deltaXpress (ΔXpress): a tool for mapping differentially correlated genes using single-cell qPCR data.

BACKGROUND: High-throughput experiments provide deep insight into the molecular biology of different species, but more tools need to be developed to handle this type of data. At the transcriptomics level, quantitative Polymerase Chain Reaction technology (qPCR) can be affordably adapted to produce high-throughput results through a single-cell approach. In addition to comparative expression profiles between groups, single-cell approaches allow us to evaluate and propose new dependency relationships among markers. However, this alternative has not been explored before for large-scale qPCR-based experiments. RESULTS: Herein, we present deltaXpress (ΔXpress), a web app for analyzing data from single-cell qPCR experiments using a combination of HTML and R programming languages in a friendly environment. This application uses cycle threshold (Ct) values and categorical information for each sample as input, allowing the best pair of housekeeping genes to be chosen to normalize the expression of target genes. ΔXpress emulates a bulk analysis by observing differentially expressed genes, but in addition, it allows the discovery of pairwise genes differentially correlated when comparing two experimental conditions. Researchers can download normalized data or use subsequent modules to map differentially correlated genes, perform conventional comparisons between experimental groups, obtain additional information about their genes (gene glossary), and generate ready-to-publication images (600 dots per inch). CONCLUSIONS: ΔXpress web app is freely available to non-commercial users at https://alexismurillo.shinyapps.io/dXpress/ and can be used for different experiments in all technologies involving qPCR with at least one housekeeping region.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients.

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox ß regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-ß pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Insights from a Computational-Based Approach for Analyzing Autophagy Genes across Human Cancers.

In the last decade, there has been a boost in autophagy reports due to its role in cancer progression and its association with tumor resistance to treatment. Despite this, many questions remain to be elucidated and explored among the different tumors. Here, we used omics-based cancer datasets to identify autophagy genes as prognostic markers in cancer. We then combined these findings with independent studies to further characterize the clinical significance of these genes in cancer. Our observations highlight the importance of innovative approaches to analyze tumor heterogeneity, potentially affecting the expression of autophagy-related genes with either pro-tumoral or anti-tumoral functions. In silico analysis allowed for identifying three genes (TBC1D12, KERA, and TUBA3D) not previously described as associated with autophagy pathways in cancer. While autophagy-related genes were rarely mutated across human cancers, the expression profiles of these genes allowed the clustering of different cancers into three independent groups. We have also analyzed datasets highlighting the effects of drugs or regulatory RNAs on autophagy. Altogether, these data provide a comprehensive list of targets to further the understanding of autophagy mechanisms in cancer and investigate possible therapeutic targets.

Promoter hypermethylation of RARB and GSTP1 genes in plasma cell-free DNA as breast cancer biomarkers in Peruvian women.

BACKGROUND: Promoter hypermethylation is one of the enabling mechanisms of hallmarks of cancer. Tumor suppressor genes like RARB and GSTP1 have been reported as hypermethylated in breast cancer tumors compared with normal tissues in several populations. This case-control study aimed to determine the association between the promoter methylation ratio (PMR) of RARB and GSTP1 genes (separately and as a group) with breast cancer and its clinical-pathological variables in Peruvian patients, using a liquid biopsy approach. METHODS: A total of 58 breast cancer patients and 58 healthy controls, matched by age, participated in the study. We exacted cell-free DNA (cfDNA) from blood plasma and converted it by bisulfite salts. Methylight PCR was performed to obtain the PMR value of the studied genes. We determined the association between PMR and breast cancer, in addition to other clinicopathological variables. The sensitivity and specificity of the PMR of these genes were obtained. RESULTS: A significant association was not found between breast cancer and the RARB PMR (OR = 1.90; 95% CI [0.62-6.18]; p = 0.210) or the GSTP1 PMR (OR = 6.57; 95% CI [0.75-307.66]; p = 0.114). The combination of the RARB + GSTP1 PMR was associated with breast cancer (OR = 2.81; 95% CI [1.02-8.22]; p = 0.026), controls under 50 years old (p = 0.048), patients older than 50 (p = 0.007), and postmenopausal (p = 0.034). The PMR of both genes showed a specificity of 86.21% and a sensitivity of 31.03%. CONCLUSION: Promoter hypermethylation of RARB + GSTP1 genes is associated with breast cancer, older age, and postmenopausal Peruvian patients. The methylated promoter of the RARB + GSTP1 genes needs further validation to be used as a biomarker for liquid biopsy and as a recommendation criterion for additional tests in asymptomatic women younger than 50 years.

Deciphering the Functional Status of Breast Cancers through the Analysis of Their Extracellular Vesicles.

Breast cancer (BC) accounts for the highest incidence of tumor-related mortality among women worldwide, justifying the growing search for molecular tools for the early diagnosis and follow-up of BC patients under treatment. Circulating extracellular vesicles (EVs) are membranous nanocompartments produced by all human cells, including tumor cells. Since minimally invasive methods collect EVs, which represent reservoirs of signals for cell communication, these particles have attracted the interest of many researchers aiming to improve BC screening and treatment. Here, we analyzed the cargoes of BC-derived EVs, both proteins and nucleic acids, which yielded a comprehensive list of potential markers divided into four distinct categories, namely, (i) modulation of aggressiveness and growth; (ii) preparation of the pre-metastatic niche; (iii) epithelial-to-mesenchymal transition; and (iv) drug resistance phenotype, further classified according to their specificity and sensitivity as vesicular BC biomarkers. We discuss the therapeutic potential of and barriers to the clinical implementation of EV-based tests, including the heterogeneity of EVs and the available technologies for analyzing their content, to present a consistent, reproducible, and affordable set of markers for further evaluation.

Impact of mini-driver genes in the prognosis and tumor features of colorectal cancer samples: a novel perspective to support current biomarkers.

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its development is associated with the gains and/or losses of genetic material, which leads to the emergence of main driver genes with higher mutational frequency. In addition, there are other genes with mutations that have weak tumor-promoting effects, known as mini-drivers, which could aggravate the development of oncogenesis when they occur together. The aim of our work was to use computer analysis to explore the survival impact, frequency, and incidence of mutations of possible mini-driver genes to be used for the prognosis of CRC. Methods: We retrieved data from three sources of CRC samples using the cBioPortal platform and analyzed the mutational frequency to exclude genes with driver features and those mutated in less than 5% of the original cohort. We also observed that the mutational profile of these mini-driver candidates is associated with variations in the expression levels. The candidate genes obtained were subjected to Kaplan-Meier curve analysis, making a comparison between mutated and wild-type samples for each gene using a p-value threshold of 0.01. Results: After gene filtering by mutational frequency, we obtained 159 genes of which 60 were associated with a high accumulation of total somatic mutations with Log2 (fold change) > 2 and p values < 10-5. In addition, these genes were enriched to oncogenic pathways such as epithelium-mesenchymal transition, hsa-miR-218-5p downregulation, and extracellular matrix organization. Our analysis identified five genes with possible implications as mini-drivers: DOCK3, FN1, PAPPA2, DNAH11, and FBN2. Furthermore, we evaluated a combined classification where CRC patients with at least one mutation in any of these genes were separated from the main cohort obtaining a p-value < 0.001 in the evaluation of CRC prognosis. Conclusion: Our study suggests that the identification and incorporation of mini-driver genes in addition to known driver genes could enhance the accuracy of prognostic biomarkers for CRC.

Chip-based digital Polymerase Chain Reaction as quantitative technique for the detection of PIK3CA mutations in breast cancer patients.

Background: PIK3CA is a gene frequently mutated in breast cancer. With the FDA approval of alpelisib, the evaluation of PIK3CA for activating mutations is becoming routinely. Novel platforms for gene analysis as digital PCR (dPCR) are emerging as a potential replacement for the traditional Sanger sequencing. However, there are still few studies on chip-based dPCR to detect mutations in tumor samples. Thus, this cross-sectional study aimed to assess the sensibility of a chip-based dPCR to detect and quantify PIK3CA mutations and compare its performance with Sanger sequencing. Materials and Methods: Tumor samples from 57 breast cancer patients (22 pre-treatment samples, 32 tumors after neoadjuvant chemotherapy, and three lymph nodes) were collected and analyzed by Sanger sequencing and dPCR for the three PIK3CA most relevant mutations (p.E545K, p. H1047R, and p. H1047L). Digital PCR sensitivity, specificity, and overall performance were estimated by contingency tables, receptor operator characteristic (ROC), and area under the curve (AUC). Association of PIK3CA mutations with clinicopathological variables was conducted. Results: Sanger sequencing identified PIK3CA mutations in six patients (10.5%), two with p. H1047R, and four with p. E545K. Digital PCR confirmed those mutations and identified 19 additional patients with at least one mutation. Comparison between dPCR and Sanger sequencing showed a sensitivity of 100% (95% CI 53-100%), and a specificity of 84.2% (95% CI 83-84.2%). Besides, p. H1047R mutation detected by dPCR showed a significant association with breast cancer phenotype (p = 0.019) and lymphatic nodes infiltration (p = 0.046). Conclusions: Digital PCR showed a high sensitivity to detect mutations in tumor samples and it might be capable to detect low-rate mutations and tumor subpopulations not detected by Sanger sequencing.

Disruption of miRNA-mRNA Networks Defines Novel Molecular Signatures for Penile Carcinogenesis.

Penile cancer (PeC) carcinogenesis is not fully understood, and no biomarkers are reported in clinical practice. We aimed to investigate molecular signatures based on miRNA and mRNA and perform an integrative analysis to identify molecular drivers and pathways for PeC development. Affymetrix miRNA microarray was used to identify differentially expressed miRNAs (DEmiRs) comparing 11 tumoral tissues (TT) paired with non-neoplastic tissues (NNT) with further validation in an independent cohort (n = 13). We also investigated the mRNA expression of 83 genes in the total sample. Experimentally validated targets of DEmiRs, miRNA-mRNA networks, and enriched pathways were evaluated in silico. Eight out of 69 DEmiRs identified by microarray analysis were validated by qRT-PCR (miR-145-5p, miR-432-5p, miR-487b-3p, miR-30a-5p, miR-200a-5p, miR-224-5p, miR-31-3p and miR-31-5p). Furthermore, 37 differentially expressed genes (DEGs) were identified when comparing TT and NNT. We identified four downregulated DEmiRs (miR-30a-5p, miR-432-5p, miR-487b-3p, and miR-145-5p) and six upregulated DEGs (IL1A, MCM2, MMP1, MMP12, SFN and VEGFA) as potential biomarkers in PeC by their capacity of discriminating TT and NNT with accuracy. The integration analysis showed eight dysregulated miRNA-mRNA pairs in penile carcinogenesis. Taken together, our findings contribute to a better understanding of the regulatory roles of miRNAs and altered transcripts levels in penile carcinogenesis.