Deciphering the oncogenic potential of ADAM9 in hepatocellular carcinoma through bioinformatics and experimental approaches.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. This study investigates the role and mechanisms of ADAM9 as a biomarker and potential therapeutic target in HCC. Utilizing RNA-sequencing data and clinicopathological characteristics from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we conducted survival and meta-analyses, functional enrichment, and immune infiltration studies.

A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study.

Background: For clinical decision making, it is crucial to identify patients with stage IV non-small cell lung cancer (NSCLC) who may benefit from tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). In this study, a deep learning-based system was designed and validated using pre-therapy computed tomography (CT) images to predict the survival benefits of EGFR-TKIs and ICIs in stage IV NSCLC patients.

Methods: This retrospective study collected data from 570 patients with stage IV EGFR-mutant NSCLC treated with EGFR-TKIs at five institutions between 2010 and 2021 (data of 314 patients were from a previously registered study), and 129 patients with stage IV NSCLC treated with ICIs at three institutions between 2017 and 2021 to build the ICI test dataset.