Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

Investigation of Diagnostic and Prognostic Value of CLEC4M of Non-Small Cell Lung Carcinoma Associated with Immune Microenvironment

Purpose

C-type lectin domain family 4 member M (CLEC4M) has been found to be involved in the occurrence and development of cancer, but its role in NSCLC remains to be fully explored. Our work aims to evaluate the diagnostic and prognostic value of CLEC4M in NSCLC and to investigate the underlying mechanisms of CLEC4M in the immune microenvironment of NSCLC.

Methods

Integrating publicly accessible data and clinical tissue samples to verify the expression of CLEC4M in NSCLC. The diagnostic value of CLEC4M was determined by receiver operating characteristic (ROC) curve. Kaplan-Meier survival analysis, nomogram plot, univariate and multivariate Cox regression models were performed to evaluate the prognostic impact of CLEC4M on NSCLC patients. The correlation between CLEC4M and tumor immune infiltration was estimated using TIMER and UALCAN databases. Functional assessments including GO, KEGG pathway and GSEA analyses were implemented to illustrate the potential mechanisms of CLEC4M in NSCLC.

Results

CLEC4M was significantly downregulated in NSCLC tissue, as confirmed by immunohistochemistry of clinical tissues. The high AUC value of ROC curves demonstrated the diagnostic accuracy of CLEC4M in NSCLC. Additionally, low CLEC4M expression was associated with poor survival in NSCLC patients. Furthermore, CLEC4M was found to be significantly associated with tumor immune infiltration, and CLEC4M may be involved in immune activation and proliferation inhibition through the functional assessment, suggesting that CLEC4M may be a therapeutic target for NSCLC patients.

Conclusion

Our findings reveal CLEC4M is significantly downregulated in NSCLC tissues, and illustrate the diagnostic and prognostic value of CLEC4M in NSCLC, as well as its potential serve as an immune-related therapeutic target.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.