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Development of cancer-associated fibroblast-related gene signature for predicting the survival and immunotherapy response in lung adenocarcinoma.

ABSTRACT: The present study aims to construct a predictive model for prognosis and immunotherapy response in lung adenocarcinoma (LUAD). Transcriptome data were extracted from the Cancer Genome Atlas (TCGA), GSE41271, and IMvigor210. The weighted gene correlation network analysis was utilized to identify the hub modules related to immune/stromal cells. Then, univariate, LASSO, and multivariate Cox regression analyses were employed to develop a predictive signature based on genes of the hub module. Moreover, the association between the predictive signature and immunotherapy response was also investigated. As a result, seven genes (FGF10, SERINE2, LSAMP, STXBP5, PDE5A, GLI2, FRMD6) were screened to develop the cancer associated fibroblasts (CAFs)-related risk signature (CAFRS). LUAD patients with high-risk score underwent shortened Overall survival (OS). A strong correlation was found between CAFRS and immune infiltrations/functions. The gene set variation analysis showed that G2/M checkpoint, epithelial-mesenchymal transition, hypoxia, glycolysis, and PI3K-Akt-mTOR pathways were greatly enriched in the high-risk subgroup. Moreover, patients with higher risk score were less likely to respond to immunotherapy. A nomogram based on CAFRS and Stage presented a stronger predictive performance for OS than the single indicator. In conclusion, the CAFRS exhibited a potent predictive value for OS and immunotherapy response in LUAD.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC10292873 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Development of cancer-associated fibroblast-related gene signature for predicting the survival and immunotherapy response in lung adenocarcinoma.

Zhang Yong Y Cheng Fuyi F Ma Jinhu J Shi Gang G Deng Hongxin H

Aging 20230606 11

The present study aims to construct a predictive model for prognosis and immunotherapy response in lung adenocarcinoma (LUAD). Transcriptome data were extracted from the Cancer Genome Atlas (TCGA), GSE41271, and IMvigor210. The weighted gene correlation network analysis was utilized to identify the hub modules related to immune/stromal cells. Then, univariate, LASSO, and multivariate Cox regression analyses were employed to develop a predictive signature based on genes of the hub module. Moreove ...[more]

PMID: 37280069

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Project description:BackgroundImmunotherapy has changed the treatment landscape for lung cancer. This study aims to construct a tumor mutation-related model that combines long non-coding RNA (lncRNA) expression levels and tumor mutation levels in tumor genomes to detect the possibilities of the lncRNA signature as an indicator for predicting the prognosis and response to immunotherapy in lung adenocarcinoma (LUAD).MethodsWe downloaded the tumor mutation profiles and RNA-seq expression database of LUAD from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were extracted based on the cumulative number of mutations. Cox regression analyses were used to identify the prognostic lncRNA signature, and the prognostic value of the five selected lncRNAs was validated by using survival analysis and the receiver operating characteristic (ROC) curve. We used qPCR to validate the expression of five selected lncRNAs between human lung epithelial and human lung adenocarcinoma cell lines. The ImmuCellAI, immunophenoscore (IPS) scores and Tumor Immune Dysfunction and Exclusion (TIDE) analyses were used to predict the response to immunotherapy for this mutation related lncRNA signature.ResultsA total of 162 lncRNAs were detected among the differentially expressed lncRNAs between the Tumor mutational burden (TMB)-high group and the TMB-low group. Then, five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as tumor mutation-related candidates for constructing the prognostic prediction model. Kaplan‒Meier curves showed that the overall survival of the low-risk group was significantly better than that of the high-risk group, and the results of the GSE50081 set were consistent. The expression levels of PD1, PD-L1 and CTLA4 in the low-risk group were higher than those in the high-risk group. The IPS scores and TIDE scores of patients in the low-risk group were significantly higher than those in the high-risk group.ConclusionOur findings demonstrated that the five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as candidates for constructing the tumor mutation-related model which may serve as an indicator of tumor mutation levels and have important implications for predicting the response to immunotherapy in LUAD.

Project description:BackgroundNicotinamide (NAM) metabolism fulfills crucial functions in tumor progression. The present study aims to establish a NAM metabolism-correlated gene (NMRG) signature to assess the immunotherapy response and prognosis of lung adenocarcinoma (LUAD).MethodsThe training set and validation set (the GSE31210 dataset) were collected The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Molecular subtypes of LUAD were classified by consensus clustering. Mutation landscape of the top 20 somatic genes was visualized by maftools package. Subsequently, differential expression analysis was conducted using the limma package, and univariate, multivariate and LASSO regression analyses were performed on the screened genes to construct a risk model for LUAD. Next, the MCP-counter, TIMER and ESTIMATE algorithms were utilized to comprehensively assess the immune microenvironmental profile of LUAD patients in different risk groups. The efficacy of immunotherapy and chemotherapy drugs was evaluated by TIDE score and pRRophetic package. A nomogram was created by integrating RiskScore and clinical features. The mRNA expressions of independent prognostic NMRGs and the migration and invasion of LUAD cells were measured by carrying out cellular assays.ResultsTwo subtypes (C1 and C2) of LUAD were classified, with C1 subtype showing a worse prognosis than C2. The top three genes with a high mutation frequency in C1 and C2 subtypes were TTN (45.25%), FLG (25.25%), and ZNF536 (19.8%). Four independent prognostic NMRGs (GJB3, CPA3, DKK1, KRT6A) were screened and used to construct a RiskScore model, which exhibited a strong predictive performance. High-risk group showed low immune cell infiltration, high TIDE score, and worse prognosis, and the patients in this group exhibited a high drug sensitivity to Cisplatin, Erlotinib, Paclitaxel, Saracatini, and CGP_082996. A nomogram was established with an accurate predictive and diagnostic performance. GJB3, DKK1, CPA3, and KRT6A were all high- expressed in LUAD cells, and silencing GJB3 inhibited the migration and invasion of LUAD cells.ConclusionA novel NMRG signature was developed, contributing to the prognostic evaluation and personalized treatment for LUAD patients.

Project description:PurposeLung adenocarcinoma (LUAD) is a prevalent malignant tumor worldwide, with high incidence and mortality rates. However, there is still a lack of specific and sensitive biomarkers for its early diagnosis and targeted treatment. Disulfidptosis is a newly identified mode of cell death that is characteristic of disulfide stress. Therefore, exploring the correlation between disulfidptosis-related long non-coding RNAs (DRGs-lncRNAs) and patient prognosis can provide new molecular targets for LUAD patients.MethodsThe study analysed the transcriptome data and clinical data of LUAD patients in The Cancer Genome Atlas (TCGA) database, gene co-expression, and univariate Cox regression methods were used to screen for DRGs-lncRNAs related to prognosis. The risk score model of lncRNA was established by univariate and multivariate Cox regression models. TIMER, CIBERSORT, CIBERSORT-ABS, and other methods were used to analyze immune infiltration and further evaluate immune function analysis, immune checkpoints, and drug sensitivity. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of DRGs-lncRNAs in LUAD cell lines.ResultsA total of 108 lncRNAs significantly associated with disulfidptosis were identified. A prognostic model was constructed by screening 10 lncRNAs with independent prognostic significance through single-factor Cox regression analysis, LASSO regression analysis, and multiple-factor Cox regression analysis. Survival analysis of patients through the prognostic model showed that there were obvious survival differences between the high- and low-risk groups. The risk score of the prognostic model can be used as an independent prognostic factor independent of other clinical traits, and the risk score increases with stage. Further analysis showed that the prognostic model was also different from tumor immune cell infiltration, immune function, and immune checkpoint genes in the high- and low-risk groups. Chemotherapy drug susceptibility analysis showed that high-risk patients were more sensitive to Paclitaxel, 5-Fluorouracil, Gefitinib, Docetaxel, Cytarabine, and Cisplatin. Additionally, RT-PCR analysis demonstrated differential expression of DRGs-lncRNAs between LUAD cell lines and the human bronchial epithelial cell line.ConclusionsThe prognostic model of DRGs-lncRNAs constructed in this study has certain accuracy and reliability in predicting the survival prognosis of LUAD patients, and provides clues for the interaction between disulfidptosis and LUAD immunotherapy.

Dataset Information

Development of cancer-associated fibroblast-related gene signature for predicting the survival and immunotherapy response in lung adenocarcinoma.

Publications

Development of cancer-associated fibroblast-related gene signature for predicting the survival and immunotherapy response in lung adenocarcinoma.

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