Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.

Background: Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up.

Extracorporeal Membrane Oxygenation Impact on Host Transcriptomic Response in Severe Coronavirus.

Background: Evidence suggests that patients critically ill with COVID-19 have a dysregulated host immune response that contributes to end-organ damage. Extracorporeal membrane oxygenation (ECMO) has been used in this population with varying degrees of success. This study was performed to evaluate the impact of ECMO on the host immunotranscriptomic response in these patients.