Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

Expression of NMU, PPBP and GNG4 in colon cancer and their influences on prognosis

BACKGROUND

This study aims to identify the core genes that influence the prognosis of colon cancer (CC) and analyze their relationships with clinical characteristics.

METHODS

The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified. The top ten core genes were selected by bioinformatics tools and screened through the Oncomine database. The expression of core genes in CC tissues and cells was validated by immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. Spearman correlation was used to analyze the relationship between different parameters. Overall survival was assessed by the Kaplan-Meier method. The area under the curve (AUC) and the receiver operating curve (ROC) were applied to assess the accuracy of genes for predicting prognosis.

RESULTS

There were 1,665 DEGs that were identified from TCGA database. Bioinformatics analysis found that GNGT1, NMU, PPBP, AGT, and GNG4 were differentially expressed in CC tissue. Overexpression of NMU, PPBP, AGT, and GNG4 in CC was associated with shortened survival time (P<0.05). In the validation studies, the high expression levels of NMU, PPBP and GNG4 in CC cells and tissues were confirmed compared to the control groups (P<0.05) and were adverse prognostic biomarkers (P<0.01). The combination prognostic model of the three core genes predicted the 1-, 3-, and 5-year survival of CC with AUCs of 0.868, 0.635 and 0.770, respectively.

CONCLUSIONS

High levels of NMU, PPBP, and GNG4 were associated with poor prognosis in CC. The combination prognostic model of these three genes could be a new option.

Bioinformatics and System Biology Approach to Reveal the Interaction Network and the Therapeutic Implications for Non-Small Cell Lung Cancer Patients With COVID-19

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the leading cause of coronavirus disease-2019 (COVID-19), is an emerging global health crisis. Lung cancer patients are at a higher risk of COVID-19 infection. With the increasing number of non-small-cell lung cancer (NSCLC) patients with COVID-19, there is an urgent need of efficacious drugs for the treatment of COVID-19/NSCLC.

Methods: Based on a comprehensive bioinformatic and systemic biological analysis, this study investigated COVID-19/NSCLC interactional hub genes, detected common pathways and molecular biomarkers, and predicted potential agents for COVID-19 and NSCLC.

Results: A total of 122 COVID-19/NSCLC interactional genes and 21 interactional hub genes were identified. The enrichment analysis indicated that COVID-19 and NSCLC shared common signaling pathways, including cell cycle, viral carcinogenesis, and p53 signaling pathway. In total, 10 important transcription factors (TFs) and 44 microRNAs (miRNAs) participated in regulations of 21 interactional hub genes. In addition, 23 potential candidates were predicted for the treatment of COVID-19 and NSCLC.

Conclusion: This study increased our understanding of pathophysiology and screened potential drugs for COVID-19 and NSCLC.

Identification of Cancer Hub Gene Signatures Associated with Immune-Suppressive Tumor Microenvironment and Ovatodiolide as a Potential Cancer Immunotherapeutic Agent

Despite the significant advancement in therapeutic strategies, breast, colorectal, gastric, lung, liver, and prostate cancers remain the most prevalent cancers in terms of incidence and mortality worldwide. The major causes ascribed to these burdens are lack of early diagnosis, high metastatic tendency, and drug resistance. Therefore, exploring reliable early diagnostic and prognostic biomarkers universal to most cancer types is a clinical emergency. Consequently, in the present study, the differentially expressed genes (DEGs) from the publicly available microarray datasets of six cancer types (liver, lung colorectal, gastric, prostate, and breast cancers), termed hub cancers, were analyzed to identify the universal DEGs, termed hub genes. Gene set enrichment analysis (GSEA) and KEGG mapping of the hub genes suggested their crucial involvement in the tumorigenic properties, including distant metastases, treatment failure, and survival prognosis. Notably, our results suggested high frequencies of genetic and epigenetic alterations of the DEGs in association with tumor staging, immune evasion, poor prognosis, and therapy resistance. Translationally, we intended to identify a drug candidate with the potential for targeting the hub genes. Using a molecular docking platform, we estimated that ovatodiolide, a bioactive anti-cancer phytochemical, has high binding affinities to the binding pockets of the hub genes. Collectively, our results suggested that the hub genes were associated with establishing an immune-suppressive tumor microenvironment favorable for disease progression and promising biomarkers for the early diagnosis and prognosis in multiple cancer types and could serve as potential druggable targets for ovatodiolide.

A miR-212-3p/SLC6A1 Regulatory Sub-Network for the Prognosis of Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) is a liver cancer with a poor prognosis. Owing to the complexity and limited pathogenic mechanism research on HCC, the molecular targeted therapy has been hindered.

Methods

In this study, we categorized transcriptome data into low-Myc and high-Myc expression groups in 365 HCC samples, screened the differentially expressed RNAs, including 441 DE-lncRNAs, 99 DE-miRNAs and 612 DE-mRNAs, constructed a lncRNA-miRNA-mRNA regulatory network, and selected a hub triple regulatory network through cytoHubba analysis. Through Gene ontology and KEGG pathway, a hub regulatory network was particularly enriched in the “Wnt signaling pathway” and “Cytochrome P450-arranged by substrate type” by Metascape. The prognostic genes in the hub regulatory network were evaluated by the RNA expression analysis, Kaplan–Meier (KM) survival analysis, and correlation analysis.

Results

The results showed that miR-212-3p/SLC6A1 axis was a potential prognostic model for HCC. Furthermore, IHC analysis showed down-regulated expression of SLC6A1 in HCC tissues and Alb-Cre;Myc mouse liver cancer tissues. The genetics and epigenetic analysis indicated that SLC6A1 expression was negatively correlated with DNA methylation. Immune infiltration analysis showed a negative relation between SLC6A1 and T cell exhaustion/monocyte in liver cancer tissues.

Conclusion

In summary, the study revealed that miR-212-3p/SLC6A1 axis could serve as a crucial therapeutic target for HCC.

Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The present study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan–Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM–receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression levels of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression levels of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD and may be potential therapeutic targets for LUAD.