Evidence map›Paper›PMID 38935217›Full record

ArticleFunctional & integrative genomics2024

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Tao Han, Ying Bai, Yafeng Liu, Yunjia Dong, Chao Liang, Lu Gao, Jiawei Zhou, Jianqiang Guo, Jing Wu, Dong Hu

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Article in Functional & integrative genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Article
  4. Article
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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Tao Han *School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Ying Bai *School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China. by0319_cpu@163.com.
Yafeng LiuSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Yunjia DongSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Chao LiangSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Lu GaoSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Jiawei ZhouSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Jianqiang GuoSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
Jing WuSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China. wujing8008@126.com.
Dong HuSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China. dhu@aust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

Indexed as

Adenocarcinoma of LungImmunotherapyLung NeoplasmsMachine LearningTumor MicroenvironmentBiomarkers, TumorGene Expression Regulation, NeoplasticGenomicsHumansMultiomicsPrognosisBiomarkers, TumorBioinformaticsDrug screeningImmunotherapyLung adenocarcinomaMachine learningMulti-omicsPrognosis

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.