Identification of prognostic biomarkers for malignant melanoma using microarray datasets

MACF1 mutations predict poor prognosis: a novel potential therapeutic target for breast cancer

OBJECTIVE

Microtubule actin cross-linking factor 1 (MACF1) mutations are known to play an important role in the progression of various cancers. However, its role in breast cancer remains to be determined. In this study, we investigated how MACF1 mutations may play a role in breast cancer development.

METHODS

The gene-expression profile data of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA)-Breast cancer cohort. We estimated the influence of MACF1 mutations on patient clinical prognosis using the Kaplan-Meier method. Further, patients with MACF1-mutant (MACF1-MT) and MACF1-wild-type (MACF1-WT) were compared to identify the differentially expressed genes (DEGs). We also performed functional enrichment analyses, constructed protein-protein interaction (PPI) and competing endogenous RNA (ceRNA) networks, and investigated the correlation between MACF1 mutations and immune-cell infiltration. To explore the prognostic value of MACF1 mutations, a nomogram was developed based on MACF1 mutations and other clinicopathological parameters.

RESULTS

Patients with MACF1-MT had a worse prognosis and higher tumor mutation burden score (P < 0.05) than patients with MACF1-WT. MACF1 mutations were demonstrated to upregulate the mTOR signaling pathway and alter energy metabolism and tumor immune microenvironment. Thus, MACF1 mutations might affect immunogenicity and result in a lower response to immunotherapy. By analyzing the Genomics of Drug Sensitivity in Cancer (GDSC), the sensitivity of breast cancer cells to 13 drugs was found to be significantly enhanced by MACF1 mutations. The prognostic model was verified in predicting the outcome of breast cancer patients.

CONCLUSION

MACF1 mutations might be a potential prognostic biomarker and a therapeutic target for breast cancer.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1–2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.