Dataset Information

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

ABSTRACT: Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tumour cell landscape. Through clustering analysis, we identified six distinct cell clusters and investigated their differentiation and metabolic heterogeneity using multi-omics approaches. Notably, cytotrace analysis and pseudotime trajectories revealed distinct stages of tumour cell differentiation, which have implications for patient survival. By leveraging markers from these clusters, we developed a tumour cell-specific machine learning model (TCM). This model not only predicts patient outcomes and responses to immunotherapy, but also distinguishes between genomically stable and unstable tumours and identifies inflamed ('hot') versus non-inflamed ('cold') tumours. Intriguingly, the TCM score showed a strong association with TOMM40, which we experimentally validated as an oncogene promoting tumour proliferation, invasion and migration. Overall, our findings introduce a novel biomarker score that aids in selecting melanoma patients for improved prognoses and targeted immunotherapy, thereby guiding clinical treatment decisions.

SUBMITTER: Cheng W

PROVIDER: S-EPMC11272603 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Cheng Wenhao W Ni Ping P Wu Hao H Miao Xiaye X Zhao Xiaodong X Yan Dali D

Journal of cellular and molecular medicine 20240701 14

Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tumour cell landscape. Through clustering analysis, we identified six distinct cell clusters and investigated their differentiation and metabolic heterogeneity using multi-omics approaches. Notably, cytotrace analysis and pseudotime trajectories reveal ...[more]

PMID: 39054572

Similar Datasets

Project description:BackgroundIntegrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system.Methods and findingsCancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10-23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman's rank correlation coefficient = 0.8316; p-value = 4.5×10-13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10-43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems.ConclusionsThe EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.

Project description:BackgroundCutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified.MethodsThe RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs' expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs' expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT).ResultsWe constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy.ConclusionThis risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

Dataset Information

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Publications

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets