Back

ANO1 expression is associated with male survival in lung squamous cell carcinoma

Afolabi, O. I.

2024-08-14 oncology
10.1101/2024.08.14.24311973 medRxiv
Show abstract

Lung cancer is the leading cause of cancer deaths, with lung squamous cell carcinoma (LUSC) accounting for a substantial proportion of cases. LUSC exhibits significant variability in patient outcomes, influenced by clinicopathological factors such as stage, age, and sex, with men exhibiting higher rates of incidence and poorer outcomes compared to women. Therefore, prognosis modeling and customized approaches to LUSC therapy require the characterization of the biological mechanisms that differentiate tumors and patient outcomes across sexes. Using data from The Cancer Genome Atlas (TCGA), this study characterized gene expression patterns that distinguish male and female LUSC. Specifically, differential expression, survival, and Cox regression analyses assessed the prognostic value of ANO1 (Anoctamin 1) expression and methylation in LUSC. Analyses uncovered a significant overexpression of ANO1 in a subset of male LUSC tumors compared to a much lower expression in normal lung and female LUSC tumors. High ANO1 expression was associated with poor survival outcomes in male subjects. High ANO1 gene body methylation mirrored gene expression and was similarly associated with poor survival outcomes. ANO1s prognostic value remained significant in a multivariate Cox regression analysis, establishing it as an independent prognostic biomarker. ANO1s marked sex-specific differences in expression and prognostic value indicate its role in the sex disparity of LUSC survival, highlighting its potential as a biomarker and a target for sex-specific customized therapies.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.