AIMC Topic: Gene Expression Regulation, Neoplastic

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Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.

Journal of cellular and molecular medicine
Oral cancer is among the most prevalent malignant tumours worldwide; prognosis can be affected by several factors, including molecular subtypes, immune microenvironment and clinical characteristics. In this study, we aimed to apply machine learning m...

Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma.

Computers in biology and medicine
BACKGROUND: The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby provi...

Cancer type and survival prediction based on transcriptomic feature map.

Computers in biology and medicine
This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA data...

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Interdisciplinary sciences, computational life sciences
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (...

Artificial intelligence-driven microRNA signature for early detection of gastric cancer: discovery and clinical functional exploration.

British journal of cancer
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with late-stage diagnoses frequently leading to poor outcomes. This underscores the need for effective early-stage gastric cancer (ESGC) diagnostics.

Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Scientific reports
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades, the identification of dependable predictive biomarkers for platinum response in clinical practice h...

Integration of Bulk RNA and Single-Cell Analyses Reveal Distinct Expression Patterns of Anoikis-Related Genes and the Immunosuppressive Role of NQO1 Macrophages in Hepatocellular Carcinoma.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes involved remain to be identified. This study aimed to investigate the prognostic value and impact of anoi...

Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.

Cell biology and toxicology
BACKGROUND: Macrophages are closely associated with the progression of Kidney renal clear cell carcinoma (KIRC) and can influence programmed cell death (PCD) of tumour cells. To identify prognostic biomarkers for KIRC, it is essential to investigate ...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Medicine
Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identi...