The susceptibility CDKN2 locus may have a role on prognosis of melanoma patients

Constructing a prognostic model for head and neck squamous cell carcinoma based on glucose metabolism related genes

Background

Glucose metabolism (GM) plays a crucial role in cancer cell proliferation, tumor growth, and survival. However, the identification of glucose metabolism-related genes (GMRGs) for effective prediction of prognosis in head and neck squamous cell carcinoma (HNSC) is still lacking.

Methods

We conducted differential analysis between HNSC and Normal groups to identify differentially expressed genes (DEGs). Key module genes were obtained using weighted gene co-expression network analysis (WGCNA). Intersection analysis of DEGs, GMRGs, and key module genes identified GMRG-DEGs. Univariate and multivariate Cox regression analyses were performed to screen prognostic-associated genes. Independent prognostic analysis of clinical traits and risk scores was implemented using Cox regression. Gene set enrichment analysis (GSEA) was used to explore functional pathways and genes between high- and low-risk groups. Immune infiltration analysis compared immune cells between the two groups in HNSC samples. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Quantitative real-time fluorescence PCR (qRT-PCR) validated the expression levels of prognosis-related genes in HNSC patients.

Results

We identified 4973 DEGs between HNSC and Normal samples. Key gene modules, represented by black and brown module genes, were identified. Intersection analysis revealed 76 GMRG-DEGs. Five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) were identified. A nomogram incorporating age, lymph node status (N), and risk score was constructed for survival prediction in HNSC patients. Immune infiltration analysis showed significant differences in five immune cell types (Macrophages M0, memory B cells, Monocytes, Macrophages M2, and Dendritic resting cells) between the high- and low-risk groups. GDSC database analysis identified 53 drugs with remarkable differences between the groups, including A.443654 and AG.014699. DNMT1 and MTHFD2 were up-regulated, while MPZ was down-regulated in HNSC.

Conclusion

Our study highlights the significant association of five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) with HNSC. These findings provide further evidence of the crucial role of GMRGs in HNSC.

A novel EZH2/NXPH4/CDKN2A axis is involved in regulating the proliferation and migration of non-small cell lung cancer cells

NXPH4 is discovered to be a neuropeptide-like glycoprotein, belonging to the Neurexophilins (Nxphs) family. NXPH4 shares a similar domain structure with NXPH1, which, however, is poorly understood in terms of its function. Bioinformatics analysis and experimental verification in this study confirmed the abnormal high expression of NXPH4 in non-small cell lung cancer (NSCLC) tissues and cells. Knockdown of NXPH4 by siRNA can inhibit the proliferation and migration of cells, resulting in significant cell cycle arrest in S1 phase. Furthermore, in NSCLC cells, NXPH4 was regulated by transcriptional activation of Enhancer of zeste homolog 2 (EZH2) in its upstream. While downstream, NXPH4 could interact with CDKN2A and downregulate its protein stability, thus participating in the cell cycle regulation through interacting with cyclinD-CDK4/6-pRB-E2F signaling pathway. To sum up, the present study reveals a regulatory pathway of EZH2/NXPH4/CDKN2A in NSCLC, providing possible reference for understanding the function of NXPH4 in tumors.

Screening of Hub Gene Targets for Lung Cancer via Microarray Data

BACKGROUND

Lung cancer is one of the malignancies exhibiting the fastest increase in morbidity and mortality, but the cause is not clearly understood. The goal of this investigation was to screen and identify relevant biomarkers of lung cancer.

METHODS

Publicly available lung cancer data sets, including GSE40275 and GSE134381, were obtained from the GEO database. The repeatability test for data was done by principal component analysis (PCA), and a GEO2R was performed to screen differentially expressed genes (DEGs), which were all subjected to enrichment analysis. Protein-protein interactions (PPIs), and the significant module and hub genes were identified via Cytoscape. Expression and correlation analysis of hub genes was done, and an overall survival analysis of lung cancer was performed. A receiver operating characteristic (ROC) curve analysis was performed to test the sensitivity and specificity of the identified hub genes for diagnosing lung cancer.

RESULTS

The repeatability of the two datasets was good and 115 DEGs and 10 hub genes were identified. Functional analysis revealed that these DEGs were associated with cell adhesion, the extracellular matrix, and calcium ion binding. The DEGs were mainly involved with ECM-receptor interaction, ABC transporters, cell-adhesion molecules, and the p53 signaling pathway. Ten genes including COL1A2, POSTN, DSG2, CDKN2A, COL1A1, KRT19, SLC2A1, SERPINB5, DSC3, and SPP1 were identified as hub genes through module analysis in the PPI network. Lung cancer patients with high expression of COL1A2, POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 had poorer overall survival times than those with low expression (p <0.05). The CTD database showed that 10 hub genes were closely related to lung cancer. Expression of POSTN, DSG2, CDKN2A, COL1A1, SLC2A1, SERPINB5, and SPP1 was also associated with a diagnosis of lung cancer (p<0.05). ROC analysis showed that SPP1 (AUC = 0.940, p = 0.000*, 95%CI = 0.930-0.973, ODT = 7.004), SLC2A1 (AUC = 0.889, p = 0.000*, 95%CI = 0.791-0.865, ODT = 7.123), CDKN2A (AUC = 0.730, p = 0.000*, 95%CI = 0.465-1.000, ODT = 6.071) were suitable biomarkers.

CONCLUSION

Microarray technology represents an effective method for exploring genetic targets and molecular mechanisms of lung cancer. In addition, the identification of hub genes of lung cancer provides novel research insights for the diagnosis and treatment of lung cancer.

A multi-factorial genetic model for prognostic assessment of high risk melanoma patients receiving adjuvant interferon

Purpose

IFNa was the first cytokine to demonstrate anti-tumor activity in advanced melanoma. Despite the ability of high-dose IFNa reducing relapse and mortality by up to 33%, large majority of patients experience side effects and toxicity which outweigh the benefits. The current study attempts to identify genetic markers likely to be associated with benefit from IFN-a2b treatment and predictive for survival.

Experimental design

We tested the association of variants in FOXP3 microsatellites, CTLA4 SNPs and HLA genotype in 284 melanoma patients and their association with prognosis and survival of melanoma patients who received IFNa adjuvant therapy.

Results

Univariate survival analysis suggested that patients bearing either the DRB1*15 or HLA-Cw7 allele suffered worse OS while patients bearing either HLA-Cw6 or HLA-B44 enjoyed better OS. DRB1*15 positive patients suffered also worse RFS and conversely HLA-Cw6 positive patients had better RFS. Multivariate analysis revealed that a five-marker genotyping signature was prognostic of OS independent of disease stage. In the multivariate Cox regression model, HLA-B38 (p = 0.021), HLA-C15 (p = 0.025), HLA-C3 (p = 0.014), DRB1*15 (p = 0.005) and CT60*G/G (0.081) were significantly associated with OS with risk ratio of 0.097 (95% CI, 0.013–0.709), 0.387 (95% CI, 0.169–0.889), 0.449 (95% CI, 0.237–0.851), 1.948 (95% CI, 1.221–3.109) and 1.484 (95% IC, 0.953–2.312) respectively.

Conclusion

These results suggest that gene polymorphisms relevant to a biological occurrence are more likely to be informative when studied in concert to address potential redundant or conflicting functions that may limit each gene individual contribution. The five markers identified here exemplify this concept though prospective validation in independent cohorts is needed.

Single versus multiple primary melanomas: old questions and new answers

BACKGROUND

In patients with multiple primary melanomas (MPM), mean tumor thickness tends to decrease from the first melanoma to the second melanoma, and prognosis may be improved compared with the prognosis for patients who have a single primary melanoma (SPM). In this study, the authors compared the clinicopathologic features of patients with MPM and SPM to better characterize the differences between these 2 groups and to determine whether or not there is an inherent difference in tumor aggression.

METHODS

In total, 788 patients with melanoma who were enrolled prospectively in the Interdisciplinary Melanoma Cooperative Group database from 2002 to 2008 were studied. Patients with SPM and with MPM were compared with regard to clinical and primary melanoma characteristics.

RESULTS

Of 788 patients with melanoma, 61 patients (7.7%) had 2 or more primary melanomas. The incidence of developing a second primary melanoma 1 year and 5 years after initial melanoma diagnosis was 4.1% and 8.7%, respectively, and most of the risk accumulated within the first year. The incidence of MPM was greater in patients aged ≥60 years than in those aged ≤60 years. The absence or presence of mitosis and other tumor characteristics did not differ significantly between patients with SPM and patients with MPM (P = .61).

CONCLUSIONS

No difference was observed in the presence or absence of mitoses, a marker of tumor proliferation, in SPM and MPM. Because it has been demonstrated that the presence of mitosis is a powerful prognostic marker, the current findings suggested that the tumors behave similarly in patients with SPM and patients with MPM. The authors concluded that differences in tumor thickness and prognosis between SPM and MPM more likely are caused by factors other than tumor biology, such as increased surveillance.