Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer.

Background: Non-small cell lung cancer (NSCLC) represents one of the most prevalent forms of lung cancer, with a five-year survival rate of 21.7%. There is an urgent need to identify pertinent biomarkers to inform the diagnosis and prognosis of tumors, particularly those that can be applied to different age groups.

Sequential treatment of anti-PD-L1 therapy prior to anti-VEGFR2 therapy contributes to more significant clinical benefits in non-small cell lung cancer.

Objective: Anti-angiogenic therapy and immune checkpoint blockade therapy are currently important treatments for non-small cell lung cancer. However, the combined use of the two therapies is controversial, and few studies have investigated the effects of different time sequences of the two therapies on treatment outcomes.

Methods: The tumor-bearing mouse model was established and the mice were divided into four groups, including AA-ICB sequence group, ICB-AA sequence group, synchronization group and the control group.

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.