RESUMO
Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)-LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity-related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high- and low-risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low-risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high-risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high-risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low-risk group. XAV-939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.
RESUMO
Background: Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes. Methods: We used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR. Results: In this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue. Conclusions: In summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.
