Disease biomarker identification from gene network modules for metastasized breast cancer

Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell level

BACKGROUND

Intrauterine adhesion (IUA) is a common cause of clinically refractory infertility, and there exists significant heterogeneity in the treatment outcomes among IUA patients with the similar severity after transcervical resection of adhesion(TCRA). The underlying mechanism of different treatment outcomes occur remains elusive, and the precise contribution of various cell subtypes in this process remains uncertain.

RESULTS

Here, we performed single-cell transcriptome sequencing on 10 human endometrial samples to establish a single-cell atlas differences between patients who responded to estrogen therapy and those who did not. The results showed increased infiltration of immune cells such as monocyte macrophages, T cells, and natural killer (NK) cells in patients who did not respond to estrogen therapy. Our findings indicate that distinct fibroblast subsets are implicated in the modulation of the Wnt, Hippo, and Hedgehog signaling pathways, as evidenced by functional enrichment analyses. This may have implications for the therapeutic efficacy in patients with IUA. Furthermore, we delineated the markers and transcriptional status of different macrophage subsets and identified two cell clusters, CXCL10high and CCL4L2high macrophage subsets, which are intimately associated with inflammation and fibrosis. The state of fibrosis and inflammatory response in human endometrial tissues with disparate treatment outcomes is revealed, and providing evidence to clarify the underlying determinants of sensitivity to estrogen therapy.

CONCLUSIONS

We described the transcriptional status of different cell subtypes in the two groups of patients, providing new ideas for exploring the molecular mechanism of the difference in the effectiveness of estrogen therapy in patients, and providing theoretical basis for providing precise and individualized treatment plans for IUA patients.

Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinomas (HNSCCs) are derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx. The present study aimed to identify the novel genes and pathways underlying HNSCC. Despite the advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise, and the mortality of advanced HNSCC is expected to increase by 50%. Therefore, there is an urgent need for effective biomarkers to predict HNSCC patients’ prognosis and provide guidance to the personalized treatment.

Methods: Both HNSCC clinical and gene expression data were abstracted from The Cancer Genome Atlas (TCGA) database. Intersecting analysis was adopted between the gene expression matrix of HNSCC patients from TCGA database to extract TME-related genes. Differential gene expression analysis between HNSCC tissue samples and normal tissue samples was performed by R software. Then, HNSCC patients were categorized into clusters 1 and 2 via NMF. Next, TME-related prognosis genes (p < 0.05) were analyzed by univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. Finally, nine genes were selected to construct a prognostic risk model and a prognostic gene signature. We also established a nomogram using relevant clinical parameters and a risk score. The Kaplan–Meier curve, survival analysis, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the concordance index (C-index) were carried out to assess the accuracy of the prognostic risk model and nomogram. Potential molecular mechanisms were revealed by gene set enrichment analysis (GSEA). Additionally, gene correlation analysis and immune cell correlation analysis were conducted for further enriching our results.

Results: A novel HNSCC prognostic model was established based on the nine genes (GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11). The value of area under the ROC curves (AUCs) (0.769, 0.841, and 0.816) in TCGA whole set showed that the model effectively predicted the 1-, 3-, and 5-year overall survival (OS). Results of the Cox regression assessment confirmed the nine-gene signature as a reliable independent prognostic factor in HNSCC patients. The prognostic nomogram developed using multivariate Cox regression analysis showed a superior C-index over other clinical signatures. Also, the calibration curve had a high level of concordance between estimated OS and the observed OS. This showed that its clinical net can precisely estimate the one-, three-, and five-year OS in HNSCC patients. The gene set enrichment analysis (GSEA) to some extent revealed the immune- and tumor-linked cascades.

Conclusion: In conclusion, the TME-related nine-gene signature and nomogram can effectively improve the estimation of prognosis in patients with HNSCC.

Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders

Different metabolic and hormonal disorders like type 2 diabetes mellitus (T2DM), obesity, and polycystic ovary syndrome (PCOS) have tangible socio-economic impact. Prevalence of these metabolic and hormonal disorders is steadily increasing among women. There are clinical evidences that these physiological conditions are related to the manifestation of different gynecological cancers and their poor prognosis. The relationship between metabolic and hormonal disorders with gynecological cancers is quite complex. The need for gene level association study is extremely important to find markers and predicting risk factors. In the current work, we have selected metabolic disorders like T2DM and obesity, hormonal disorder PCOS, and 4 different gynecological cancers like endometrial, uterine, cervical, and triple negative breast cancer (TNBC). The gene list was downloaded from DisGeNET database (v 6.0). The protein interaction network was constructed using HIPPIE (v 2.2) and shared proteins were identified. Molecular comorbidity index and Jaccard coefficient (degree of similarity) between the diseases were determined. Pathway enrichment analysis was done using ReactomePA and significant modules (clusters in a network) of the constructed network was analyzed by MCODE plugin of Cytoscape. The comorbid conditions like PCOS-obesity found to increase the risk factor of ovarian and triple negative breast cancers whereas PCOS alone has highest contribution to the endometrial cancer. Different gynecological cancers were found to be differentially related to the metabolic/hormonal disorders and comorbid condition.

WDR5 promotes the tumorigenesis of oral squamous cell carcinoma via CARM1/β-catenin axis

Oral squamous cell carcinoma (OSCC) is a malignancy all over the world. WD repeat domain 5 (WDR5) is involved in cancer progression. In addition, it was reported that WDR5 is upregulated in head and neck cancer, while its role in OSCC is unknown. First, the expression of WDR5 in oral cancer tissues and cells was examined by qRT-PCR, IHF and western blot. CCK-8 assay was performed to test the cell viability. Cell migration was assessed by transwell assay. Knocking down WDR5 or CARM1 in oral cancer cells to detect its function on cancer growth, WDR5 and CARM1 were significantly upregulated in OSCC. Silencing WDR5 suppressed OSCC cell viability and migration. CARM1 level in OSCC cells was significantly inhibited by WDR5 downregulation, and CARM1 elevation could rescue the effect of WDR5 knockdown on tumorigenesis of OSCC. Moreover, silencing of WDR5 notably inactivated β-catenin signaling pathway, while this phenomenon was restored by CARM1 overexpression. Silencing of WDR5 attenuated the tumorigenesis of OSCC via CARM1/β-catenin axis. Thus, WDR5 might be a target for OSCC treatment.

ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets

Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php.

Identification of profiles of volatile organic compounds in exhaled breath by means of an electronic nose as a proposal for a screening method for breast cancer: a case-control study

The objective of the present study was to identify volatile prints from exhaled breath, termed breath-print, from breast cancer (BC) patients and healthy women by means of an electronic nose and to evaluate its potential use as a screening method. A cross-sectional study was performed on 443 exhaled breath samples from women, of whom 262 had been diagnosed with BC by biopsy and 181 were healthy women (control group). Breath-print analysis was performed utilizing the Cyranose 320 electronic nose. Group data were evaluated by principal component analysis (PCA), canonical discriminant analysis (CDA), and support vector machine (SVM), and the test's diagnostic power was evaluated by means of receiver operating characteristic (ROC) curves. The results obtained using the model generated from the CDA, which best describes the behavior of the assessed groups, indicated that the breath-print of BC patients was different from that of healthy women and that they presented with a variability of up to 98.8% and a correct classification of 98%. The sensitivity, specificity, negative predictive value, and positive predictive value reached 100% according to the ROC curve. The present study demonstrates the capability of the electronic nose to separate between healthy subjects and BC patients. This research could have a beneficial impact on clinical practice as we consider that this test could probably be used at the first point before the application of established gold tests (mammography, ultrasound, and biopsy) and substantially improve screening tests in the general population.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Discovery and characterization of functional modules associated with body weight in broilers

Aim of the present study was to investigate whether body weight (BW) in broilers is associated with functional modular genes. To this end, first a GWAS for BW was conducted using 6,598 broilers and the high density SNP array. The next step was to search for positional candidate genes and QTLs within strong LD genomic regions around the significant SNPs. Using all positional candidate genes, a network was then constructed and community structure analysis was performed. Finally, functional enrichment analysis was applied to infer the functional relevance of modular genes. A total number of 645 positional candidate genes were identified in strong LD genomic regions around 11 genome-wide significant markers. 428 of the positional candidate genes were located within growth related QTLs. Community structure analysis detected 5 modules while functional enrichment analysis showed that 52 modular genes participated in developmental processes such as skeletal system development. An additional number of 14 modular genes (GABRG1, NGF, APOBEC2, STAT5B, STAT3, SMAD4, MED1, CACNB1, SLAIN2, LEMD2, ZC3H18, TMEM132D, FRYL and SGCB) were also identified as related to body weight. Taken together, current results suggested a total number of 66 genes as most plausible functional candidates for the trait examined.

Epigenetic arginine methylation in breast cancer: emerging therapeutic strategies

Breast cancer is a heterogeneous disease, and the complexity of breast carcinogenesis is associated with epigenetic modification. There are several major classes of epigenetic enzymes that regulate chromatin activity. This review will focus on the nine mammalian protein arginine methyltransferases (PRMTs) and the dysregulation of PRMT expression and function in breast cancer. This class of enzymes catalyse the mono- and (symmetric and asymmetric) di-methylation of arginine residues on histone and non-histone target proteins. PRMT signalling (and R methylation) drives cellular proliferation, cell invasion and metastasis, targeting (i) nuclear hormone receptor signalling, (ii) tumour suppressors, (iii) TGF-β and EMT signalling and (iv) alternative splicing and DNA/chromatin stability, influencing the clinical and survival outcomes in breast cancer. Emerging reports suggest that PRMTs are also implicated in the development of drug/endocrine resistance providing another prospective avenue for the treatment of hormone resistance and associated metastasis. The complexity of PRMT signalling is further underscored by the degree of alternative splicing and the scope of variant isoforms (with distinct properties) within each PRMT family member. The evolution of PRMT inhibitors, and the ongoing clinical trials of PRMT inhibitors against a subgroup of solid cancers, coupled to the track record of lysine methyltransferases inhibitors in phase I/II clinical trials against cancer underscores the potential therapeutic utility of targeting PRMT epigenetic enzymes to improve survival outcomes in aggressive and metastatic breast cancer.

PNME - A gene-gene parallel network module extraction method

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.

It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.