Author(s) :

Anthony Wang.

Volume/Issue :

Abstract :

Lung cancer is the leading cause of cancer death globally, with non-small cell lung cancer (NSCLC) accounting for most cases. This remains driven by its heterogeneity and metastatic potential. In this research, the primary objective is to understand the differential expression of metastasis-associated markers and differences in therapeutic potential, both of which are key to treatment outcomes. In this study, cell scoring was applied to a dataset of 20 treatment-naive NSCLC patients, which consisted of 14 adenocarcinoma (ADC) patients, 3 squamous cell carcinoma (SCC) patients, 1 combined small cell lung cancer (C-SCLC) patient, 1 patient with mixed adenocarcinoma and neuroendocrine carcinoma (MANEC), and 1 pulmonary chondroid hamartoma patient undergoing surgical resections. This raw dataset contained 9,001 cells and 24,873 genes. A narrative, literature-supported exploratory study was conducted in which genes expressed in fewer than 3 cells and cells with fewer than 100 genes were filtered out, resulting in 8,979 cells and 22,596 genes (Supplementary Figure S1). This allowed the researcher to determine the relative proportions of immune and cancerous cells among the 8,979 cells in total (Supplementary Figure S1). It enabled the researcher to assess the overall immune response in patients diagnosed with NSCLC. Next, immune cells were filtered out to focus on cancerous epithelial cells, resulting in a total of 3,324 cells. From these epithelial cells, known expression markers associated with NSCLC subtyping, multidrug resistance, apoptosis resistance, and altered cancer metabolism (GSE119911) were identified. In this analysis, the researcher demonstrated that the samples encompass diverse immune cell populations that play a crucial role in shaping cancer heterogeneity. Concepts of anti-apoptosis, multi-drug resistance, and metastatic potential are also explored using marker genes. Cellular subtypes and clusters defined by the selected markers, which may otherwise be missed, were characterized by the researcher using single-cell RNA sequencing. The observed results demonstrated the utility of this method for uncovering cellular diversity, which could be used for cancer outcome prediction and treatment decision-making.