Identifying cancer driver genes from functional genomics screens

Degradome-focused RNA interference screens to identify proteases important for breast cancer cell growth

Proteases are known to promote or impair breast cancer progression and metastasis. However, while a small number of the 588 human and 672 murine protease genes have been extensively studied, others were neglected. For an unbiased functional analysis of all genome-encoded proteases, i.e., the degradome, in breast cancer cell growth, we applied an inducible RNA interference library for protease-focused genetic screens. Importantly, these functional screens were performed in two phenotypically different murine breast cancer cell lines, including one stem cell-like cell line that showed phenotypic plasticity under changed nutrient and oxygen availability. Our unbiased genetic screens identified 252 protease genes involved in breast cancer cell growth that were further restricted to 100 hits by a selection process. Many of those hits were supported by literature, but some proteases were novel in their functional link to breast cancer. Interestingly, we discovered that the environmental conditions influence the degree of breast cancer cell dependency on certain proteases. For example, breast cancer stem cell-like cells were less susceptible to depletion of several mitochondrial proteases in hypoxic conditions. From the 100 hits, nine proteases were functionally validated in murine breast cancer cell lines using individual knockdown constructs, highlighting the high reliability of our screens. Specifically, we focused on mitochondrial processing peptidase (MPP) subunits alpha (Pmpca) and beta (Pmpcb) and discovered that MPP depletion led to a disadvantage in cell growth, which was linked to mitochondrial dysfunction.

Comprehensive Analysis of Immune-Related Metabolic Genes in Lung Adenocarcinoma

PURPOSE

The immunotherapy of lung adenocarcinoma (LUAD) has received much attention in recent years and metabolic reprogramming is linked to immune infiltration in the tumor microenvironment. Therefore, it is indispensable to dissect the role of immune-related metabolic genes in lung adenocarcinoma.

METHODS

In this study, we screened immune-related genes by Pearson correlation. The function of these genes was explored by gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. The differently expressed immune-related genes were analyzed by Limma. Furthermore, the LUAD patients were clustered based on immune-related genes through consensus clustering. The Unicox was used to identify survival-immune-related metabolic genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to optimize the gene sets. A prediction model was constructed and tested. The potential therapeutic target was selected based on two criteria, these immune-related metabolic genes that were highly expressed in tumor tissues and negatively correlated with the survival of patients in LUAD. Quantitative real-time PCR (qRT-PCR) was used for in vitro experimental validations.

RESULTS

We identified 346 immune-related genes, mainly involved in arachidonic acid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling. Moreover, a total of 141 immune-related genes were dysregulated between tumor and normal tissues. We clustered three subtypes of LUAD based on immune-related metabolic genes and these subtypes exhibited different survival and immune status. We found Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) as a potential therapeutic target, which is positively correlated with the cyclin-dependent kinase family of genes.

CONCLUSION

We comprehensively analyzed the immune-related metabolic genes in LUAD. RRM2 was determined as a promising metabolic checkpoint for lung adenocarcinoma.