Gene-to-gene interactions and the association of TP53, XRCC1, TNFα, HMMR, MDM2 and PALB2 with breast cancer in Kyrgyz females

HMMR potential as a diagnostic and prognostic biomarker of cancer-speculation based on a pan-cancer analysis

Background

Although the status of universal upregulation for the Hyaluronan-Mediated Motility Receptor (HMMR) in pan-cancer is still unknown, HMMR is upregulated and associated with poor prognosis for some tumors.

Methods

Exploring HMMR expression in different tumor types using The Cancer Genome Atlas (TCGA) or other public databases for a pan-cancer analysis, exploring the relationship between HMMR and tumor prognosis, and exploring the role of HMMR in tumor immunity.

Results

No matter the pairing or unpairing of data, HMMR expression generally increased compared to corresponding normal tissue. Based on a CCLE study, our results indicated that HMMR is widely expressed in various tumor cells. For most tumor types, high HMMR expression was associated with reduced Overall Survival (OS), Return to Functional Status (RFS), and Platinum Free Interval (PFI). ROC curves indicated that HMMR displays high prediction potential for most tumor types. In pan-cancer, HMMR is correlated with some clinical staging, immune cells, and immune checkpoints for some tumors. The GO/KEGG enrichment analysis results for proteins most closely related to HMMR indicate that the most highly enriched pathways are all related to tumor development.

Conclusions

Our pan-cancer analysis of HMMR suggests that HMMR can be used as a potential diagnostic and prognostic indicator of pan-cancer and that HMMR may be involved in tumor development.

HMMR associates with immune infiltrates and acts as a prognostic biomaker in lung adenocarcinoma

BACKGROUND

To explore the expression of hyaluronan mediated motility receptor (HMMR) in lung adenocarcinoma (LUAD) and its relationship with clinicopathological features and tumor-infiltrating is not clear.

METHODS

The expression of HMMR in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA)-LUAD. TCGA was employed to examine the relationship between the clinicopathological characteristics and HMMR expression and the LUAD patients' prognosis. Tumor Immune Estimation Resource (TIMER)database was employed to analyze the relationship between immune infiltration and HMMR. Gene Set Enrichment Analysis was explored through gene enrichment. Gene Expression Omnibus (GEO) data and our hospital data were utilized to confirm the findings.

RESULTS

The expression level of HMMR in lung adenocarcinoma tissue and cells was greater than that in the normal group, which was linked to clinical stage, smoking history, and recurrence, and could increase the progression or recurrence of LUAD. Patients in the pathological grade had a significant expression of HMMR in moderately differentiated LUAD tissues. In addition, HMMR has an impact on LUAD patients' overall survival rate [P = 9.5e-06; hazard ratio (HR) = 2]. The level of HMMR expression in LUAD was significantly linked to neutrophils, CD8+T, and CD4+T cells. TMB analysis showed that HMMR could also affect the tumor microenvironment in LUAD. HMMR might be employed as an independent predictive biomarker of LUAD, according to a multivariate COX regression analysis. The findings of GSEA analysis showed that the samples with high HMMR expression levels were rich in cell cycle, cell metabolism, and DNA replication. The analysis results of GSE31210 data are basically consistent with those of TCGA-LUAD.

CONCLUSIONS

It is suggested that HMMR has an effect on the occurrence and development of lung adenocarcinoma. Besides, HMMR is also linked to the level of immune infiltration of neutrophils, CD8+T cells, and CD4+T cells and LUAD patients' prognosis. HMMR was suggested to be utilized as a biomarker or therapeutic target to judge the prognosis and immune infiltration of LUAD.

DNA repair in lung cancer: a large-scale quantitative analysis for polymorphisms in DNA repairing pathway genes and lung cancer susceptibility

BACKGROUND

The results of associations between single nucleotide polymorphisms (SNPs) of genes in DNA repairing pathway and lung cancer (LC) risk are inconsistent.

METHODS

We applied allele, dominant and recessive models to explore the risk of researched variants to LC in total LC and subgroups by ethnicity or LC subtypes with a cutoff point of p < 0.05.

RESULTS

A total of 76,935 cases and 88,649 controls from 192 articles were included. Among the analyzed 40 variants from 20 genes, we found 9 statistically significant variants in overall populations by allele model, including five SNPs (rs1760944, rs9344, rs13181, rs1001581, and rs915927) increasing LC risk (odd ratios [ORs] = 1.10-1.71) and four SNPs (rs1042522, rs3213245, rs11615, and rs238406) decreasing the risk (ORs = 0.75-0.94). We identified rs1042522 and rs13181 as significant variants for LC in three models. Additionally, we identified differential significant SNPs in ethnic and subtype's analysis with comparison to total population.

CONCLUSIONS

There are five SNPs in DNA repairing pathway associated with increased LC risk and four others decreased LC risk. Besides, the risky SNPs in different ethnicities and various LC subtypes were partly different, and the contribution of different genotypes to risk alleles were various as well.

Bioinformatics analysis to determine the prognostic value and prospective pathway signaling of miR-126 in non-small cell lung cancer

Background

MicroRNAs (miRNAs) have been demonstrated to play crucial roles in the initiation and development of non-small cell lung cancer (NSCLC). However, further investigation of the specific role of miR-126 in NSCLC is still required.

Methods

An analysis of miR-126 expression in NSCLC was carried out using the Gene Expression Omnibus (GEO) database, and a literature review was also performed. The differentially expressed genes (DEGs) in three mRNA datasets, GSE18842, GSE19804, and GSE101929, from GEO were identified. Following the prediction of hsa-miR-126-5p target genes by TargetScan, the overlap of miR-126 target genes with DEGs in NSCLC was examined. After that, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed. Finally, an analysis to identify the impact of hub genes on the prognosis of NSCLC was carried out on the basis of a protein-protein interaction (PPI) network constructed using STRING and Cytoscape.

Results

The data in the literature review revealed a trend that miR126 was downregulated in NSCLC. The number of both NSCLC-related and miR-126-related DEGs was 187. Dozens of DEGs were significantly enriched in biological regulation, cell membrane binding, and signal receptor binding. In the PPI network analysis, 3 of 10 identified hub genes, namely NCAPG, MELK, and KIAA0101, were obviously related to poor prognosis in NSCLC; the survival rate was low among patients with high expression levels of these genes. Furthermore, through network analysis, TPX2, HMMR, and ANLN were identified as recessive miR-126-related genes that may be involved in NSCLC.

Conclusions

MiR-126 plays an essential role in the biological processes of NSCLC through binding to target genes and influences the prognosis of patients with the disease.