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Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma.

ABSTRACT: BACKGROUND:Gliomas are the most common and malignant brain tumors. The standard therapy is surgery combined with radiotherapy, chemotherapy, and/or other comprehensive methods. However, the emergence of chemoresistance is the main obstacle in treatment and its mechanism is still unclear. METHODS:We firstly developed a multi-gene signature by integrated analysis of cancer stem cell and drug resistance related genes. The Chinese Glioma Genome Atlas (CGGA, 325 samples) and The Cancer Genome Atlas (TCGA, 699 samples) datasets were then employed to verify the efficacy of the risk signature and investigate its significance in glioma prognosis. GraphPad Prism, SPSS and R language were used for statistical analysis and graphical work. RESULTS:This signature could distinguish the prognosis of patients, and patients with high risk score exhibited short survival time. The Cox regression and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with a well-known chemotherapy impact factor-MGMT promoter methylation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell proliferation, immune response and cell stemness. These mechanisms were confirmed in patient samples. CONCLUSIONS:The signature was an independent and powerful prognostic biomarker in glioma, which would improve risk stratification and provide a more accurate assessment of personalized treatment. Additional file 8 Video abstract.

SUBMITTER: Zeng F

PROVIDER: S-EPMC6943920 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated. METHODS:Gene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed. RESULTS:According to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma. CONCLUSIONS:We profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression.

Project description:IntroductionThe introduction of Immune Checkpoint Inhibitors (ICIs) has marked a paradigm shift in treating Lung Squamous Cell Carcinoma (LUSC), emphasizing the urgent need for precise molecular biomarkers to reliably forecast therapeutic efficacy. This study aims to identify potential biomarkers for immunochemotherapy efficacy by focusing on plasma extracellular vesicle (EV)-derived long RNAs (exLRs).MethodsWe enrolled 78 advanced LUSC patients undergoing first-line immunochemotherapy. Plasma samples were collected, and exLR sequencing was conducted to establish baseline profiles. A retrospective analysis was performed on 42 patients to identify differentially expressed exLRs. Further validation of the top differentially expressed exLRs was conducted using quantitative reverse transcription PCR (qRT-PCR). Univariate Cox analysis was applied to determine the prognostic significance of these exLRs. Based on these findings, we developed a predictive signature (p-Signature).ResultsIn the retrospective analysis of 42 patients, we identified 460 differentially expressed exLRs, with pathways related to leukocyte migration notably enriched among non-responders. Univariate Cox analysis revealed 45 exLRs with prognostic significance. The top 6 protein-coding exLRs were validated using qRT-PCR, identifying CXCL8, SSH3, and SDHAF1 as differentially expressed between responders and non-responders. The p-Signature, comprising these three exLRs, demonstrated high accuracy in distinguishing responders from non-responders, with an Area Under the Curve (AUC) of 0.904 in the retrospective cohort and 0.812 in the prospective cohort.DiscussionThis study highlighted the potential of plasma exLR profiles in predicting LUSC treatment efficacy. Intriguingly, lower p-Signature scores were associated with increased abundance of activated CD4+ and CD8+ T cells, indicating a more robust immune environment. These findings suggest that the p-Signature could serve as a valuable tool in guiding personalized and effective therapeutic strategies for LUSC.

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Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma.

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