The activity of cuproptosis pathway calculated by AUCell algorithm was employed to construct cuproptosis landscape in lung adenocarcinoma

Abstract

Cuproptosis is a recently described copper-dependent cell death pathway. Consequently, there are still few studies on lung adenocarcinoma (LUAD)-related cuproptosis, and we aimed to deepen in this matter. In this study, data from 503 patients with lung cancer from the TCGA-LUAD cohort data collection and 11 LUAD single-cells from GSE131907 as well as from 10 genes associated with cuproptosis were analyzed. The AUCell R package was used to determine the copper-dependent cell death pathway activity for each cell subpopulation, calculate the CellChat score, and display cell communication for each cell subpopulation. The PROGENy score was calculated to show the scores of tumor-related pathways in different cell populations. GO and KEGG analyses were used to calculate pathway activity. Univariate COX and random forest analyses were used to screen prognosis-associated genes and construct models. The ssGSEA and xCell algorithms were used to calculate the immunocyte infiltration score. Based on data from the GDSC database, the drug sensitivity score was calculated using oncoPredict. Finally, in vitro experiments were performed to determine the role of TLE1, the most important gene in the prognostic model. The 11 LUAD single-cell samples were classified into 8 different cell populations, from which epithelial cells showed the highest copper-dependent cell death pathway activity. Epithelial cell subsets were significantly positively correlated with MAKP, hypoxia, and other pathways. In addition, cell subgroup communication showed highly active collagen and APP pathways. Using the Findmark algorithm, differentially expressed genes (DEGs) between epithelial and other cell types were identified. Combined with the bulk data in the TCGA-LUAD database, DEGs were enriched in pathways such as EGFR tyrosine kinase inhibitor resistance, Hippo signaling pathway, and tight junction. Subsequently, we selected 4 genes (out of 112) with prognostic significance, ANKRD29, RHOV, TLE1, and NPAS2, and used them to construct a prognostic model. The high- and low-risk groups, distinguished by the median risk score, showed significantly different prognoses. Finally, we chose TLE1 as a biomarker based on the relative importance score in the prognostic model. In vitro experiments showed that TLE1 promotes tumor proliferation and migration and inhibits apoptosis.

Multi-gradient Permutation Survival Analysis Identifies Mitosis and Immune Signatures Steadily Associated with Cancer Patient Prognosis

“…Many of these genes were previously reported to associate with tumorigenesis 18,29–40 . For example, TLE1 is known as a transcriptional repressor that promotes cell proliferation, migration, and inhibits apoptosis in LUAD 41,42 . Additionally, PGK1 , a key enzyme in the glycolytic process, has been shown to promote cell proliferation, migration, and invasion in multiple cancers 43,44 .…”

Multiomics blueprint of PANoptosis in deciphering immune characteristics and prognosis stratification of glioma patients

“…The dimensionality reduction method allowed for the separation of the data into seven distinct cell populations, namely OPCs, myeloid (myeloid cells), neoplastic (tumor cells), oligodendrocytes (oligodendrocytes), astrocytes (astrocytes), vascular (vascular cells) and neurons (neuronal cells). To examine cell–cell communication, CellChat scores 30 were calculated for the eight cell populations in the reduced‐dimensional grouping. Moreover, gene set enrichment scoring was performed on the PANoptosis pathway using the AddModuleScore function of the Seruat package, 31 resulting in the computation of PANoptosis scores.…”