Identification of therapeutic targets Based on the Reversal of Gene Expression Profiles in lung adenocarcinoma malignant single-cells.

• Grant number: 23/03020-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2023
• End date: July 15, 2024
• Field of knowledge: Biological Sciences - Genetics - Human and Medical Genetics
• Principal Investigator: Robson Francisco Carvalho
• Grantee: Isabeli Francischini da Silva
• Host Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

Lung cancer is the second most prevalent and deadly cancer, accounting for 18% of all cancer deaths in 2020. Among the histological types of lung cancer, non-small cell lung cancer is the most common (~85%), with the subtype lung adenocarcinoma (LUAD) standing out. This subtype originates from lung glandular cells and affects ~40% of patients. The treatment is established according to tumor staging, which ranges from I to IV, with the latter being the most advanced. Although treatment includes localized surgery, chemotherapy, and radiation therapy, however, the chances of survival and cure are small. The implementation of new drugs requires approval from federal agencies, a process in which only 5% of tests are approved. Therefore, drug repositioning strategy can be a quick alternative for more effective treatment, because uses already approved drugs for a different therapy than intended. The reverse gene expression strategy aims to combine the gene expression of a disease with drug-induced gene expression profiles to identify new possible therapeutic targets that can be repositioned. Although some studies have been conducted on drug repositioning in conventional RNA-Seq data, none have been carried out with such precision at the level of single cells (single-cell RNA-Seq; scRNA-Seq) for LUAD. In this study, scRNA-Seq analysis will be performed on LUAD tumor samples (N=7) publicly available in the Curated Cancer Cell Atlas (3CA) database. Malignant cells (N=11,055) and normal lung epithelial cells (N=28,127) from non-cancerous lung tissue samples (N=4) will be selected for differential expression analysis using the Seurat v4.2.0 package. Genes with increased and decreased expression in neoplastic cells will be selected considering an adjusted p-value < 0.05. This list of genes will be used for gene expression reversal analysis using the OCTAD webtool, which will indicate approved drugs with the greatest potential to reverse the expression profile of neoplastic cells. This study aims to accelerate the identification of new drugs for lung cancer treatment that are effective and have a higher chance of survival.