Systemic analysis of the DNA replication regulator origin recognition complex in lung adenocarcinomas identifies prognostic and expression significance

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

MATERIALS AND METHODS

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

RESULTS

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

CONCLUSION

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Revealing a Novel Methylated Integrin Alpha-8 Related to Extracellular Matrix and Anoikis Resistance Using Proteomic Analysis in the Immune Microenvironment of Lung Adenocarcinoma

Genomic epigenetics of extracellular matrix (ECM) play an important role in lung adenocarcinoma (LUAD). Our study identified a signature of potential prognostic genes associated with ECM and constructed immune risk-related prognosis model in LUAD. We downloaded mRNAs transcriptome data, miRNAs expression data, and clinical patient information for LUAD based on The Cancer Genome Atlas. "Limma, clusterProfiler, ggplot2" R packages and GSEA were used to analyze meaningful genes and explore potential biological function. A competing endogenous RNA network was constructed to reveal the mechanism of ECM-related genes. Combined with clinical LUAD patients' characteristics, univariate and multivariate Cox regression analyses were used to build prognostic immune risk model. Next, we calculated AUC value of ROC curve, and explored survival probability of different risk groups. A total of 2966 mRNAs were differently expressed in LUAD samples and normal samples. Function enrichment analyses proved mRNAs were associated with many tumor pathways, such as cell adhesion, vascular smooth muscle contraction, and cell cycle. There were 18 mRNAs related to ECM receptor signaling pathway, and 7 mRNAs expressions were correlated with EGFR expression, but only 5mRNAs were associated with the long-term prognosis. Based on Integrin alpha-8 (ITGA8) molecule, we identified potential 3 miRNAs from several databases. The promoter of ITGA8 was higher-methylated and lower-expressed in LUAD. And lower-expressed group has poor prognosis for patients. 66 immunomodulators related to ITGA8 were performed to construct immune correlation prediction model (p < 0.05). Comprehensive analyses of ITGA8 revealed it combined focal adhesion kinase to activate PI3K/AKT signaling pathway to influence the occurrence and development of LUAD. A novel immune prognostic model about ITGA8 was constructed and verified in LUAD patients. Combined with non-coding genes and genomic epigenetics, identification of potential biomarkers provided new light on therapeutic strategy for clinical patients.

ORC6 acts as an effective prognostic predictor for non‑small cell lung cancer and is closely associated with tumor progression

Origin recognition complexes (ORCs) are vital in the control of DNA replication and the progression of the cell cycle, however the precise function and mechanism of ORC6 in non-small cell lung cancer (NSCLC) is still not well understood. The present study used bioinformatics methods to assess the predictive significance of ORC6 expression in NSCLC. Moreover, the expression of ORC6 was further evaluated using reverse transcription-quantitative PCR and western blotting, and its functional significance in lung cancer was assessed via knockdown experiments using small interfering RNA. A significant association was demonstrated between the expression of ORC6 and the clinical features of NSCLC. In particular, elevated levels of ORC6 were significantly strongly correlated with an unfavorable prognosis. Multivariate analysis demonstrated that increased ORC6 expression independently contributed to the risk of overall survival (HR 1.304; P=0.015) in individuals diagnosed with NSCLC. Analysis of Kaplan-Meier plots demonstrated that ORC6 expression served as a valuable indicator for diagnosing and predicting the prognosis of NSCLC. Moreover, in vitro studies demonstrated that modified ORC6 expression had a significant impact on the proliferation, migration and metastasis of NSCLC cells. NSCLC cell lines (H1299 and mH1650) exhibited markedly higher ORC6 expression than normal lung cell lines. The results of the present study indicated a strong association between the expression of ORC6 and the clinicopathological characteristics of NSCLC, which suggested its potential as a reliable biomarker for predicting NSCLC. Furthermore, ORC6 may have important therapeutic implications in the management of NSCLC.

The mitotic cell cycle-associated nomogram predicts overall survival in lung adenocarcinoma

BACKGROUND

This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) associated with mitotic cell cycle. The model will predict the probability of survival at different time points and serve as a reference tool to evaluate the effectiveness of LUAD treatment.

METHODS

A cohort of 442 patients with LUAD from the gene expression omnibus (GEO) database was randomly divided into a training group (n = 299) and a validation group (n = 99). The least absolute shrinkage and selection operator (LASSO)-COX algorithm was used to reduce the number of predictors based on the clinicopathological and RNA sequencing data to establish mutant characteristics that could predict patient survival. Additionally, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) analyses were conducted on the mutant characteristics. The performance of the developed nomogram was evaluated using calibration curves and the C-index.

RESULTS

The mutant characteristics had prognostic value for LUAD and acted as an independent prognostic factor. The mutant characteristics profile derived from the LASSO-COX algorithm demonstrated a significant association with overall survival in patients with LUAD. Functional annotation based on the mutant score, its involvement in the phase transition of the mitotic cell cycle, and its regulatory processes. The nomogram, which combined the mutant score with clinical factors associated with prognosis, showed robust accuracy in both the training and validation groups.

CONCLUSION

This study presents the first individualized model that establishes a mutant score for predicting survival in LUAD. This model can be used as a predictive tool for determining 1-, 2-, 3-, and 5-year survival probabilities in patients with LUAD.

Construction and Evaluation of an M2 Macrophage-Related Prognostic Model for Colon Cancer

Colon cancer (CC) is a primary human malignancy. Recently, the mechanism of the tumor microenvironment (TME) in CC has been a hot topic of research. However, there is uncertainty regarding the contribution of M2 macrophages and related genes to the prognosis for CC. M2 macrophage-related genes (M2RGs) were obtained from The Cancer Genome Atlas (TCGA) database. Immune cell infiltration in CC tissue was assessed by Cibersort. Based on the TCGA-COAD training set, a Least Absolute Shrinkage and Selection Operator (LASSO) Cox risk model was constructed and its efficiency was evaluated by analyzing risk profiles and survival profiles. Using gene set enrichment analysis (GSEA), the functional distinctions between high-risk and low-risk categories were further investigated. Finally, potential immune checkpoints, immunotherapy efficiency, and clinical treatment of high-risk patients were evaluated. A total of 1063 M2RGs were identified in TCGA-COAD, 32 of these were confirmed to be strongly related to overall survival (OS), and 14 of these were picked to construct an OS-oriented prognostic model in CC patients. The M2RG signature had a positive correlation with unfavorable prognosis according to the survival analysis. Correlation analysis revealed that the risk model was positively associated with clinicopathological characteristics, immune cell infiltration, immune checkpoint inhibitor targets, the risk of immune escape, and the efficiency of anti-cancer medications. The risk model created using M2RGs may be useful in predicting the prognosis of CC.

LncRNA RP11-58O9.2 predicts poor prognosis and promotes progression of non-small cell lung cancer

OBJECTIVE

Long non-coding RNAs (lncRNAs) play a crucial role in non-small cell lung cancer (NSCLC). This study aimed to investigate the novel biomarker, lncRNA RP11-58O9.2, in patients with NSCLC.

METHODS

RP11-58O9.2 expression in NSCLC cells and tissues was detected by reverse transcription-quantitative polymerase chain reaction. Patient survival was analyzed in relation to RP11-58O9.2 expression levels. RP11-58O9.2 expression was knocked down and endogenous expression was verified in two NSCLC cell lines. Cell proliferation was then assessed by Cell Counting Kit-8 and colony-formation assays, and cell invasion and migration were assessed by Transwell and wound-healing assays, respectively. In vivo experiments were performed in mice, and the combination of RP11-58O9.2 and miR-6749-3p was predicted by miRanda.

RESULTS

RP11-58O9.2 was highly expressed in NSCLC cell lines and tissues, and was associated with advanced stage, lymphatic metastasis, and differentiation group. High RP11-58O9.2 levels were also associated with shorter survival. RP11-58O9.2 knockdown inhibited the proliferation, invasion, and migration of lung cancer cells, and tumor growth in mouse xenografts in vivo. RP11-58O9.2 may target and regulate miR-6749-3p.

CONCLUSIONS

LncRNA RP11-58O9.2 is associated with NSCLC prognosis and promotes lung cancer progression. Further studies are needed to investigate the mechanisms and the regulatory association between RP11-58O9.2 and miR-6749-3p.