Identifying condition specific key genes from basal-like breast cancer gene expression data

Oral cancers preceded by proliferative verrucous leukoplakia exhibit distinctive molecular features

OBJECTIVE

Proliferative verrucous leukoplakia (PVL) has high rates of malignant transformation into oral squamous cell carcinoma (OSCC), but the clinical and evolutionary pattern of OSCC from PVL (PVL-OSCC) is more favorable than that of OSCC not preceded by PVL (OSCC). Here, we aimed to explore the pathophysiologic differences between PVL-OSCC and OSCC through transcriptomic and DNA methylation analyses.

MATERIALS AND METHODS

In this case-control study, oral biopsies from 8 PVL-OSCC and 10 OSCC patients were obtained for global sequencing using RNAseq and a genome-wide DNA methylation analysis via the Infinium EPIC Platform (graphical abstract).

RESULTS

One hundred and thirty-three differentially expressed genes (DEGs) were detected, 94 of them upregulated in OSCC. Most of these genes were previously described in cancer and associated with prognosis. The integrative analysis revealed 26 DEGs, corresponding to 37 CpGs, whose promoters were regulated by DNA methylation. Twenty-nine of the CpGs were found as hypermethylated in PVL-OSCC. Only 5 of the genes that were aberrantly methylated and differentially expressed were upregulated in PVL-OSCC patients, whereas 21 were underexpressed.

CONCLUSIONS

PVL-OSCC patients presented lower expression of cancer-related genes. Hypermethylation of the promoter region of many genes was also noticed, indicating that DNA methylation could be a regulatory mechanism.

SHC4 orchestrates β-catenin pathway-mediated metastasis in triple-negative breast cancer by promoting Src kinase autophosphorylation

Triple-negative breast cancer (TNBC) is highly aggressive and metastatic, and has the poorest prognosis among all breast cancer subtypes. Activated β-catenin is enriched in TNBC and involved in Wnt signaling-independent metastasis. However, the underlying mechanisms of β-catenin activation in TNBC remain unknown. Here, we found that SHC4 was upregulated in TNBC and high SHC4 expression was significantly correlated with poor outcomes. Overexpression of SHC4 promoted TNBC aggressiveness in vitro and facilitated TNBC metastasis in vivo. Mechanistically, SHC4 interacted with Src and maintained its autophosphorylated activation, which activated β-catenin independent of Wnt signaling, and finally upregulated the transcription and expression of its downstream genes CD44 and MMP7. Furthermore, we determined that the PxPPxPxxxPxxP sequence on CH2 domain of SHC4 was critical for SHC4-Src binding and Src kinase activation. Overall, our results revealed the mechanism of β-catenin activation independent of Wnt signaling in TNBC, which was driven by SHC4-induced Src autophosphorylation, suggesting that SHC4 might be a potential prognostic marker and therapeutic target in TNBC.

Bidirectional Mediation Effects between Intratumoral Microbiome and Host DNA Methylation Changes Contribute to Stomach Adenocarcinoma

The induction of aberrant DNA methylation is the major pathway by which Helicobacter pylori infection induces stomach adenocarcinoma (STAD). The involvement of the non-H. pylori gastric microbiota in this mechanism remains to be examined. RNA sequencing data, clinical information, and DNA methylation data were obtained from The Cancer Genome Atlas (TCGA) STAD project. The Kraken 2 pipeline was employed to explore the microbiome profiles. The microbiome was associated with occurrence, distal metastasis, and prognosis, and differential methylation changes related to distal metastasis and prognosis were analyzed. Bi-directional mediation effects of the intratumoral microbiome and host DNA methylation changes on the metastasis and prognosis of STAD were identified by mediation analysis. The expression of the ZNF215 gene was verified by real-time quantitative PCR (RT-qPCR). A cell counting kit 8 (CCK8) cell proliferation experiment and a cell clone formation experiment were used to evaluate the proliferation and invasion abilities of gastric cells. Our analysis revealed that H. pylori and other cancer-related microorganisms were related to the occurrence, progression, or prognosis of STAD. The related methylated genes were particularly enriched in related cancer pathways. Kytococcus sedentarius and Actinomyces oris, which interacted strongly with methylation changes in immune genes, were associated with prognosis. Cell experiments verified that Staphylococcus saccharolyticus could promote the proliferation and cloning of gastric cells by regulating the gene expression level of the ZNF215 gene. Our study suggested that the bi-directional mediation effect between intratumoral microorganisms and host epigenetics was key to the distal metastasis of cancer cells and survival deterioration in the tumor microenvironment of stomach tissues of patients with STAD. IMPORTANCE The burgeoning field of oncobiome research declared that members of the intratumoral microbiome besides Helicobacter pylori existed in tumor tissues and participated in the occurrence and development of gastric cancer, and the methylation of host DNA may be a potential target of microbes and their metabolites. Current research focuses mostly on species composition, but the functional genes of the members of the microbiota are also key to their interaction with the host. Therefore, we focused on characterizing the species composition and functional gene composition of microbes in gastric cancer, and we suggest that microbes may further participate in the occurrence and development of cancer by influencing abnormal epigenetic changes in the host. Some key bioinformatics analysis results were verified by in vitro experiments. Thus, we consider that the tumor microbiota-host epigenetic axis of gastric cancer microorganisms and the host explains the mechanism of the microbiota participating in cancer occurrence and development, and we make some verifiable experimental predictions.

Whole Transcriptome Analysis of Breast Cancer Tumors during Neoadjuvant Chemotherapy: Association with Hematogenous Metastasis

The assessment of molecular genetic landscape changes during NAC and the relationship between molecular signatures in residual tumors are promising approaches for identifying effective markers of outcome in breast cancer. The majority of the data in the literature present the relationship between the molecular genetic landscape and the response to NAC or are simply descriptive. The present study aimed to determine changes in expression profiles during NAC and assess the relationship between gene expression and the outcome of patients with luminal B HER2 breast cancer depending on distant hematogenous metastasis. The study included 39 patients with luminal B HER2-BC. The patients received 6-8 courses of NAC, and paired samples consisting of biopsy and surgical materials were analyzed. A full transcriptome microarray analysis was performed using the human Clariom™ S Assay platform (Affymetrix, 3450 Central Expy, Santa Clara, CA, 95051, USA). A comparison of the expression profiles of patients with breast cancer before and after NAC, depending on the status of hematogenous metastasis, was conducted. It was shown that the amount of DEGs in the tumor was reduced by more than six times after NAC. The top 10 signaling pathways were also found, the activity of which varied depending on the status of hematogenous metastasis before and after NAC. In addition, the association of DEGs with hematogenous metastasis in patients with breast cancer was evaluated: MFS was assessed depending on the expression level of 21 genes. It was shown that MFS was significantly associated with the expression level and pattern of nine genes. The expression levels of nine DEGs in the tumors of patients with breast cancer after NAC were significantly correlated with MFS when the status of hematogenous metastasis was taken into account.

DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes

Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. Results from four case studies confirm the validity and relevance of the proposed methods, further unveiling critical directions for research on biotechnology and biomedicine. Availability: DISA is freely available at https://github.com/JupitersMight/DISA under the MIT license.

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.