BackgroundExtensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.MethodsWe obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.ResultsWe identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.ConclusionsThe risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

BackgroundIt has been suggested that lactate metabolism (LM) is crucial for the development of cancer. Using integrated single-cell RNA sequencing (scRNA-seq) analysis, we built predictive models based on LM-related genes (LMRGs) to propose novel targets for the treatment of LUAD patients. MethodsThe most significant genes for LM were identified through the use of the AUCell algorithm and correlation analysis in conjunction with scRNA-seq analysis. To build risk models with superior predictive performance, cox- and lasso-regression were utilized, and these models were validated on multiple external independent datasets. We then explored the differences in the tumor microenvironment (TME), immunotherapy, mutation landscape, and enriched pathways between different risk groups. Finally, cell experiments were conducted to verify the impact of AHSA1 in LUAD.ResultsA total of 590 genes that regulate LM were identified for subsequent analysis. Using cox- and lasso-regression, we constructed a 5-gene signature that can predict the prognosis of patients with LUAD. Notably, we observed differences in TME, immune cell infiltration levels, immune checkpoint levels, and mutation landscapes between different risk groups, which could have important implications for the clinical treatment of LUAD patients.ConclusionBased on LMRGs, we constructed a prognostic model that can predict the efficacy of immunotherapy and provide a new direction for treating LUAD.

Background: Pyroptosis is a form of programmed cell death triggered by the rupture of cell membranes and the release of inflammatory substances; it is essential in the occurrence and development of cancer. A considerable number of studies have revealed that pyroptosis is closely associated to the biological process of several cancers. However, the role of pyroptosis in lung adenocarcinoma (LUAD) remains elusive. The purpose of this study was to explore the prognostic role of pyroptosis-related genes (PRGs) and their relationship with the tumor immune microenvironment (TIME) in LUAD.Methods: Gene expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prognostic PRG signature was established in the training set and verified in the validation sets. Functional enrichment and immune microenvironment analyses related to PRGs were performed and a nomogram based on the risk score and clinical characteristics was established. What is more, quantitative real-time PCR (qRT-PCR) analysis was applied in order to verify the potential biomarkers for LUAD.Results: A prognostic signature based on five PRGs was constructed to separate LUAD patients into two risk groups. Patients in the high-risk group had worse prognoses than those in the low-risk group. The signature was identified as independent via Cox regression analyses and obtained the largest area under the curve (AUC = 0.677) in the receiver operating characteristic (ROC). Functional enrichment and immune microenvironment analyses demonstrated that the immune status was significantly different in the two subgroups and that immunotherapy may be effective for the high-risk group. Furthermore, qRT-PCR analysis verified that serum PRKACA and GPX4 could serve as diagnostic biomarkers for LUAD.Conclusion: Overall, a risk signature based on five PRGs was generated, providing a novel perspective on the determinants of prognosis and survival in LUAD, as well as a basis for the development of individualized regimes.