A novel molecular subtyping based on multi-omics analysis for prognosis predicting in colorectal melanoma: A 16-year prospective multicentric study

Liu, Chuan; Cheng, Xiaofei; Han, Kai; Hong, Libing; Hao, Shuqiang; Sun, Xuqi; Xu, Jingfeng; Li, Benfeng; Jin, Dongqing; Tian, Weihong; Jin, Yuzhi; Wang, Yanli; Fang, Weijia; Bao, Xuanwen; Zhao, Peng; Chen, Dong

doi:10.1016/j.canlet.2024.216663

Cited by 3 publications

(2 citation statements)

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“…CRC is composed of several molecularly distinct subtypes, each characterized by specific genetic and epigenetic alterations. These subtypes have demonstrated substantial influence over prognosis, response to therapy, and overall clinical outcomes ( 12 , 13 ). Recent studies have leveraged gene profile datasets to develop angiogenesis-related molecular subtypes, with the aim of investigating associations with CRC’s clinical characteristics, prognosis, and tumor microenvironment (TME) ( 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Wei,

Xie,

Liu

et al. 2024

Front. Oncol.

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BackgroundAngiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis.MethodsCRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay.ResultsTwo distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls.ConclusionThis study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

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Section: Introductionmentioning

confidence: 99%

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Wei,

Xie,

Liu

et al. 2024

Front. Oncol.

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“…CRC is composed of several molecularly distinct subtypes, each characterized by specific genetic and epigenetic alterations. These subtypes have demonstrated substantial influence over prognosis, response to therapy, and overall clinical outcomes (12,13). Recent studies have leveraged gene profile datasets to develop angiogenesis-related molecular subtypes, with the aim of investigating associations with CRC's clinical characteristics, prognosis, and tumor microenvironment (TME) (14, 15).…”

Section: Introductionmentioning

confidence: 99%

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Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

Zhang,

Wang,

Qian

2024

Front. Immunol.

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BackgroundLung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.MethodsData on clinical and pathological characteristics, genetic alterations, DNA methylation patterns, and the expression of mRNA, lncRNA, and microRNA, along with somatic mutations in LUAD patients, were gathered from the TCGA and GEO datasets. A computational workflow was utilized to merge multi-omics data from LUAD patients through 10 clustering techniques, which were paired with 10 machine learning methods to pinpoint detailed molecular subgroups and refine a prognostic risk model. The disparities in somatic mutations, copy number alterations, and immune cell infiltration between high- and low-risk groups were assessed. The effectiveness of immunotherapy in patients was evaluated through the TIDE and SubMap algorithms, supplemented by data from various immunotherapy groups. Furthermore, the Cancer Therapeutics Response Portal (CTRP) and the PRISM Repurposing dataset (PRISM) were employed to investigate new drug treatment approaches for LUAD. In the end, the role of SLC2A1 in tumor dynamics was examined using RT-PCR, immunohistochemistry, CCK-8, wound healing, and transwell tests.ResultsBy employing multi-omics clustering, we discovered two unique cancer subtypes (CSs) linked to prognosis, with CS2 demonstrating a better outcome. A strong model made up of 17 genes was created using a random survival forest (RSF) method, which turned out to be an independent predictor of overall survival and showed reliable and impressive performance. The low-risk group not only had a better prognosis but also was more likely to display the “cold tumor” phenotype. On the other hand, individuals in the high-risk group showed a worse outlook and were more likely to respond positively to immunotherapy and six particular chemotherapy medications. Laboratory cell tests demonstrated that SLC2A1 is abundantly present in LUAD tissues and cells, greatly enhancing the proliferation and movement of LUAD cells.ConclusionsThorough examination of multi-omics data offers vital understanding and improves the molecular categorization of LUAD. Utilizing a powerful machine learning system, we highlight the immense potential of the riskscore in providing individualized risk evaluations and customized treatment suggestions for LUAD patients.

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A novel molecular subtyping based on multi-omics analysis for prognosis predicting in colorectal melanoma: A 16-year prospective multicentric study

Cited by 3 publications

References 65 publications

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Table_1.docx

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

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