Eight-gene Signature Research Articles

A hot and cold tumor‑related prognostic signature for stage II colorectal cancer.

Globally, colorectal cancer (CRC) is one of the most lethal and prevalent malignancies. Based on the presence of immune cell infiltration in the tumor microenvironment, CRC can be divided into immunologically 'hot' or 'cold' tumors, which in turn leads to the differential efficacy of immunotherapy. However, the immune characteristics of hot and cold CRC tumors remain largely elusive, prompting further investigation of their properties regarding the tumor microenvironment. In the present study, a predictive model was developed based on the differential expression of proteins between cold and hot CRC tumors. First, the differentially expressed proteins (DEPs) were identified using digital spatial profiling and mass spectrometry-based proteomics analysis, and the pathway features of the DEPs were analyzed using functional enrichment analysis. A novel eight-gene signature prognostic risk model was developed (IDO1, MAT1A, NPEPL1, NT5C, PTGR2, RPL29, TMEM126A and TUBB4B), which was validated using data obtained from The Cancer Genome Atlas. The results revealed that the risk score of the eight-gene signature acted as an independent prognostic indicator in patients with stage II CRC (T3-4N0M0). It was also found that a high-risk score in the eight-gene signature was associated with high immune cell infiltration in patients with CRC. Taken together, these findings revealed some of the differential immune characteristics of hot and cold CRC tumors, and an eight-gene signature prognostic risk model was developed, which may serve as an independent prognostic indicator for patients with stage II CRC (T3-4N0M0).

Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer.

Gastric cancer (GC), the third most lethal cancer worldwide, is often diagnosed at an advanced stage, leaving limited therapeutic options. Given the diverse outcomes among GC patients with similar AJCC/UICC-TNM characteristics, there is a pressing need for more reliable prognostic tools. Recent advances in targeted therapy and immunotherapy have underscored this necessity. In this context, our study focused on a novel stress response state of T cells, termed TSTR, identified across multiple cancers, which is associated with resistance to immunotherapy. We aimed to develop a predictive gene signature for the TSTR phenotype within the tumor microenvironment (TME) of GC patients. By categorizing GC patients into high and low TSTR groups based on the infiltration states of TME TSTR cells, we observed significant differences in clinical prognosis and characteristics between the groups. Through a multi-step bioinformatics approach, we established an eight-gene signature based on genes differentially expressed between these groups. We conducted functional validations for the signature gene PDGFRL in GC cells. This gene signature effectively stratifies GC patients into high and low-risk categories, demonstrating robustness in predicting clinical outcomes. Furthermore, these risk groups exhibited distinct immune profiles, somatic mutations, and drug susceptibilities, highlighting the potential of our gene signature to enhance personalized treatment strategies in clinical practice.

Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.

We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment. We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

Identification of differentially expressed genes and splicing events in early-onset colorectal cancer.

The incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC. We performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens. We identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens. Our transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.

Novel T cell exhaustion gene signature to predict prognosis and immunotherapy response in thyroid carcinoma from integrated RNA-sequencing analysis.

Exhausted CD8+ T lymphocytes and tumor-associated macrophages play critical roles in determining cancer prognosis and the efficacy of immunotherapy. Our study revealed a negative correlation between exhausted CD8+ T lymphocytes and prognosis in thyroid carcinoma (THCA). Consensus clustering divided patients into two subgroups of exhaustion with different prognoses, as defined by marker genes of exhausted CD8+ T cells. Subsequently, we constructed an eight-gene prognostic signature, and developed a risk score named the exhaustion-related gene score (ERGS) to forecast both prognosis and immunotherapy response in THCA. Bulk RNA sequencing analysis revealed a higher prevalence of M2 macrophages, indicative of an immunosuppressive tumor microenvironment (TME), in the high-ERGS group. Single-cell RNA sequencing showed that SPP1+ macrophages and CD14+ monocytes infiltrations were positively associated with higher ERGS. Functionally, it was determined that SPP1+ macrophages exert an immunosuppressive role, while CD14+ monocytes were implicated in promoting tumor progression and angiogenesis. Analysis of cell-cell interactions between SPP1+ macrophages and T cells highlighted the activation of the SPP1-CD44 and MIF-CD74 axes, both of which could foster an immunosuppressive TME. Therapeutic strategies that target SPP1+ macrophages, CD14+ monocytes, and the SPP1-CD44 and MIF-CD74 axes may potentially improve the prognosis and amplify the immunotherapy response in THCA patients.

Metabolic Reprogramming of Cancer Stem Cells and a Novel Eight-Gene Metabolism-Related Risk Signature in Clear Cell Renal Carcinoma

Background:: Clear cell renal carcinoma (ccRCC) is one of the most common urological tumors worldwide and metabolic reprogramming is its distinguishing feature. A systematic study on the role of the metabolism-related genes in ccRCC cancer stem cells (CSCs) is still lacking. Moreover, an effective metabolism-related prediction signature is urgently needed to assess the prognosis of ccRCC patients. Methods:: Gene expression profiles of GSE48550 and GSE84546 were analyzed for the role of metabolism-related gene in ccRCC-CSCs. The GSE22541 dataset were used to construct and validate an effective metabolism-related prediction signature to assess the prognosis of ccRCC patients. Results:: For glycolytic metabolism, we found that HKDC1, PFKM and LDHB were significantly upregulated in ccRCC-CSCs in GSE84546. For TCA cycle, ACO1, SDHA and MDH1 were significantly downregulated in ccRCC-CSCs in both GSE48550 and GSE84546. For fatty acid metabolism, CPT1A and ACACB were significantly upregulated in ccRCC-CSCs in GSE84546. It is worth noting that SCD was significantly downregulated in both GSE48550 and GSE84546. For glutamine metabolism, SLC1A5, GLS and GOT1 were significantly upregulated in GSE84546. An eight-gene CSCs metabolism-related risk signature including HKDC1, PFKM, LDHB, IDH1, OGDH, SDHA, GLS and GLUL were constructed to predict the overall survival (OS) of ccRCC patients. Patients could be separated into two groups, and the patients with lower risk scores had longer survival time. Conclusion:: Our study indicated that metabolic reprogramming, including glycolytic metabolism, TCA cycle, fatty acid metabolism and glutamine metabolism, is more obvious in CD105+ renal cells (GSE84546) than CD133+ renal cells (GSE48550). An eight-gene metabolismrelated risk signature including HKDC1, PFKM, LDHB, IDH1, OGDH, SDHA, GLS and GLUL can effectively predict OS in ccRCC.

The development of prognostic gene markers associated with disulfidptosis in gastric cancer and their application in predicting drug response

BackgroundGastric cancer (GC) is a malignancy known for its high fatality rate. Disulfidptosis, a potentially innovative therapeutic strategy for cancer treatment, has been proposed. Nevertheless, the specific involvement of disulfidptosis in the context of GC remains uncertain. MethodsThe mRNA expression profiles were obtained from the TCGA and GEO databases. Univariate and LASSO Cox regression analyses were employed to identify differentially expressed genes and develop a risk model for disulfidptosis-related genes. The performance of the model was evaluated using Kaplan-Meier curve, ROC curve, and nomogram. Univariate and multivariate Cox regression analyses were conducted to determine if the risk model could serve as an independent prognostic factor. The biological function of the identified genes was assessed through GO, KEGG, and GSEA analyses. The prediction of drug response was conducted employing the package “pRRophetic”. Furthermore, gene expression was determined using qRT-PCR. ResultsAn eight-gene signature were identified and utilized to categorize patients into low- and high-risk groups. Survival, receiver operating characteristic (ROC) curve, and Cox analyses provided clarification that these eight hub genes served as a favorable independent prognostic factor for patients with GC. A nomogram was constructed by integrating clinical parameters with the risk signatures, demonstrating high precision in predicting 1-, 3-, and 5-year survival rates. Additionally, drug sensitivity was different in the high-risk and low-risk groups, and the expression of three genes was verified by qRT-PCR. ConclusionThe prognostic risk model developed in this study demonstrates the potential to accurately forecast the prognosis of patients with GC.

NPC1 promotes autophagy with tumor promotion and acts as a prognostic model for hepatocellular carcinoma

Backgroundmore and more studies have indicated that autophagy plays a crucial role in hepatocellular carcinoma (HCC) in recent years. Hence, our study aimed to establish a prognostic signature for HCC based on autophagy-related genes (ARGs) in order to predict the prognosis of HCC. MethodsAll original gene-expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). ARGs were obtained from the Human Autophagy Database (HADb). Univariate Cox regression analysis, Least absolute shrinkage and selection operator (LASSO) and Principal Component Analysis (PCA) analysis were performed to identify and validate the validity and reliability of our eight-gene signature, Gene Set Enrichment Analysis (GSEA) was used to perform enrichment analysis by comparing high-risk and low-risk groups in KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) gene sets. Finally, we verified the gene (NPC1) by functional experiments in vitro and in vivo. Results8 ARGs were identified for establishing an eight-gene signature. Then, we validated our eight-gene signature in training, internal, external, and entire testing cohorts. Besides, we also explored the relationships between the eight-gene signature and immune infiltration or immune checkpoints. We also identified NPC1 was closely related to Activated CD4 T cell and Type I IFN Response, and higher expressed level of HCC patients was more sensitive to CTLA4 and TNFRSF9 immune checkpoint inhibitors. NPC1 is highly expressed in HCC cells and tumor tissues, which promotes the proliferation, migration, and invasion of tumor cells by activating autophagy.. Conclusion8 ARGs were used to establish a gene signature to predict the prognosis of HCC. we inferred that NPC1 can promote late autophagy, it could be a future novel therapeutic target of HCC.

Development and validation of a combined hypoxia- and metabolism-related prognostic signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma.

Hypoxia and metabolism are closely correlated with the progression of cancer. We aimed to construct a combined hypoxia- and metabolism-related genes (HMRGs) prognostic signature to predict survival and immunotherapy responses in patients with clear cell renal cell carcinoma (ccRCC). The RNA-seq profiles and clinical data of ccRCC were acquired from the TCGA and the ArrayExpress (E-MTAB-1980) databases. Least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox regression analyses were applied to establish a prognostic signature. The E-MTAB-1980 cohort was selected for validation. The effectiveness and reliability of the signature were further evaluated by Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) curves. Further analyses, including functional enrichment, ssGSEA algorithm, CIBERSORT algorithm, and expression of immune checkpoints, were explored to investigate immune status and immunotherapy responses. We constructed a prognostic eight-gene signature with IRF6, TEK, PLCB2, ABCB1, TGFA, COL4A5, PLOD2, and TUBB6. Patients were divided into high-risk and low-risk groups based on the medium-risk score. The K-M analysis revealed that patients in the high-risk group had an apparently poor prognosis compared to those in the low-risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p < 0.005). The area under ROC curve (AUC) of the prognostic signature was 0.8 at 1 year, 0.77 at 3 years, and 0.78 at 5 years in the TCGA, respectively, and was 0.82 at 1 year, 0.74 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Independent prognostic analysis confirmed the risk score as a separate prognostic factor in ccRCC patients (p < 0.001). The results of ssGSEA showed not only a high degree of immune cell infiltration but also high scores of immune-related functions in the high-risk group. The CIBERSORT analysis further confirmed that the abundance of immune cells was apparently different between the two risk groups. The risk score was significantly correlated with the expression of cytotoxic T lymphocyte-associated antigen-4 (CTLA4), lymphocyte-activation gene 3 (LAG3), and programmed cell death protein 1 (PD-1). The HMRGs signature could be used to predict clinical prognosis, evaluate the efficacy of immunotherapy, and guide personalized immunotherapy in ccRCC patients.

Identification of epithelial-mesenchymal transition-related biomarkers in lung adenocarcinoma using bioinformatics and lab experiments.

Lung adenocarcinoma accounts for approximately 40% of lung cancer cases and poses a serious threat to human health. Therefore, there is an urgent need to identify central biomarkers in lung adenocarcinoma. We first identified the EMT-associated genes in LUAD based on the TCGA cohort. Then we screened these 90 EMT-associated genes using univariate Cox regression analysis and LASSO regression analysis to develop a prognostic gene signature in the training set. The predictive performance of the gene signature was assessed in the validation set and multiple external test sets using the ROC cure, C index and log-rank tests. RT-PCR, western blot, wound healing assays, and siRNA methods were further used to investigate the role of PLEK2 in tumor behaviors. Eight genes (CCNB1, PLEK2, DERL3, C1QTNF6, DLGAP5, HMMR, GJB3, and SPOCK1) were eventually selected to develop an eight-gene signature. The 5-year AUC of the gene signature has a robust predictive ability both for predicting overall survival (0.774, 0.756, and 0.669 in the external test sets, respectively), and for progression free survival (0.774, 0.746, and 0.755 in the external test sets, respectively). C-index of the gene signature was 0.961 ± 0.005, 0.916 ± 0.011, and 0.868 ± 0.234 in the external test sets, respectively. Four genes (C1QTNF6, DLGAP5, HMMR, and PLEK2) were identified as key genes in LUAD progression, which were upregulated in the cancerous tissue compared with in the normal tissue (P < 0.001), and correlated with an unwanted prognosis in lung cancer (P < 0.05). PLEK2 was used as an example to explore its effect on LUAD progression in vitro using RT-PCR, western blot, CCK8, si-RNA and wound healing assay. Silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy. This study developed a novel EMT-related gene signature benefiting precision medicine, and identified four pivotal genes which can serve as therapeutic targets in LUAD. Four key genes can serve as molecular targets for patients with LUAD; silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy.

A Novel Eight-Gene Signature for Lipid Metabolism Predicts the Progression of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma.

Recently, studies on the mechanisms underlying lipid metabolic reprogramming in cancer have increased. However, its significance in cervical cancer remains unclear. In the present study, a prognostic signature was constructed for patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) using the expression profiles of lipid metabolism-related genes (LMRGs). Furthermore, using various bioinformatics methods, a prognostic gene signature was developed for progression-free survival (PFS). This signature was externally validated using a cervical cancer dataset (GSE44001). The characteristics of the molecular subgroups of LMRGs were analyzed, and target LMRGs were identified via differential gene analysis of the expression profiles and weighted gene correlation network analysis. Thereafter, the identified target genes were used to develop the prognostic gene signature using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The performance of the LMRG signature was evaluated using Kaplan-Meier curves, time-dependent receiver operating characteristic curves, decision curve analysis, mutation landscapes, gene set enrichment analysis, and immune score calculation. As a result, a novel eight-LMRG signature comprising ALDH3B2, CERS3, FA2H, GLTP, NR1H3, PLIN3, SLC44A3, and SQLE was constructed. Using this gene signature, patients with CESC and significantly distinguished PFS were divided. This eight-LMRG signature exhibited independent prognostic potential and superior predictive performance compared with a previously developed 12-gene signature. Our findings suggest that our novel eight-LMRG signature contributes to the implementation of precision medicine strategies for managing patients with cervical cancer by facilitating CESC prognosis.

Identification of positive cofactor 4 as a diagnostic and prognostic biomarker associated with immune infiltration in hepatocellular carcinoma

Background and aimsHuman positive cofactor 4 (PC4) is associated with the development and therapeutic resistance of several malignancies. However, the role of PC4 in hepatocellular carcinoma (HCC) remains obscure. MethodsThe expression status of PC4 was explored in Gene Expression Omnibus and The Cancer Genome Atlas datasets. Subsequently, the prognostic and diagnostic significance of PC4 in HCC patients was analyzed. Functional enrichment analyses were conducted to explore biological functions and potential mechanisms. The CIBERSORT algorithm was used for immune infiltration analysis. The risk signature was constructed by LASSO-Cox regression and was validated with the International Cancer Genome Consortium dataset. Quantitative real-time polymerase chain reaction was used to verify the expression levels of all genes. Tumor Immune Dysfunction and Exclusion analysis evaluated immunotherapy response. Finally, using online databases, PC4-related competing endogenous RNA networks were constructed. ResultsPC4 levels were significantly upregulated in HCC and positively correlated with the pathological grade and clinical stage. The PC4-high expression group showed worse prognosis. In addition, PC4 could distinguish between tumor and normal tissues with an area under the curve of 0.965. The PC4 level was associated with immune checkpoints and immune cell infiltration. In the training and validation sets, the eight-gene risk signature strongly correlated with HCC patient prognosis. Tumor Immune Dysfunction and Exclusion analysis showed that patients in both the PC4-low and low-risk groups were more likely to benefit from immunotherapy. Finally, an lncRNA/microRNA-101-3p/PC4 network was constructed. ConclusionWe confirmed PC4 as a diagnostic and prognostic biomarker in HCC patients. We also developed and validated an eight-gene risk signature, which will help in clinical decision-making. The competing endogenous RNA network could help explore the regulatory mechanisms of PC4 in HCC.

A novel necroptosis related gene signature and regulatory network for overall survival prediction in lung adenocarcinoma

We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA–miRNA–lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.

A Blood-Based Immune Gene Signature with Prognostic Significance in Localized Prostate Cancer.

Prostate cancer (PCa) is one of the most common male cancers worldwide and one of the deadliest if unsuccessfully treated. Τhe need for reliable, easily accessible immune-related molecular biomarkers that could be combined with clinically defined criteria, including PSA and Gleason score, to accurately predict PCa patients' clinical outcomes is emerging. Herein, we describe for the first time a blood-identified immune-related gene signature comprising eight upregulated multi-functional genes associated with poor prognosis. Next-generation sequencing (NGS) analysis of PCa patients' peripheral blood samples revealed a more than three-fold upregulation of each of the eight genes as compared to samples originating from healthy donors. The construction of gene and protein interaction networks revealed different extents of the functional implications of these genes in the regulation of cell proliferation and immune responses. Analysis of the available data from The Cancer Genome Atlas (TCGA) regarding gene expression and survival of prostate adenocarcinoma (PRAD) and pan-cancer (PANCAN) patients revealed that intra-tumoral upregulation of this eight-gene signature (8-GS) was associated with poor 5-year progression-free intervals in PCa patients, even in those with high Gleason scores, and also with an unfavorable prognosis for cancer patients irrespective of the cancer type and even in the early stages. These observations suggest that further investigation of the 8-GS prospectively in randomized clinical trials, in which clinical benefit in terms of evaluating time to disease progression can be assessed, is warranted.

Oxidative stress genes define two subtypes of triple-negative breast cancer with prognostic and therapeutic implications.

Introduction: Oxidative stress (OS)-related genes have been confirmed to be closely related to the prognosis of triple-negative breast cancer (TNBC) patients; despite this fact, there is still a lack of TNBC subtype strategies based on this gene guidance. Here, we aimed to explore OS-related subtypes and their prognostic value in TNBC. Methods: Data from The Cancer Genome Atlas (TCGA)-TNBC and Sequence Read Archive (SRA) (SRR8518252) databases were collected, removing batch effects using a combat method before analysis. Consensus clustering analysis identified two OS subtypes (clusters A and B), with cluster A showing a better prognosis. Immune infiltration characteristics were analyzed using ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) algorithms, revealing higher ImmuneScore and ESTIMATEscore in cluster A. Tumor-suppressive immune cells, human leukocyte antigen (HLA) genes, and three immune inhibitors were more prevalent in cluster A. Results: An eight-gene signature, derived from differentially expressed genes, was developed and validated as an independent risk factor for TNBC. A nomogram combining the risk score and clinical variables accurately predicted patient outcomes. Finally, we also validated the classification effect of subtypes using hub markers of each subtype in the test dataset. Conclusion: Our study reveals distinct molecular clusters based on OS-related genes to better clarify the reactive oxygen species (ROS)-mediated progression and the crosstalk between the ROS and tumor microenvironment (TME) in this heterogenetic disease, and construct a risk prognostic model which could provide more support for clinical treatment decisions.

A Multigene Signature Associated with Progression-Free Survival after Treatment for IDH Mutant and 1p/19q Codeleted Oligodendrogliomas

Background. IDH mutant and 1p/19q codeleted oligodendrogliomas are the gliomas associated with the best prognosis. However, despite their sensitivity to treatment, patient survival remains heterogeneous. We aimed to identify gene expressions associated with response to treatment from a national cohort of patients with oligodendrogliomas, all treated with radiotherapy +/− chemotherapy. Methods. We extracted total RNA from frozen tumor samples and investigated enriched pathways using KEGG and Reactome databases. We applied a stability selection approach based on subsampling combined with the lasso-pcvl algorithm to identify genes associated with progression-free survival and calculate a risk score. Results. We included 68 patients with oligodendrogliomas treated with radiotherapy +/− chemotherapy. After filtering, 1697 genes were obtained, including 134 associated with progression-free survival: 35 with a better prognosis and 99 with a poorer one. Eight genes (ST3GAL6, QPCT, NQO1, EPHX1, CST3, S100A8, CHI3L1, and OSBPL3) whose risk score remained statistically significant after adjustment for prognostic factors in multivariate analysis were selected in more than 60% of cases were associated with shorter progression-free survival. Conclusions. We found an eight-gene signature associated with a higher risk of rapid relapse after treatment in patients with oligodendrogliomas. This finding could help clinicians identify patients who need more intensive treatment.

Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte signature in SKCM.

Global patterns of immune cell communications in the immune microenvironment of skin cutaneous melanoma (SKCM) haven't been well understood. Here we recognized signaling roles of immune cell populations and main contributive signals. We explored how multiple immune cells and signal paths coordinate with each other and established a prognosis signature based on the key specific biomarkers with cellular communication. The single-cell RNA sequencing (scRNA-seq) dataset was downloaded from the Gene Expression Omnibus (GEO) database, in which various immune cells were extracted and re-annotated according to cell markers defined in the original study to identify their specific signs. We computed immune-cell communication networks by calculating the linking number or summarizing the communication probability to visualize the cross-talk tendency in different immune cells. Combining abundant analyses of communication networks and identifications of communication modes, all networks were quantitatively characterized and compared. Based on the bulk RNA sequencing data, we trained specific markers of hub communication cells through integration programs of machine learning to develop new immune-related prognostic combinations. An eight-gene monocyte-related signature (MRS) has been built, confirmed as an independent risk factor for disease-specific survival (DSS). MRS has great predictive values in progression free survival (PFS) and possesses better accuracy than traditional clinical variables and molecular features. The low-risk group has better immune functions, infiltrated with more lymphocytes and M1 macrophages, with higher expressions of HLA, immune checkpoints, chemokines and costimulatory molecules. The pathway analysis based on seven databases confirms the biological uniqueness of the two risk groups. Additionally, the regulon activity profiles of 18 transcription factors highlight possible differential regulatory patterns between the two risk groups, suggesting epigenetic event-driven transcriptional networks may be an important distinction. MRS has been identified as a powerful tool to benefit SKCM patients. Moreover, the IFITM3 gene has been identified as the key gene, validated to express highly at the protein level via the immunohistochemical assay in SKCM. MRS is accurate and specific in evaluating SKCM patients' clinical outcomes. IFITM3 is a potential biomarker. Moreover, they are promising to improve the prognosis of SKCM patients.

ImmuneScore of eight-gene signature predicts prognosis and survival in patients with endometrial cancer.

Endometrial cancer (EC) is a common gynecological cancer worldwide and the sixth most common female malignant tumor. A large number of studies conducted through database mining have identified many biomarkers that may be related to survival and prognosis. However, the predictive ability of single-gene biomarkers is not sufficiently accurate. In recent years, tumors have been shown to interact closely with their microenvironment, and tumor-infiltrating immune cells in the tumor microenvironment were associated with therapeutic effects. Furthermore, sequencing technology has evolved and allowed the identification of genetic signatures that may improve prediction results. The purpose of this research was to discover the Cancer Genome Atlas (TCGA) data to evaluate new genetic features that can predict the prognosis of EC. mRNA expression profiling was analyzed in patients with EC identified in the TCGA database (n = 530). Differentially expressed genes at different stages of EC were screened using the immune cell enrichment score (ImmuneScore). Univariate and multivariate Cox regression analyses was applied to evaluate genes significantly related to overall survival and establish the prognostic risk parameter formula. Kaplan-Meier survival curves and the logarithmic rank method were applied to verify the importance of risk parameters for the prognostic forecast. The accuracy of survival prediction was confirmed using the nomogram and Receiver Operating Characteristic (ROC) curve analysis. The mRNA expression of eight genes were measured by qRT-PCR. According to COX and HR values, NBAT1, a representative gene among 8 genes, was selected for CCK-8 assay, colony formation assay and transwell invasion assay to verify the effect on survival. Eight related genes (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, UNQ6494, KLRB1, and PRAC1) were discovered to be significantly associated with the overall survival rate. According to these eight-gene signatures, 530 patients with EC were assigned to high- and low-risk subgroups. The prognostic capability of the eight-gene signature was not influenced by other elements. Eight related gene markers were identified using ImmuneScore, which could predict prognosis and survival in patients with EC. These findings provide a basis for understanding the application of biological information in tumors and identifying the poor prognosis of EC.

Proteome-Wide Analysis Reveals TFEB Targets for Establishment of a Prognostic Signature to Predict Clinical Outcomes of Colorectal Cancer.

Dephosphorylation of transcription factor EB (TFEB) at Ser142 and Ser138 determines its nuclear localization and transcriptional activity. The link between TFEB-associated genes and colorectal cancer (CRC) progression and prognosis remains unclear. To systematically identify the targets of TFEB, we performed data-independent acquisition (DIA)-based quantitative proteomics to compare global protein changes in wild-type (WT) DLD1 cells and TFEBWT- or TFEBS142A/S138A (activated status)-expressing DLD1 cells. A total of 6048 proteins were identified and quantified in three independent experiments. The differentially expressed proteins in TFEBS142A/S138A versus TFEBWT and TFEBWT versus control groups were compared, and 60 proteins were identified as products of TFEB transcriptional regulation. These proteins were significantly associated with vesicular endocytic trafficking, the HIF-1 signaling pathway, and metabolic processes. Furthermore, we generated a TFEB-associated gene signature using a univariate and LASSO Cox regression model to screen robust prognostic markers. An eight-gene signature (PLSCR3, SERPINA1, ATP6V1C2, TIMP1, SORT1, MAP2, KDM4B, and DDAH2) was identified. According to the signature, patients were assigned to high-risk and low-risk groups. Higher risk scores meant worse overall survival and higher epithelial-mesenchymal transition (EMT) scores. Additionally, as per the clinicopathological parameters and gene signature, a nomogram was constructed that was utilized to enhance the quantification capacity in risk assessment for individual patients. This research shows that TFEB directly mediates network effects in CRC, and the identified TFEB gene signature-based model may provide important information for the clinical judgment of prognosis.

A prognostic hypoxia gene signature is associated with a dampened tumour immune microenvironment in cervical cancer

Background: Hypoxia, or low oxygen, is a condition that is characteristic of solid tumours, including cervical cancer. Hypoxia is associated with worse survival in this disease and supports tumorigenic characteristics of stemness, survival, metastasis and angiogenesis, as well as immune resistance. Aims: In this work, we aimed to determine the prognostic significance of a previously derived in vitro eight-gene hypoxia signature in cervical cancer, and to investigate the interplay between the hypoxic and immune features of the cervical tumour microenvironment (TME). Subjects and Methods: The 240 patients with cervical squamous cell carcinoma obtained from the Cancer Genome Atlas Pan-Cancer study were first given a hypoxia score and an immune score (IS) and subsequently subjected to survival analysis. CIBERSORTx was also applied to determine the fractions of immune cells residing in the hypoxia high versus hypoxia low patient groups. Results: We show that the eight-gene hypoxia signature is predictive of worse prognosis in patients with cervical cancer and is associated with a less immunogenic TME. Furthermore, by combining the hypoxia score with an IS to stratify patients based on both their hypoxic and immune status, we achieve an enhancement in survival prediction, with the hypoxia high/immune low subgroup of patients showing the worse probability of overall and disease-specific survival. Conclusions: It is clear from this work that the interplay between the hypoxic and immune microenvironment can contribute to cervical cancer patients' survival. Furthermore, patients with tumours that are both highly hypoxic and immune deprived should be considered for hypoxia-targeted therapy when planning treatment modalities to improve survival.