Dataset Information

Machine learning developed a programmed cell death signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.

ABSTRACT:

Background

Lung cancer is the leading cause of cancer-related deaths worldwide with poor prognosis. Programmed cell death (PCD) plays a crucial function in tumor progression and immunotherapy response in lung adenocarcinoma (LUAD).

Methods

Integrative machine learning procedure including 10 methods was performed to develop a prognostic cell death signature (CDS) using TCGA, GSE30129, GSE31210, GSE37745, GSE42127, GSE50081, GSE68467, GSE68571, and GSE72094 dataset. The correlation between CDS and tumor immune microenvironment was evaluated using various methods and single cell analysis. qRT-PCR and CCK-8 assay were conducted to explore the biological functions of hub gene.

Results

The prognostic CDS developed by Lasso + survivalSVM method was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting the clinical outcome of LUAD and served as an independent risk factor in TCGA and 8 GEO datasets. The C-index of CDS was higher than that of clinical stage and many developed signatures for LUAD. LUAD patients with low CDS score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score, indicating a better immunotherapy benefit. Single cell analysis revealed a strong and frequent communication between epithelial cells and cancer-related fibroblasts by specific ligand-receptor pairs, including COL1A2-SDC4 and COL1A2-SDC1. Vitro experiment showed that SLC7A5 was upregulated in LUAD and knockdown of SLC7A5 obviously suppressed tumor cell proliferation.

Conclusion

Our study developed a novel CDS for LUAD. The CDS served as an indicator for predicting the prognosis and immunotherapy benefits of LAUD patients.

SUBMITTER: Ding D

PROVIDER: S-EPMC10511492 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Machine learning developed a programmed cell death signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.

Ding Dongxiao D Wang Liangbin L Zhang Yunqiang Y Shi Ke K Shen Yaxing Y

Translational oncology 20230916

<h4>Background</h4>Lung cancer is the leading cause of cancer-related deaths worldwide with poor prognosis. Programmed cell death (PCD) plays a crucial function in tumor progression and immunotherapy response in lung adenocarcinoma (LUAD).<h4>Methods</h4>Integrative machine learning procedure including 10 methods was performed to develop a prognostic cell death signature (CDS) using TCGA, GSE30129, GSE31210, GSE37745, GSE42127, GSE50081, GSE68467, GSE68571, and GSE72094 dataset. The correlation ...[more]

PMID: 37722290

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Project description:Backgroundlung adenocarcinoma (LUAD) remains one of the most common and lethal malignancies with poor prognosis. Programmed cell death (PCD) is an evolutionarily conserved cell suicide process that regulates tumorigenesis, progression, and metastasis of cancer cells. However, a comprehensive analysis of the role of PCD in LUAD is still unavailable.MethodsWe analyzed multi-omic variations in PCD-related genes (PCDRGs) for LUAD. We used cross-validation of 10 machine learning algorithms (101 combinations) to synthetically develop and validate an optimal prognostic cell death score (CDS) model based on the PCDRGs expression profile. Patients were classified based on their median CDS values into the high and low-CDS groups. Next, we compared the differences in the genomics, biological functions, and tumor microenvironment of patients between both groups. In addition, we assessed the ability of CDS for predicting the response of patients from the immunotherapy cohort to immunotherapy. Finally, functional validation of key genes in CDS was performed.ResultsWe constructed CDS based on four PCDRGs, which could effectively and consistently stratify patients with LUAD (patients with high CDS had poor prognoses). The performance of our CDS was superior compared to 77 LUAD signatures that have been previously published. The results revealed significant genetic alterations like mutation count, TMB, and CNV were observed in patients with high CDS. Furthermore, we observed an association of CDS with immune cell infiltration, microsatellite instability, SNV neoantigens. The immune status of patients with low CDS was more active. In addition, CDS could be reliable to predict therapeutic response in multiple immunotherapy cohorts. In vitro experiments revealed that high DNA damage inducible transcript 4 (DDIT4) expression in LUAD cells mediated protumor effects.ConclusionCDS was constructed based on PCDRGs using machine learning. This model could accurately predict patients' prognoses and their responses to therapy. These results provide new promising tools for clinical management and aid in designing personalized treatment strategies for patients with LUAD.

Project description:BackgroundTumor development involves the critical role of programmed cell death (PCD), but the correlation between colon adenocarcinoma (COAD) and PCD-related genes is not clear.MethodsSubtyping analysis of COAD was performed by consensus clustering based on The Cancer Genome Atlas (TCGA), with the AC-ICAM queue from the cBioportal database as a validation set. Immune infiltration of the samples was evaluated using CIBERSORT and Microenvironment Cell Populations (MCP)-counter algorithms. Patients' immunotherapy response was predicted by the TIDE and aneuploidy scores. Pathway enrichment analysis was conducted with gene set enrichment analysis (GSEA). A RiskScore model was established with independent prognostic PCD-related genes filtered by Cox regression analysis. The mafCompare function was used to compare the differences in mutation rates of somatic genes. Wound healing, transwell assays and Flow cytometer were applied to measure the cell migration, invasion and apoptosis.ResultsThe patients were grouped into S1 and S2 subtypes based on a total of 21 PCD genes associated with the prognostic outcomes of COAD. Specifically, patients of S1 subtype were mainly related to the pathway activation in tumor invasion and deterioration and had a worse prognosis. A RiskScore model was established based on six prognostic genes, including two protective genes (ATOH1, ZG16) and four risk genes (HSPA1A, SEMA4C, CDKN2A, ARHGAP4). Notably, silencing of CDKN2A inhibited the activity of migration and invasion and promoted apoptosis of tumor cells. Based on the RiskScore model, the patients were grouped into high- and low-risk groups. Independent prognostic factors, namely, Age, pathologic_M, pathologic_stage, and RiskScore, were integrated to develop a nomogram with strong good prediction performance. High-risk group had high-expressed immune checkpoint genes and higher TIDE scores, showing a strong immune escape ability and less active immunotherapy response. Compared to the low-risk group, TP53 exhibited a higher rate of somatic mutation in the high-risk group.ConclusionWe constructed a RiskScore model with six PCD-related genes for the prognostic assessment of COAD, providing a valuable insight into the exploration of new targets for the prognostic improvement in COAD.

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

Machine learning developed a programmed cell death signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.

Background

Methods

Results

Conclusion

Publications

Machine learning developed a programmed cell death signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.

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