Comparative analyses of salivary exosomal miRNAs for patients with or without lung cancer.

Introduction: Lung cancer is the most frequent cause of cancer-related deaths worldwide. Exosomes are involved in different types of cancer, including lung cancer. Methods: We collected saliva from patients with (LC) or without (NC) lung cancer and successfully isolated salivary exosomes by ultracentrifugation. MiRNA sequencing was implemented for the exosome samples from NC and LC groups, dgeR was used to determine differentially expressed miRNAs (DE miRNAs), and quantitative real-time polymerase chain reaction (qPCR) was used to verify three differentially expressed microRNAs (miRNAs). Results: A total of 372 miRNAs were identified based on the sequencing results. Subsequently, 15 DE miRNAs were identified in LC vs. NC, including eight upregulated miRNAs and seven downregulated miRNAs. Some DE miRNAs were validated via qPCR. A total of 488 putative target genes of the upregulated DE miRNAs were found, and the functional analyses indicated that numerous target genes were enriched in the pathways associated with cancer. Discussion: This suggests that miRNAs of salivary exosomes might have the potential to be used as biomarkers for prediction and diagnosis of lung cancer.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer.

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein-protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein-protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients' prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

Establishing a Novel Gene Signature Related to Histone Modifications for Predicting Prognosis in Lung Adenocarcinoma.

Background: Epigenetic modifications have been revealed to play an important role in tumorigenesis and tumor development. This study aims to analyze the role of histone modifications and the prognostic values of histone modifications in lung adenocarcinoma (LUAD). The promoters and enhancers of protein encoding genes (PCGs) were the regions of enriched histone modifications. Methods: Expression profiles and clinical information of LUAD samples were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Histone modification data of LUAD cell lines were downloaded from Encyclopedia of DNA Elements (ENCODE) database. Limma R package was used to identify differentially expressed PCGs. To identify molecular subtypes, consensus clustering was conducted based on the expression of dysregulated PCGs with abnormal histone modifications. Univariate Cox regression analysis and stepwise Akaike information criterion (stepAIC) were utilized to establish a prognostic model. Results: We identified a total of 699 epigenetic dysregulated genes with 122 of them significantly correlating with LUAD prognosis. We constructed three molecular subtypes (C1, C2, and C3) based on the 122 prognostic genes. C2 had the longest overall survival while C1 had the worst prognosis. In addition, three subtypes had differential immune infiltration and the response to immune checkpoint inhibitors. Moreover, we identified a risk model containing 5 epi-PCGs that had favorable performance to predict prognosis in different datasets. Conclusions: This study further supported the critical histone modifications in LUAD development. Three subtypes may provide guidance for the immunotherapy of LUAD patients. Importantly, the prognostic model had great potential to predict LUAD prognosis.

Characterization of lung adenocarcinoma based on immunophenotyping and constructing an immune scoring model to predict prognosis.

Background: Lung cancer poses great threat to human health, and lung adenocarcinoma (LUAD) is the main subtype. Immunotherapy has become first line therapy for LUAD. However, the pathogenic mechanism of LUAD is still unclear. Methods: We scored immune-related pathways in LUAD patients using single sample gene set enrichment analysis (ssGSEA) algorithm, and further identified distinct immune-related subtypes through consistent clustering analysis. Next, immune signatures, Kaplan-Meier survival analysis, copy number variation (CNV) analysis, gene methylation analysis, mutational analysis were used to reveal differences between subtypes. pRRophetic method was used to predict the response to chemotherapeutic drugs (half maximal inhibitory concentration). Then, weighted gene co-expression network analysis (WGCNA) was performed to screen hub genes. Significantly, we built an immune score (IMscore) model to predict prognosis of LUAD. Results: Consensus clustering analysis identified three LUAD subtypes, namely immune-Enrich subtype (Immune-E), stromal-Enrich subtype (Stromal-E) and immune-Deprived subtype (Immune-D). Stromal-E subtype had a better prognosis, as shown by Kaplan-Meier survival analysis. Higher tumor purity and lower immune cell scores were found in the Immune-D subtype. CNV analysis showed that homologous recombination deficiency was lower in Stromal-E and higher in Immune-D. Likewise, mutational analysis found that the Stromal-E subtype had a lower mutation frequency in TP53 mutations. Difference in gene methylation (ZEB2, TWIST1, CDH2, CDH1 and CLDN1) among three subtypes was also observed. Moreover, Immune-E was more sensitive to traditional chemotherapy drugs Cisplatin, Sunitinib, Crizotinib, Dasatinib, Bortezomib, and Midostaurin in both the TCGA and GSE cohorts. Furthermore, a 6-gene signature was constructed to predicting prognosis, which performed better than TIDE score. The performance of IMscore model was successfully validated in three independent datasets and pan-cancer.

Surgical Trauma-induced CCL2 Upregulation Mediates Lung Cancer Progression by Promoting Treg Recruitment in Mice and Patients.

Surgical removal of the tumor is currently the first-line treatment for lung cancer, but the procedure may accelerate cancer progression through immunosuppression. However, whether CCL2 (C-C motif chemokine ligand 2) enhances cancer progression by affecting regulatory T cells (Tregs) remains unknown. We found that the volume and weight of tumors were larger in the surgical trauma group than in the control group. CCL2 expression and Treg abundance were increased in tumor tissues after surgical trauma, and CCL2 expression was positively associated with Treg abundance. These results demonstrated that surgical trauma contributes to lung cancer progression by increasing CCL2 expression, thus promoting Treg recruitment.

circRNA-002178 act as a ceRNA to promote PDL1/PD1 expression in lung adenocarcinoma.

Circular RNAs (circRNAs) have been identified play a vital role in various different types of cancer via sponging miRNAs (microRNAs). However, their role in lung adenocarcinoma (LUAD) remains largely unclear. In this study, we systematically characterized the circRNA expression profiles in the LUAD cancer tissues and paired adjacent non-cancerous tissues. Three circRNAs were found to be significantly upregulated. Among them, has-circRNA-002178 was further confirmed to be upregulated in the LUAD tissues, and LUAD cancer cells. Subsequently, we also found has-circRNA-002178 could enhance PDL1 expression via sponging miR-34 in cancer cells to induce T-cell exhaustion. More importantly, circRNA-002178 could be detected in exosomes of plasma from LUAD patients and could serve as biomarkers for LUAD early diagnosis. Finally, we found circRNA-002178 could be delivered into CD8+ T cells to induce PD1 expression via exosomes. Taken together, our study revealed that circRNA-002178 could act as a ceRNA to promote PDL1/PD1 expression in lung adenocarcinoma.