AIMC Topic: Gene Expression Regulation, Neoplastic

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Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

Scientific reports
The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and pro...

A novel machine learning-based immune prognostic signature for improving clinical outcomes and guiding therapy in colorectal cancer: an integrated bioinformatics and experimental study.

BMC cancer
Immune cells are pivotal components in the tumor microenvironment (TME), which can interact with tumor cells and significantly influence cancer progression and therapeutic outcomes. Therefore, classifying cancer patients based on the status of immune...

Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma.

BMC cancer
OBJECTIVE: Neuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. Th...

SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma.

Scientific reports
Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction presenting ongoing challenges. In this study, we integrated data from multiple public datasets, including...

Glycosylation profiling of triple-negative breast cancer: clinical and immune correlations and identification of LMAN1L as a biomarker and therapeutic target.

Frontiers in immunology
INTRODUCTION: Breast cancer (BC) is the most prevalent malignant tumor in women, with triple-negative breast cancer (TNBC) showing the poorest prognosis among all subtypes. Glycosylation is increasingly recognized as a critical biomarker in the tumor...

Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.

Nature biomedical engineering
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the dev...

PRAF2 as a novel biomarker for breast cancer with machine learning and experimentation validation.

BMC cancer
BACKGROUND: Breast cancer (BC) is the most prevalent malignancy in women. Potential therapeutic targets for BC are of great significance. In our previous study, we found that prenylated rab acceptor 1 domain family member 2 (PRAF2) is an oncogene in ...

Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway.

Frontiers in immunology
BACKGROUND: Recent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironme...