LASSO Cox Analysis Research Articles

Construction of M2 macrophage-related gene signature for predicting prognosis and revealing different immunotherapy response in bladder cancer patients.

Bladder cancer development is closely associated with the dynamic interaction and communication between M2 macrophages and tumor cells. However, specific biomarkers for targeting M2 macrophages in immunotherapy remain limited and require further investigation. In this study, we identified key co-expressed genes in M2 macrophages and developed gene signatures to predict prognosis and immunotherapy response in patients. Public database provided the bioinformatics data used in the analysis. We created and verified an M2 macrophage-related gene signature in these datasets using Lasso-Cox analysis. The predictive value and immunological functions of our risk model were examined in bladder cancer patients, and 158 genes were found to be significantly positively correlated with M2 macrophages. Moreover, we identified two molecular subgroups of bladder cancer with markedly different immunological profiles and clinical prognoses. The five key risk genes identified in this model were validated, including CALU, ECM1, LRP1, CYTL1, and CCDC102B, demonstrating the model can accurately predict prognosis and identify unique responses to immunotherapy in patients with bladder cancer. In summary, we constructed and validated a five-gene signature related to M2 macrophages, which shows strong potential for forecasting bladder cancer prognosis and immunotherapy response.

System analysis based on T-cell exhaustion-related genes identifies PTPRT as a promising diagnostic and prognostic biomarker for gastric cancer

Multiple investigations have demonstrated the crucial involvement of T-cell exhaustion (TEX) in anti-tumor immune response and their strong correlation with prognosis. This study aimed at creating a strong signature using TEX for gastric cancer through bioinformatics analysis and experimental validation. We utilized data from The Cancer Genome Atlas (TCGA) databases to retrieve RNA-seq data from patients with stomach adenocarcinoma (STAD). Genes related to TEX were discovered using gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Subsequently, prognostic signature based on TEX was developed using LASSO-Cox analysis. Relationship between key genes and immune cells were examined. Finally, biological function of a key TEX-related gene PTPRT in gastric cancer was verified by in vivo experiment. A total of 29 TEX-related biomarkers were screened by WGCNA and random forest. Among them, five core signatures (PTPRT, CAV2, PPIH, PRDM2, and FGF1), further identified by LASSO-Cox, were considered as strong predictors of prognosis for gastric cancer and associated with immune infiltration. PTPRT gene had the largest number of SNPs, with the most mutation types. In vivo experiments revealed that PTPRT overexpression significantly inhibited tumor malignant progression and accelerated apoptosis through stimulating the secretion of killer cytokines such as TNF-α and IFN-γ. In addition, flow cytometry revealed that PTPRT overexpression alleviated TEX by increasing the abundance of CD8+ T cells, with inhibition of cell surface PD-1 and Tim-3. The predictive prognostic value of TEX gene expression levels was evaluated in patients with gastric cancer, providing a new perspective for precision immuno-oncology studies.

Systematic transcriptomic analysis of childhood medulloblastoma identifies N6- methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis.

Medulloblastoma, the most common malignant pediatric brain tumor, is classified into four main molecular subgroups, but group 3 and group 4 tumors are difficult to subclassify and have a poor prognosis. Rapid point-of-care diagnostic and prognostic assays are needed to improve medulloblastoma risk stratification and management. N6-methyladenosine (m6A) is a common RNA modification and long non-coding RNAs (lncRNAs) play a central role in tumor progression, but their impact on gene expression and associated clinical outcomes in medulloblastoma are unknown. Here we analyzed 469 medulloblastoma tumor transcriptomes to identify lncRNAs co-expressed with m6A regulators. Using LASSO-Cox analysis, we identified a five-gene m6A-associated lncRNA signature (M6LSig) significantly associated with overall survival, which was combined in a prognostic clinical nomogram. Using expression of the 67 m6A-associated lncRNAs, a subgroup classification model was generated using the XGBoost machine learning algorithm, which had a classification accuracy > 90%, including for group 3 and 4 samples. All M6LSig genes were significantly correlated with at least one immune cell type abundance in the tumor microenvironment, and the risk score was positively correlated with CD4 + naïve T cell abundance and negatively correlated with follicular helper T cells and eosinophils. Knockdown of key m6A writer genes METTL3 and METTL14 in a group 3 medulloblastoma cell line (D425-Med) decreased cell proliferation and upregulated many M6LSig genes identified in our in silico analysis, suggesting that the signature genes are functional in medulloblastoma. This study highlights a crucial role for m6A-dependent lncRNAs in medulloblastoma prognosis and immune responses and provides the foundation for practical clinical tools that can be rapidly deployed in clinical settings.

Identification and validation of potential prognostic biomarkers in glioblastoma via the mesenchymal stem cell infiltration level.

Mesenchymal stem cells (MSCs) are key components in promoting glioblastoma (GBM) progression. This study aimed to explore new therapeutic targets and related pathogenic mechanisms based on different MSCs infiltration levels in GBM patients. We estimated the relationship between cell infiltration and prognosis of GBM. Subsequently, key risk genes were identified and prognostic models were constructed by LASSO-Cox analysis. The risk genes were validated by five independent external cohorts, single-cell RNA analysis, and immunohistochemistry of human GBM tissues. TIDE analysis predicted responsiveness to immune checkpoint inhibitors in different risk groups. The MSCs infiltration level was negatively associated with survival in GBM patients. LOXL1, LOXL4, and GUCA1A are key risk genes that promote GBM progression and may act through complex intercellular communication. This research has provided a comprehensive study for exploring the MSCs infiltration environment on GBM progression, which could shed light on novel biomarkers and mechanisms involved in GBM progression.

Prognostic cellular senescence-related lncRNAs patterns to predict clinical outcome and immune response in colon cancer.

Cellular senescence (CS) is believed to be a major factor in the evolution of cancer. However, CS-related lncRNAs (CSRLs) involved in colon cancer regulation are not fully understood. Our goal was to create a novel CSRLs prognostic model for predicting prognosis and immunotherapy and exploring its potential molecular function in colon cancer. The mRNA sequencing data and relevant clinical information of GDC TCGA Colon Cancer (TCGA-COAD) were obtained from UCSC Xena platform, and CS-associated genes was acquired from the CellAge website. Pearson correlation analysis was used to identify CSRLs. Then we used Kaplan-Meier survival curve analysis and univariate Cox analysis to acquire prognostic CSRL. Next, we created a CSRLs prognostic model using LASSO and multivariate Cox analysis, and evaluated its prognostic power by Kaplan-Meier and ROC curve analysis. Besides, we explored the difference in tumor microenvironment, somatic mutation, immunotherapy, and drug sensitivity between high-risk and low-risk groups. Finally, we verified the functions of MYOSLID in cell experiments. Three CSRLs (AC025165.1, LINC02257 and MYOSLID) were identified as prognostic CSRLs. The prognostic model exhibited a powerful predictive ability for overall survival and clinicopathological features in colon cancer. Moreover, there was a significant difference in the proportion of immune cells and the expression of immunosuppressive point biomarkers between the different groups. The high-risk group benefited from the chemotherapy drugs, such as Teniposide and Mitoxantrone. Finally, cell proliferation and CS were suppressed after MYOSLID knockdown. CSRLs are promising biomarkers to forecast survival and therapeutic responses in colon cancer patients. Furthermore, MYOSLID, one of 3-CSRLs in the prognostic model, could dramatically regulate the proliferation and CS of colon cancer.

Systematic transcriptomic analysis of childhood medulloblastoma identifies N6-methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis

Medulloblastoma, the most common malignant pediatric brain tumor, is classified into four main molecular subgroups, but group 3 and group 4 tumors are difficult to subclassify and have a poor prognosis. Rapid point-of-care diagnostic and prognostic assays are needed to improve medulloblastoma risk stratification and management. N6-methyladenosine (m6A) is a common RNA modification and long non-coding RNAs (lncRNAs) play a central role in tumor progression, but their impact on gene expression and associated clinical outcomes in medulloblastoma are unknown. Here we analyzed 469 medulloblastoma tumor transcriptomes to identify lncRNAs co-expressed with m6A regulators. Using LASSO-Cox analysis, we identified a five-gene m6A-associated lncRNA signature (M6LSig) significantly associated with overall survival, which was combined in a prognostic clinical nomogram. Using expression of the 67 m6A-associated lncRNAs, a subgroup classification model was generated using the XGBoost machine learning algorithm, which had a classification accuracy > 90%, including for group 3 and 4 samples. All M6LSig genes were significantly correlated with at least one immune cell type abundance in the tumor microenvironment, and the risk score was positively correlated with CD4+ naïve T cell abundance and negatively correlated with follicular helper T cells and eosinophils. Knockdown of key m6A writer genes METTL3 and METTL14 in a group 3 medulloblastoma cell line (D425-Med) decreased cell proliferation and upregulated many M6LSig genes identified in our in silico analysis, suggesting that the signature genes are functional in medulloblastoma. This study highlights a crucial role for m6A-dependent lncRNAs in medulloblastoma prognosis and immune responses and provides the foundation for practical clinical tools that can be rapidly deployed in clinical settings.

AURKB promotes immunogenicity and immune infiltration in clear cell renal cell carcinoma

BackgroundChromatin regulators (CRs) are capable of causing epigenetic alterations, which are significant features of cancer. However, the function of CRs in controlling Clear Cell Renal Cell Carcinoma (ccRCC) is not well understood. This research aims to discover a CRs prognostic signature in ccRCC and to elucidate the roles of CRs-related genes in tumor microenvironment (TME).MethodsExpression profiles and relevant clinical annotations were retrieved from the Cancer Genome Atlas (TCGA) and UCSC Xena platform for progression-free survival (PFS) data. The R package "limma" was used to identify differentially expressed CRs. A predictive model based on five CRs was developed using LASSO-Cox analysis. The model's predictive power and applicability were validated using K-M curves, ROC curves, nomograms, comparisons with other models, stratified survival analyses, and validation with the ICGC cohort. GO and GSEA analyses were performed to investigate mechanisms differentiating low and high riskScore groups. Immunogenicity was assessed using Tumor Mutational Burden (TMB), immune cell infiltrations were inferred, and immunotherapy was evaluated using immunophenogram analysis and the expression patterns of human leukocyte antigen (HLA) and checkpoint genes. Differentially expressed CRs (DECRs) between low and high riskScore groups were identified using log2|FC|> 1 and FDR < 0.05. AURKB, one of the high-risk DECRs and a component of our prognostic model, was selected for further analysis.ResultsWe constructed a 5 CRs signature, which demonstrated a strong capacity to predict survival and greater applicability in ccRCC. Elevated immunogenicity and immune infiltration in the high riskScore group were associated with poor prognosis. Immunotherapy was more effective in the high riskScore group, and certain chemotherapy medications, including cisplatin, docetaxel, bleomycin, and axitinib, had lower IC50 values. Our research shows that AURKB is critical for the immunogenicity and immune infiltration of the high riskScore group.ConclusionOur study produced a reliable prognostic prediction model using only 5 CRs. We found that AURKB promotes immunogenicity and immune infiltration. This research provides crucial support for the development of prognostic biomarkers and treatment strategies for ccRCC.

Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection.

Mast Cell-related Prognosis Signature Characterizes Immune Landscape and Predicts Prognosis of Ovarian Cancer.

Ovarian cancer (OVC) is a common, aggressive, and heterogeneous malignancy, with a widely variable prognosis. With the advances of modern immunology, mast cells (MCs) have been shown to play a significant role in the prognosis of some malignant tumors. However, the role of mast cells in the prognosis of OVC is unknown. In this study, MC-associated prognostic genes (MRGs) were used to classify OVC from The Cancer Genome Atlas (TCGA)-OVC cohort. Genes were evaluated using univariate cox regression analysis. Twenty-nine prognostic gene signatures were identified using LASSO-COX analysis. COX regression models and principal component analysis (PCA) algorithms were used to construct MRG scores and individual MRGs patterns. External validation was performed in the TCGA-breast cancer (BRCA) and IMvigor210 cohorts. Immunity analysis based on MRGs was performed using CIBERSORT, and GSVA methods, and immunotherapy response was evaluated using the TIDE website. Using TCGA-OVC data, we established a model for constructing MRG scores based on the twenty-nine identified prognostic gene signatures using the PCA algorithm. MRG scores were found to be strongly correlated with immune cell infiltration and were excellent predictors of prognosis in patients with OVC. Low MRG scores were associated with better prognosis and better response to immunotherapy and chemotherapy. MC-related prognosis signature characterizes the immune landscape and predicts the prognosis of OVC. Understanding the correlation between MC-related gene signatures and immunotherapy and chemotherapy may improve the development of personalized clinical treatment strategies.

The prognostic and clinical value of genes associate with immunity and amino acid Metabolism in Lung Adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the commonest subtype of primary lung cancer. A comprehensive analysis of the association of immunity with amino acid metabolism in LUAD is critical for understanding the disease. MethodsThe present study examined LUAD and noncancerous cases from the TCGA database. Differentially expressed genes (DEGs) between LUAD and noncancerous tissues were detected by analyzing processed expression profiles. We cross-referenced the up-regulated DEGs with Immune and Amino Acid Metabolism-related genes (I&AAMGs), resulting in Immune and Amino Acid Metabolism related differentially expressed genes (IAAAMRDEGs). The STRING database was employed to analyze PPI on IAAAMRDEGs, obtaining excavated hub genes, whose biological processes, molecular functions and cellular components were examined with GO/KEGG. Potential mechanisms related to LUAD were investigated by GSEA and GSVA. A prognostic model was built by LASSO-COX analysis, taking into consideration risk scores and prognostic factors to determine biomarkers affecting LUAD occurrence and prognosis. ResultsTotally 377 genes were detected at the intersection of upregulated DEGs and I&AAMGs. Analysis of PPI on these 377 IAAAMRDEGs yielded 17 hub genes. A LASSO regression analysis was utilized to assess the prognostic values of the 17 hub genes. Validation using the combined dataset confirmed 4 genes, e.g., polo-like kinase (PLK1), Ribonucleotide Reductase Subunit M2 (RRM2), Thyroid Hormone Receptor Interactor 13 (TRIP13), and Hyaluronan-Mediated Motility Receptor (HHMR). The model's accuracy was further assessed by ROC curve analysis and the COX model. In addition, immunohistochemical staining obtained from the HPA database, revealed enhanced PLK1 expression in LUAD samples. ConclusionLUAD pathogenesis is highly associated with immunity and amino acid metabolism. The PLK1, RRM2, TRIP13, and HMMR genes have prognostic values for LUAD. PLK1 upregulation in LUAD might be involved in tumorigenesis by modulating the cell cycle and represents a potential prognostic factor in clinic.

Distinct cuproptosis patterns in hepatocellular carcinoma patients correlate with unique immune microenvironment characteristics and cell-cell communication, contributing to varied overall survival outcomes.

Hepatocellular carcinoma (HCC), a prevalent cancer, is linked to cuproptosis in tumor progression. However, cuproptosis's impact on HCC prognosis and its role in the tumor microenvironment remain unclear. We aimed to explore the correlation between cellular cuproptosis and the immune microenvironment in HCC, providing potential immunotherapeutic insights. Examining cuproptosis-related genes and the immune microenvironment through consensus clustering and WGCNA. Risk models were constructed using LASSO Cox analysis and validated in an independent cohort. Gene expression data from The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were utilized. We scored cuproptosis expression and explored immunoinfiltration and cell-cell communication. Differential signals in T_memory cells were compared across different cuproptosis levels. Cuproptosis genes associated with fibroblast recruitment (GLS) and macrophage infiltration (FDX1). Liver cancer patients categorized into two subtypes based on cuproptosis gene expression. High expression of DLAT, GLS, and CDKN2A linked to immunosuppression (TGF-β), while high FDX1, MTF1, LIAS, and LIPT1 expression enhanced communication with non-immune cells. Developed reliable prognostic signature score and nomogram using cuproptosis-related genes. Single-cell analysis revealed differences in T_memory and TAM infiltration based on cuproptosis scores, with SPP1 and MIF as dominant signaling molecules. Finally, the results of in vitro experiments showed that when DLAT or CDKN2A was knocked down, the proliferation, migration, and invasion of HCC cells were significantly decreased. Our study demonstrates that cuproptosis affects the immune microenvironment and cell-cell communication. Identified 9 genetic markers predicting survival outcomes and immunotherapy responses. Evaluating cuproptosis signaling can optimize immunotherapeutic strategies for hepatocellular carcinoma.

Identifying and Validating an Angiogenesis-related Signature for the Prognosis of Head and Neck Squamous Cell Carcinoma.

The present study aimed todevelop a prognostic model for HNSCC treatment on the basis of angiogenesis-related signatures. Head and Neck Squamous Cell Carcinoma (HNSCC) is the most frequent malignancy with poor prognostic outcomes in the head and neck. Angiogenesis plays a critical role in tumorigenesis and is expected to be an effective therapeutic target. The RNA-seq dataset TCGA-HNSCC and the hallmark gene set were used for angiogenesis-related RiskScore model construction. The RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA), and the hallmark gene set was used to measure the angiogenesis score using the GSVA R package. Then, the optimal cutoff point for prognostic classification was calculated by the survminer package, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify angiogenesis gene modules . Multi/univariable and Lasso Cox analyses were performed to develop the RiskScore model, and the classifier efficiency was evaluated by the Receiver Operating Characteristic curve (ROC). Furthermore, a nomogram was designed for survival probability prediction, and the immune infiltration and immunotherapy differences among different risk patients were assessed. After calculating the angiogenesis score, we found that this indicator and patients' prognosis were closely correlated, especially when patients with a high angiogenesis score had a poor prognosis. Then, WGCNA identified a blue gene module positively correlated with angiogenesis. Multivariate and Lasso Cox analysis further identified 9 risk model genes for developing a RiskScore, which was used to divide low- and high- -risk groups of patients. Those with a high risk tended to show poor prognosis, immune infiltration, and higher immune escape. Finally, a nomogram was developed to optimize the risk model, and it exhibited excellent short- and long-term survival prediction performance. We constructed a reliable RiskScore model for the prognostic prediction of HNSCC patients, contributing to precise therapeutic intervention of the cancer.

COL8A1 is a potential prognostic biomarker associated with migration, proliferation, and tumor microenvironment in glioma

Glioblastoma (GBM) is a common primary central nervous system tumor. The molecular mechanisms of glioma are unknown, and the prognosis is poor. Therefore, exploring the underlying mechanisms and screening for new prognostic markers and therapeutic targets is crucial. We utilized the weighted gene co-expression network analysis (WGCNA), Differentially Expressed Genes (DEGs), and LASSO-COX analysis to identify three target genes. Next, we constructed and evaluated a prognostic model, screening out COL8A1 as a risk gene. Through a sequence of cellular functional experiments, in vivo studies, and RNA sequencing, we delved into exploring the functional effects and molecular mechanisms of COL8A1 on GBM cells. Finally, the correlation between COL8A1 and tumor immune cells and different inflammatory responses was analyzed. Immunohistochemistry experiments revealed the influence of COL8A1 on macrophage polarization. The COL8A1 expression level was associated with the grade, prognosis, and tumor microenvironment (TME) of glioma. Functional experiments showed that COL8A1 inhibited GBM cell apoptosis and promoted migration, invasion, and proliferation in vitro and in vivo. We also found that COL8A1 promotes the epithelial-mesenchymal transition process and may mediate the activation of the ERK pathway through SHC1. In addition, immune infiltration analysis showed that COL8A1 was closely associated with macrophages in glioma tissues, significantly suppressing the signaling of M1-like -type macrophages and enhancing the signaling of M2-like -type macrophages. COL8A1 was first found to be associated with prognosis, progression, and immune microenvironment of glioma and may serve as a new marker of prognosis and a therapeutic target.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BackgroundCancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.MethodsUtilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan–Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.ResultsThe research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.ConclusionThe identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing to Identify an Immunogenic Cell Death-Related 5-Gene Prognostic Signature in Hepatocellular Carcinoma.

Immunogenic cell death (ICD) can enhance the potency of immunotherapy in cancer treatment. Nevertheless, it is ambiguous how ICD-related genes (ICDRGs) contribute to hepatocellular carcinoma (HCC). Single-cell RNA sequencing (scRNA-seq) data were used to distinguish malignant cells from normal cells in the HCC tumor microenvironment(TME). Bulk RNA sequencing data was employed to acquire the landscape of the 33 ICDRGs. Unsupervised clustering identified two ICD molecular subtypes. The cellular infiltration characteristics and biological behavior in different subtypes were analyzed by ssGSEA. Subsequently, differentially expressed genes (DEGs) between the two subtypes were determined, based on which patients were classified into three gene clusters. Then, the prognostic model was constructed by Lasso-Cox analysis. Finally, we investigated the expression of risk genes in cancer cell line encyclopedia (CCLE) and validated the function of NKX3-2 in vitro experiments. ICD scores and ICDRGs expression in malignant cells were significantly lower than in normal cells by scRNA-seq analysis. ICD-high subtype was characterized by ICD-related gene overexpression and high levels of immune infiltration abundance and immune checkpoints; Three DEGs-related gene clusters were likewise strongly linked to stromal and immunological activation. In the ICD-related prognostic model consisting of NKX3-2, CHODL, MMP1, NR0B1, and CTSV, the low-risk group patients had a better endpoint and displayed increased susceptibility to immunotherapy and chemotherapeutic drugs like 5-Fluorouracil, afatinib, bortezomib, cediratinib, lapatinib, dasatinib, gefitinib and crizotinib. Moreover, NKX3-2 amplification in HCC samples has been verified by experiments, and its disruption suppressed the proliferation and invasion of tumor cells. Our study highlighted the potential of the ICDRGs risk score as a prognostic indicator to aid in the accurate diagnosis and immunotherapy sensitivity of HCC.

A hypoxia–glycolysis–lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

BackgroundLiver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC.MethodsThis study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes.ResultsWe classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1.ConclusionThe hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Immune cell infiltration and drug response in glioblastoma multiforme: insights from oxidative stress-related genes

BackgroundGBM, also known as glioblastoma multiforme, is the most prevalent and lethal type of brain cancer. The cell proliferation, invasion, angiogenesis, and treatment of gliomas are significantly influenced by oxidative stress. Nevertheless, the connection between ORGs and GBM remains poorly comprehended. The objective of this research is to investigate the predictive significance of ORGs in GBM and their potential as targets for therapy.MethodsWe identified differentially expressed genes in glioma and ORGs from public databases. A risk model was established using LASSO regression and Cox analysis, and its performance was evaluated with ROC curves. We then performed consistent cluster analysis on the model, examining its correlation with immunity and drug response. Additionally, PCR, WB and IHC were employed to validate key genes within the prognostic model.Results9 ORGs (H6PD, BMP2, SPP1, HADHA, SLC25A20, TXNIP, ACTA1, CCND1, EEF1A1) were selected via differential expression analysis, LASSO and Cox analysis, and incorporated into the risk model with high predictive accuracy. Enrichment analyses using GSVA and GSEA focused predominantly on malignancy-associated pathways. Subtype C of GBM had the best prognosis with the lowest risk score. Furthermore, the model exhibited a strong correlation with the infiltration of immune cells and had the capability to pinpoint potential targeted therapeutic medications for GBM. Ultimately, we selected HADHA for in vitro validation. The findings indicated that GBM exhibits a significant upregulation of HADHA. Knockdown of HADHA inhibited glioma cell proliferation and diminished their migration and invasion capacities and influenced the tumor growth in vivo.ConclusionThe risk model, built upon 9 ORGs and the identification of GBM subtypes, suggests that ORGs have a broad application prospect in the clinical immunotherapy and targeted drug treatment of GBM. HADHA significantly influences the development of gliomas, both in vivo and in vitro.

Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients

BackgroundLiver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC. MethodsSingleR's annotations combined with Seurat was used to automatically annotate the single-cell clustering results of the LIHC dataset GSE166635 downloaded from the Gene Expression Omnibus (GEO) database and to identify clusters related to exhausted T cells. Patients were classified using ConsensusClusterPlus package. Next, weighted gene co-expression network analysis (WGCNA) package was employed to distinguish key gene module, based on which least absolute shrinkage and selection operator (Lasso) and multi/univariate cox analysis were performed to construct a RiskScore system. Kaplan-Meier (KM) analysis and receiver operating characteristic curve (ROC) were employed to evaluate the efficacy of the model. To further optimize the risk model, a nomogram capable of predicting immune infiltration and immunotherapy sensitivity in different risk groups was developed. Expressions of genes were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and immunofluorescence and Cell Counting Kit-8 (CCK-8) were performed for analyzing cell functions. ResultsWe obtained 18,413 cells and clustered them into 7 immune and non-immune cell subpopulations. Based on highly variable genes among T cell exhaustion clusters, 3 molecular subtypes (C1, C2 and C3) of LIHC were defined, with C3 subtype showing the highest score of exhausted T cells and a poor prognosis. The Lasso and multivariate cox analysis selected 7 risk genes from the green module, which were closely associated with the C3 subtype. All the patients were divided into low- and high-risk groups based on the medium value of RiskScore, and we found that high-risk patients had higher immune infiltration and immune escape and poorer prognosis. The nomogram exhibited a strong performance for predicting long-term LIHC prognosis. In vitro experiments revealed that the 7 risk genes all had a higher expression in HCC cells, and that both liver HCC cell numbers and cell viability were reduced by knocking down MMP-9. ConclusionWe developed a RiskScore model for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent prognostic factor could improve the clinical treatment for LIHC patients.

Comprehensive Analysis of the Expression of Cell Adhesion Molecules Genes in Hepatocellular Carcinoma and their Prognosis, and Biological Significance.

Collagen-related cell adhesion molecules (CAMs) are a major component of the extracellular matrix (ECM) and often accumulate in the liver during chronic liver disease or hepatocellular carcinoma (HCC). In this study we identified several promising collagens related to CAMs that may be of clinical use for the diagnosis and prognosis of HCC. We obtained multi-omics data including RNA sequencing (RNA-Seq) data, microarray data, proteomic data from the TCGA, GEO databases, GTEx, and NODE. Bioinformatics analyses were then performed to investigate correlations between the expression patterns of significant genes and HCC. Tumor tissue and para-cancerous tissue samples from HCC patients were also used to validate the results using RT-PCR. A literature research and LASSO-COX analysis identified three significant collagen-related CAM genes: SERPINH1, DCN, and ITGB1. Immunohistochemistry images in the Human Protein Atlas Project database showed that SERPINH1 and ITGB1 proteins were moderately or highly expressed in HCC tumor tissues compared to para-cancerous tissue, whereas DCN expression was lower in HCC tumor tissue. These results were validated by RT-PCR. Low- and high-risk groups of HCC patients were distinguished by the logistic panel in the TCGA database. These showed significantly different prognosis, clinicopathological features, and immune cell infiltration. Logistic regression was used to construct predictive models based on the individual expression levels of DCN, SERPINH1, and ITGB1. These showed highly accurate diagnostic ability (AUC = 0.987). The current findings suggest that the collagen-related CAMs DCN, SERPINH1, and ITGB1 may be potential therapeutic targets in HCC. Logistic panels of DCN, SERPINH1 and ITGB1 could serve as non-invasive and effective diagnostic biomarkers for HCC. Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (https://clinicaltrials.gov/).