multivariate analyses both concluded that this signature is an independent risk factor for LUAD. ROC curves demonstrated that this risk signature is capable of accurately predicting the 1-year, 2-year, 3-year, and 5-year survival rates of patients who have LUAD. The infiltration of antigen-presenting cells was higher in the low-risk group, such as aDCs, DCs, pDCs, and iDCs. The expression of multiple immune checkpoints was significantly higher in the low-risk group, such as BTLA, CD28, and CD86. Finally, we showed that the signature can be used to predict the drug sensitivity of already available or under investigational drugs. Overall, patient classification and individualized therapy options may benefit from this study's development of a powerful gene signature with high value for prognostic prediction in LUAD.

A Three-Gene Signature for Predicting the Prognosis of Patients Treated with Transarterial Chemoembolization (TACE) and Identification of PD-184352 as a Potential Drug to Reverse Nonresponse to TACE.

Background Transarterial chemoembolization (TACE) is a first-line treatment for patients with unresectable hepatocellular carcinoma (HCC). Owing to differences in its efficacy across individuals, determining the indicators of patient response to TACE and finding approaches to reversing nonresponse thereto are necessary. Methods Transcriptome data were obtained from the GSE104580 dataset, in which patients were marked as having TACE response or nonresponse. We identified differentially expressed genes (DEGs) and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. We screened genes with a prognostic value for TACE in the HIF-1 signaling pathway by univariate regression analysis. By using least absolute shrinkage and selection operator (LASSO) Cox regression, we established a multigene signature in GSE14520, which we verified using a drug sensitivity test. The Connectivity Map (CMap) database was used to find potential drugs to reverse nonresponse to TACE. Results We constructed a prognostic signature consisting of three genes (erythropoietin (EPO), heme oxygenase 1 (HMOX1), and serine protease inhibitor 1 (SERPINE1)) that we validated by drug sensitivity test. After dividing patients treated with TACE into high- and low-risk groups based on this new signature, we showed that overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group and that the risk score was an independent predictor of OS in patients treated with TACE. Based on our CMap findings, we speculated that PD-184352, an inhibitor of mitogen-activated protein kinase (MEK), had potential as a drug treatment to reverse nonresponse to TACE. We confirmed this speculation by using PD-184352 in a cell promotion experiment in a TACE environment. Conclusion We constructed a TACE-specific three-gene signature that could be used to predict HCC patients' responses to and prognosis after TACE treatment. PD-184352 might have potential as a drug to improve TACE efficacy.

A novel T-cell proliferation-associated regulator signature pre-operatively predicted the prognostic of bladder cancer.

BackgroundBladder cancer (BCa) is a remarkably malignant and heterogeneous neoplastic disease, and its prognosis prediction is still challenging. Even with the mounting researches on the mechanisms of tumor immunotherapy, the prognostic value of T-cell proliferation regulators in bladder cancer remains elusive.MethodsHerein, we collected mRNA expression profiles and relevant clinical information of bladder cancer sufferers from a publicly available data base. Then, the LASSO Cox regression model was utilized to establish a multi-gene signature for the TCGA cohort to predict the prognosis and staging of bladder cancer. Eventually, the predictive power of the model was validated by randomized grouping.ResultsThe outcomes revealed that most genes related to T-cell proliferation in the TCGA cohort exhibited different expressions between BCa cells and neighboring healthy tissues. Univariable Cox regressive analyses showed that four DEGs were related to OS in bladder cancer patients (p<0.05). We constructed a histogram containing four clinical characteristics and separated sufferers into high- and low-risk groups. High-risk sufferers had remarkably lower OS compared with low-risk sufferers (P<0.001). Eventually, the predictive power of the signature was verified by ROC curve analyses, and similar results were obtained in the validation cohort. Functional analyses were also completed, which showed the enrichment of immune-related pathways and different immune status in the two groups. Moreover, by single-cell sequencing, our team verified that CXCL12, a T-lymphocyte proliferation regulator, influenced bladder oncogenesis and progression by depleting T-lymphocyte proliferation in the tumor microenvironment, thus promoting tumor immune evasion.ConclusionThis study establishes a novel T cell proliferation-associated regulator signature which can be used for the prognostic prediction of bladder cancer. The outcomes herein facilitate the studies on T-cell proliferation and its immune micro-environment to ameliorate prognoses and immunotherapeutic responses.

Pyroptosis related genes signature predicts prognosis and immune infiltration of tumor microenvironment in hepatocellular carcinoma

Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan–Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.

Construction of a Novel Oxidative Stress Response-Related Gene Signature for Predicting the Prognosis and Therapeutic Responses in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor outcomes, and the assessment of its prognosis as well as its response to therapy is still challenging. In this study, we aimed to construct an oxidative stress response–related genes–(OSRGs–) based gene signature for predicting prognosis and estimating treatment response in patients with HCC. We integrated the transcriptomic data and clinicopathological information of HCC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. LASSO Cox regression analysis was utilized to establish an integrated multigene signature in the TCGA cohort, and its prediction performance was validated in the ICGC cohort. The CIBERSORT algorithm was employed to evaluate immune cell infiltration. The response rate to immune checkpoint inhibition (ICI) therapy was assessed using a TIDE platform. Drug activity data from the Cancer Genome Project and NCI-60 human cancer cell lines were used to predict sensitivity to chemotherapy. We successfully established a gene signature comprising G6PD, MT3, CBX2, CDKN2B, CCNA2, MAPT, EZH2, and SLC7A11. The risk score of each patient, which was determined by the multigene signature, was identified as an independent prognostic marker. The immune cell infiltration patterns, response rates to ICI therapy, and the estimated sensitivity of 89 chemotherapeutic drugs were associated with risk scores. Individual prognostic genes were also associated with susceptibility to various FDA-approved drugs. Our study indicates that a comprehensive transcriptomic analysis of OSRGs can provide a reliable molecular model to predict prognosis and therapeutic response in patients with HCC.

A Novel Oxidative Phosphorylation-Associated Gene Signature for Prognosis Prediction in Patients with Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common type of malignant tumor with high morbidity and mortality. The oxidative phosphorylation (OXPHOS) metabolic pathway produces adenosine triphosphate (ATP) by delivering electrons to transmembrane protein complexes in the mitochondria. This research was dedicated to identifying an OXPHOS-associated signature for the assessment of prognosis of HCC patients. A total of 371 HCC patients from the Cancer Genome Atlas (TCGA) and 231 HCC patients from the International Cancer Genome Consortium (ICGC) with RNA expression data and clinical data were employed as construction and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to establish a multigene signature in the TCGA cohort, and the ICGC cohort was used for validation. The prognostic value of the risk signature was evaluated using univariate and multivariate Cox regression, Kaplan–Meier curves, and receiver operating characteristic (ROC) curves. The potential enrichment of biological functions was investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Meanwhile, we analyzed the correlation between the risk score and the tumor microenvironment (TME). A five-gene signature including ATP6V0B, ATP6V1C1, ATP6V1E1, TIMM9, and UQCRH was identified by LASSO Cox regression to classify patients into low- and high-risk groups. ROC curve analysis indicated that the five-gene signature is a prospective prognostic factor in HCC patients. Univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent prognostic factor for overall survival (OS). Functional analysis showed that differentially expressed genes (DEGs) between the low- and high-risk groups were enriched in mitosis and the cell cycle pathway. In addition, the five-gene signature was associated with innate immune cell infiltration, immune subtypes, and tumor stemness. A novel OXPHOS-associated gene signature can be used for prognostic prediction for patients with HCC.

The Prognostic Role of Cuproptosis in Head and Neck Squamous Cell Carcinoma Patients: A Comprehensive Analysis.

Purpose Head and neck squamous cell carcinoma (HNSCC) exhibits a high mortality and morbidity rate, and its treatment is facing clinical challenges. Cuproptosis, a copper-dependent cell death process, can help derive new forms of cancer therapies. However, the potential of cuproptosis-related genes (CRGs) as novel biomarkers for risk prediction, screening, and prognosis remains to be further explained in HNSCC. Methods We built a prognostic multigene signature with CRGs, which is associated with the tumor immune microenvironment (TME) by gene set enrichment analysis (GSEA), in the TCGA cohort. Furthermore, we systematically correlated risk signature with immunological characteristics in TME including tumor-infiltrating immune cells (TIICs), immune checkpoints, T cell inflamed score, and cancer immunity cycles. We also thoroughly investigated the biological functions of cuproptosis-associated lncRNAs and its immunological characteristics. Results CRGs-related prognostic model showed good prediction performance. A higher risk score was associated with a poorer overall survival (OS) than those with low-risk scores, according to the results of the survival analysis (p < 0.0001). The risk score was significantly related to the variable clinicopathological factors. Samples with high-risk scores had lower levels of CD8+ T cells infiltration. Immune therapy might be effective for the low-risk subtype of HNSCC patients (p < 0.05). Moreover, 11 differentially expressed lncRNAs as the independent prognostic factor could also predict TME in an accurate manner. Conclusion Our study identified and validated novel cuproptosis-related biomarkers for HNSCC prognosis and screening, which offer better insights into developing accurate, reliable, and novel cancer therapies in the era of precision medicine.

164P Current use of breast cancer multigene signatures in clinical practice among European experts: Results from the PROCURE Project

164P Current use of breast cancer multigene signatures in clinical practice among European experts: Results from the PROCURE Project

Identification of cuprotosis-mediated subtypes, the development of a prognosis model, and influence immune microenvironment in hepatocellular carcinoma.

PurposeCuprotosis is a newly discovered form of non-apoptotic regulated cell death and is characterized by copper-dependent and associated with mitochondrial respiration. However, the prognostic significance and function of cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) are unknown. This study aims to develop cuprotosis-mediated patterns-related gene (CMPRG) prediction models for the prognosis of patients with HCC, exploring the functional underlying the CRGs on the influence of tumor microenvironment (TME) features.Experimental designThis study obtained transcriptome profiling and the corresponding clinical information from the TCGA and GEO databases. Besides, the Cox regression model with LASSO was implemented to build a multi-gene signature, which was then validated in an internal validation set and two external validation sets through Kaplan-Meier, DCA, and ROC analyses.ResultsAccording to the LASSO analysis, we screened out a cuprotosis-mediated pattern 5-gene combination (including PBK; MMP1; GNAZ; GPC1 and AKR1D1). A nomogram was constructed for the presentation of the final model. The ROC curve assessed the model’s predictive ability, which resulted in an area under the curve (AUC) values ranging from 0.604 to 0.787 underwent internal and two external validation sets. Meanwhile, the risk score divided the patients into two groups of high and low risk, and the survival rate of high-risk patients was significantly lower than that of low-risk patients (P<0.01). The risk score could be an independent prognostic factor in the multifactorial Cox regression analysis (P<0.01). Functional analysis revealed that immune status, mutational loads, and drug sensitivity differed between the two risk groups.ConclusionsIn summary, we identified three cuprotosis-mediated patterns in HCC. And CMPRGs are a promising candidate biomarker for HCC early detection, owing to their strong performance in predicting HCC prognosis and therapy. Quantifying cuprotosis-mediated patterns in individual samples may help improve the understanding of multiomic characteristics and guide the development of targeted therapy for HCC.

Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer.

DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. Methods. The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary. Lasso regression was employed to build a multi-gene signature. An independent data set was applied to verify the prognostic value of the signature. The Gene Set Variation Analysis (GSVA) was used to carry out the enrichment analysis of the pathways linked to the gene signature. The IMvigor 210 cohort was used to explore the predictive efficacy of the gene signature for immunotherapy response. Results. We distinguished ovarian cancer samples into two subtypes with different prognosis, based on the omics data of DNA methylation. Differentially expressed genes and enrichment analysis among subtypes indicated that DNA methylation was related to fatty acid metabolism and the extracellular matrix (ECM)-receptor. Furthermore, we constructed an 8-gene signature, which proved to be efficient and stable in predicting prognostics in ovarian cancer patients with different data sets and distinctive pathological characteristics. Finally, the 8-gene signature could predict patients' responses to immunotherapy. The polymerase chain reaction experiment was further used to verify the expression of 8 genes. Conclusion. We analyzed the prognostic value of the related genes of methylation in ovarian cancer. The 8-gene signature predicted the prognosis and immunotherapy response of ovarian cancer patients well and is expected to be valuable in clinical application.

Bioinformatics Analysis of Inflammation Gene Signature in Indicating Cholangiocarcinoma Prognosis

Aim We studied inflammatory response-related genes in cholangiocarcinoma by bioinformatics analysis. Methods The expression profiles and clinical information of cholangiocarcinoma patients were downloaded from the TCGA cohort and the Gene Expression Omnibus. The greatest absolute shrinking and selecting operator Cox analyses were utilized to build a multigene predictive signature. Results An inflammation response-related gene profile was generated using LASSO-Cox regression analysis of Homo sapiens bestrophin 1 (BEST1), Chemokine (C–C motif) ligand 2 (CCL2), and plasminogen activator, urokinase receptor (PLAUR). Individuals in the highest category had a significantly lower overall survival time than those from the low-risk group. A receiver operating curve analysis was used to demonstrate the predictive ability of the predictive gene signature. Through multivariate Cox analysis, the risk score was discovered to be a predictor of overall survival (OS). According to functional assessments, the immunological state and milieu of the two risk areas were significantly different. The expression levels of predictive genes were found to be strongly linked to the sensitivity of cancer cells to antitumor therapy. Conclusion A new signature made up of three respective response-relevant genes is found to be a promising indicator of prognosis by influencing the immune condition and tumor microenvironment.

Identification of ferroptosis-related signature with potential implications in prognosis and immunotherapy of renal cell carcinoma.

Developing individualized therapies for different renal cell carcinoma patients is pivotal for improving the efficacy of immunotherapy. It has been reported that ferroptosis is involved in T cell-mediated anti-tumor immunity, and that therapeutic approaches targeting tumor ferroptosis pathway in combination with immune checkpoint blockade drugs improve the efficacy of cancer immunotherapy. This study focused specifically on ferroptosis genes to identify novel biomarkers that reflect prognosis in different renal cell carcinoma subtypes. LASSO algorithm and multivariate Cox regression were initiated for identifying ferroptosis-related multigene risk signature (FRGsig) and established a FRGsig score model. We used multiple tumor microenvironment gene signatures and methods to infer tumor microenvironment status and immune cell invasion levels. Our study found that high FRGsig score was associated with poor prognosis in patients with predominant histologic subtypes of renal cell carcinoma. And high FRGsig score samples had higher levels of anti-tumor immunity cells infiltration, and there was a feedback mechanism whereby anti-tumor inflammation promoted the recruitment or differentiation of immunosuppressive cells. FRGsig was a potential biomarker for predicting the response to immune checkpoint blockade therapy in kidney clear cell carcinoma and kidney papillary cell carcinoma, and the kidney papillary cell carcinoma patients with high FRGsig was associated with better response to anti-VEGF therapy. Our findings provided further insights into assessing immunotherapy sensitivity of predominant histologic subtypes of renal cell carcinoma. FRGsig might be a potential biomarker for predicting the efficacy of angiogenic blocking drugs or immune checkpoint inhibitors in different renal cell carcinoma subtypes, enabling more precise patient selection.

Prognostic Signature, Immune Features, and Therapeutic Responses of a Novel Ubiquitination-Related Gene Signature in Lung Adenocarcinoma.

Growing studies have implicated the association of ubiquitination-related genes (UbRGs) with the cancer progression and the long-term survival of patients. However, the prognostic values of UbRGs in lung adenocarcinoma (LUAD) have not been investigated. Our study aimed to establish a ubiquitination-related model for prognosis prediction and internal mechanism investigation. The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from TCGA and GEO datasets. Differentially expressed genes (DEGs) were screened between LUAD specimens and nontumor specimens. Kaplan–Meier analysis and univariate assays were carried out on DEGs to preliminarily screen survival-related UbRGs. Then, the LASSO Cox regression model was applied to develop a multigene signature, which was then demonstrated in two GEO datasets by the use of Kaplan-Meier, ROC, and Cox analyses. We estimated the immune cell infiltration in tumor microenvironment via CIBERSORT and immunotherapy response through the TIDE algorithm. In this study, a total of 71 ubiquitination-related DEGs were identified. Nine UbRGs, including TUBA4A, TRIM2, PLK1, ARRB1, TRIM58, PLK1, ARRB1, CCNB1, TRIM6, PTTG1, and CCT2, were included to establish a risk model, which was validated in TCGA and GEO datasets. The multivariate assays demonstrated that the 9-UbRGs signature was a robust independent prognostic factor in the overall survival of LUAD patients. The abundance of CD8 T cells, activated CD4 T memory cells, resting NK cells and macrophages was higher in the high-risk group, and the TMB of high-risk group was statistically higher than the low-risk group. Multiple drugs approved by FAD, targeting UbRGs, were available for the treatment of LUAD. Overall, we identified a nine ubiquitination-related gene signature, and the signature may be applied to be a potential biomarker for CD8 T cells response and clinical responses to immune checkpoint inhibitors for LUAD.

Development and Validation of a Novel Circadian Rhythm-Related Signature to Predict the Prognosis of the Patients with Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most important causes of cancer-related deaths and remains a major public health challenge worldwide. Considering the extensive heterogeneity of HCC, more accurate prognostic models are imperative. The circadian genes regulate the daily oscillations of key biological processes, such as nutrient metabolism in the liver. Circadian rhythm disruption has recently been recognized as an independent risk factor for cancer. In this study, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were compared and 248 differentially expressed genes (DEGs) of the circadian rhythm were identified. HCC was classified into two subtypes based on these DEGs. The prognostic value of each circadian rhythm-associated gene (CRG) for survival was assessed by constructing a multigene signature from TCGA cohort. A 6-gene signature was created by applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, and all patients in TCGA cohort were divided into high- and low-risk groups according to their risk scores. The survival rate of patients with HCC in the low-risk group was significantly higher than that in the high-risk group (p < 0.001). The patients with HCC in the Gene Expression Omnibus (GEO) cohort were also divided into two risk subgroups using the risk score of TCGA cohort, and the overall survival time (OS) was prolonged in the low-risk group (p = 0.012). Based on the clinical characteristics, the risk score was an independent predictor of OS in the patients with HCC. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that multiple metabolic pathways, cell cycle, etc., were enhanced in the high-risk group. Using the metabolic pathway single-sample gene set enrichment analysis (ssGSEA), it was found that the metabolic pathways in the high- and low-risk groups between TCGA and GEO cohorts were altered essentially in the same way. In conclusion, the circadian genes play an important role in HCC metabolic rearrangements and can be further used to predict the prognosis the patients with HCC.

A 13-gene signature to predict the prognosis and immunotherapy responses of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) comprises 20–30% of all lung cancers. Immunotherapy has significantly improved the prognosis of LUSC patients; however, only a small subset of patients responds to the treatment. Therefore, we aimed to develop a novel multi-gene signature associated with the immune phenotype of the tumor microenvironment for LUSC prognosis prediction. We stratified the LUSC patients from The Cancer Genome Atlas dataset into hot and cold tumor according to a combination of infiltration status of immune cells and PD-L1 expression level. Kaplan–Meier analysis showed that hot tumors were associated with shorter overall survival (OS). Enrichment analyses of differentially expressed genes (DEGs) between the hot and cold tumors suggested that hot tumors potentially have a higher immune response ratio to immunotherapy than cold tumors. Subsequently, hub genes based on the DEGs were identified and protein–protein interactions were constructed. Finally, we established an immune-related 13-gene signature based on the hub genes using the least absolute shrinkage and selection operator feature selection and multivariate cox regression analysis. This gene signature divided LUSC patients into high-risk and low-risk groups and the former inclined worse OS than the latter. Multivariate cox proportional hazard regression analysis showed that the risk model constructed by the 13 prognostic genes was an independent risk factor for prognosis. Receiver operating characteristic curve analysis showed a moderate predictive accuracy for 1-, 3- and 5-year OS. The 13-gene signature also performed well in four external cohorts (three LUSC and one melanoma cohorts) from Gene Expression Omnibus. Overall, in this study, we established a reliable immune-related 13-gene signature that can stratify and predict the prognosis of LUSC patients, which might serve clinical use of immunotherapy.

A New Prognostic Indicator of Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma Based on Peroxisome-Related Genes.

Lung adenocarcinoma (LUAD) has been the major cause of tumor-associated mortality in recent years and exhibits a poor outcome. New data revealed that peroxisomes have a function in the regulation of the development and progression of several tumors. However, the prognostic values of peroxisome-related genes (PRGs) were rarely reported. Genomic sequence, mutation, and clinical data of 535 LUAD tissues were obtained from TCGA data sets. Within the TCGA cohort, a multigene signature was constructed with the assistance of the LASSO Cox regression model. Three GEO data sets, including GSE3141, GSE31210, and GSE72094, were obtained as validation cohorts. ROC assays, Kaplan-Meier methods, and multivariate assays were applied to examine the prognostic capacities of the novel signature. Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. In this study, we identified 47 differentially expressed peroxisome-related genes (PRGs), including 25 increased and 22 decreased PRGs. A prognostic model of six PRGs was established. The univariate and multivariate Cox analyses both showed that the p value of risk score was less than 0.05. In LUAD patients, the strong connection between the risk score and overall survival was further verified in three other GEO data sets. TMB and cancer stem cell infiltration were shown to be significantly higher in the high-risk group in comparison to the low-risk group. The TIDE score of the group with the low risk was considerably greater than that of the group with the high risk. Several drugs, targeting PRG-related genes, were available for the treatments of LUAD. Overall, we developed a novel peroxisome-related prognostic signature for LUAD patients. This signature could successfully indicate LUAD patients' chances of survival as well as their immune system's responsiveness to treatments. In addition, it has the potential to serve as immunotherapeutic targets for LUAD patients.

Identifying Differential Expression Genes and Prognostic Signature Based on Subventricular Zone Involved Glioblastoma.

Background: Studies have suggested that glioblastoma (GBM) cells originate from the subventricular zone (SVZ) and that GBM contact with the SVZ correlated with worse prognosis and higher recurrence. However, research on differentially expressed genes (DEGs) between GBM and the SVZ is lacking. Methods: We performed deep RNA sequencing on seven SVZ-involved GBMs and paired tumor-free SVZ tissues. DEGs and enrichment were assessed. We obtained GBM patient expression profiles and clinical data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a multigene signature in the CGGA cohort. GBM patient data from TCGA cohort were used for validation. Results: We identified 137 (97 up- and 40 down-regulated) DEGs between GBM and healthy SVZ samples. Enrichment analysis revealed that DEGs were mainly enriched in immune-related terms, including humoral immune response regulation, T cell differentiation, and response to tumor necrosis factor, and the MAPK, cAMP, PPAR, PI3K-Akt, and NF-κb signaling pathways. An eight-gene (BCAT1, HPX, NNMT, TBX5, RAB42, TNFRSF19, C16orf86, and TRPC5) signature was constructed. GBM patients were stratified into two risk groups. High-risk patients showed significantly reduced overall survival compared with low-risk patients. Univariate and multivariate regression analyses indicated that the risk score level represented an independent prognostic factor. High risk score of GBM patients negatively correlated with 1p19q codeletion and IDH1 mutation. Immune infiltration analysis further showed that the high risk score was negatively correlated with activated NK cell and monocyte counts, but positively correlated with macrophage and activated dendritic cell counts and higher PD-L1 mRNA expression. Conclusion: Here, a novel gene signature based on DEGs between GBM and healthy SVZ was developed for determining GBM patient prognosis. Targeting these genes may be a therapeutic strategy for GBM.