AIMC Topic: Gene Expression Regulation, Neoplastic

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Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...

Establishment of an amino acid metabolism related signature for prognostic and therapeutic sensitivity prediction in breast cancer by machine learning.

PloS one
Amino acid metabolism plays a critical role in tumor growth and immune regulation, yet its comprehensive function in breast cancer remains underexplored. We developed an amino acid metabolism-related gene signature (AAMRGS) to predict prognosis and t...

Decoding the germline genetic architecture of prostate cancer at a single cell resolution.

PLoS genetics
Prostate cancer exhibits a strong familial association, and its heritability indicates a significant contribution from germline variants. While genome-wide association studies (GWAS) have identified common germline variants associated with prostate c...

Multi-omics and machine learning refine HCC molecular subtypes and prognosis based on liquid-liquid phase separation related genes.

Scientific reports
Accumulating evidence has demonstrated that biological processes associated with liquid-liquid phase separation (LLPS) play a critical role in cancer development. However, the effect of LLPS on hepatocellular carcinoma (HCC) remains largely unknown. ...

Unraveling diethyl phthalate-induced prostate carcinogenesis: core targets revealed by integrated network toxicology, machine learning, and structural validation.

Human genomics
PURPOSE: Diethyl phthalate (DEP), a widely distributed environmental contaminant, is epidemiologically linked to prostate cancer (PCa). However, its molecular mechanisms beyond endocrine disruption remain poorly defined. We aimed to investigate the c...

Unraveling tissue-specific molecular targets of dihydroartemisinin in non-small cell lung cancer: an integrative machine learning and network pharmacology approach.

Medical oncology (Northwood, London, England)
Non-small cell lung cancer (NSCLC) presents significant therapeutic challenges due to resistance and immune evasion. Dihydroartemisinin (DHA), a derivative of artemisinin, exhibits broad anti-tumor activity, but its molecular targets and mechanisms i...

A novel prognostic model for lung squamous cell carcinoma based on multi-omics analysis and machine learning.

PloS one
Lung squamous-cell carcinoma (LUSC) is a highly aggressive malignancy with a poor prognosis. Tertiary lymphoid structures (TLS) play a crucial role in the immune response and significantly influence the efficacy of immunotherapy. However, the prognos...

Predicting the influence of homologous recombination repair deficiency genes on glioma heterogeneity and patient prognosis using multi-omics analysis and machine learning.

PloS one
BACKGROUND: Glioma is the most common malignant tumor of the central nervous system, and homologous recombination deficiency (HRD) may play a crucial role in its progression. Our study aimed to predict the impact of HRD on glioma heterogeneity and pa...

Systems pharmacology approaches decipher the anti-cancer efficacy of ethnopharmacological agents in hepatocellular carcinoma.

Scientific reports
Hepatocellular carcinoma (HCC) poses a significant global health burden with limited therapeutic efficacy. Chinese herbal medicines (CHMs) offer multi-target potential, yet their systematic screening and mechanistic elucidation remain challenging. We...