Variation in NF-κB signaling pathways and survival in invasive epithelial ovarian cancer

TNFRSF13B is a potential contributor to prostate cancer

Background

Immunodeficiencies are genetic diseases known to predispose an individual to cancer owing to defective immunity towards malignant cells. However, the link between immunodeficiency and prostate cancer progression remains unclear. Therefore, the aim of this study was to evaluate the effects of common genetic variants among eight immunodeficiency pathway-related genes on disease recurrence in prostate cancer patients treated with radical prostatectomy.

Methods

Genetic and bioinformatic analyses on 19 haplotype-tagging single-nucleotide polymorphisms in eight immunodeficiency pathway-related genes were conducted in 458 patients with prostate cancer after receiving radical prostatectomy. Furthermore, the TNFRSF13B was knocked down in 22Rv1 and PC-3 human prostate cancer cell lines via transfecting short hairpin RNAs and cell proliferation and colony formation assays were performed. The molecular mechanisms underlying the effects of TNFRSF13B were further explored by microarray gene expression profiling.

Results

TNFRSF13B rs4792800 was found to be significantly associated with biochemical recurrence even after adjustment for clinical predictors and false discovery rate correction (adjusted hazard ratio 1.78, 95% confidence interval 1.16–2.71, p = 0.008), and the G allele was associated with higher TNFRSF13B expression (p = 0.038). Increased TNFRSF13B expression suggested poor prognosis in four independent prostate cancer datasets. Furthermore, silencing TNFRSF13B expression resulted in decreased colony formation of 22Rv1 and PC-3 cells through modulating the cell cycle and p53 signalling pathways.

Conclusions

The present study suggests the potential role of immunodeficiency pathway-related genes, primarily TNFRSF13B, in prostate cancer progression.

Supplementary information

The online version contains supplementary material available at 10.1186/s12935-022-02590-2.

A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity

Motivation

Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.

Results

Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells.

Availability and implementation

R codes can be found at https://github.com/petraf01/TSNet.

Supplementary information

Supplementary data are available at Bioinformatics online.

CTD-2020K17.1, a Novel Long Non-Coding RNA, Promotes Migration, Invasion, and Proliferation of Serous Ovarian Cancer Cells In Vitro

Background

Ovarian cancer is the most lethal malignant tumor of the female reproductive system, and the metastasis is one of the major factors that contribute to the poor outcome of patients with OC. Accumulating evidence indicates that lncRNAs are expressed and play important regulatory roles in ovarian cancer.

Material/Methods

Aberrant lncRNAs in primary ovarian cancer tissues (POCTs) and paired omental metastasis tissues (OMTs) of patients with HGSOC were studied via lncRNA microarray. Real-time PCR was performed to examine CTD-2020K17.1 expression in HGSOC tissues from 38 patients, a normal ovarian surface epithelium cell line, and 4 ovarian cancer cell lines. Additionally, Transwell assays, wound healing assays, CCK-8 proliferation assays, and flow cytometry were used to explore the biological function of CTD-2020K17.1 in ovarian cancer cells. Finally, Western blot analysis was used to verify the potential target gene of CTD-2020K17.1.

Results

A novel lncRNA named CTD-2020K17.1 was identified via microarray analysis. Expression of CTD-2020K17.1 was significantly increased in OMTs and in 4 ovarian cancer cell lines compared with POCTs (P<0.05) or normal ovarian surface epithelial cell line (P<0.05). Moreover, CTD-2020K17.1 overexpression promoted migration, invasion, and proliferation of ovarian cancer cells, and CTD-2020K17.1 regulated the expression of CARD11.

Conclusions

CTD-2020K17.1 is significantly upregulated in OMTs and ovarian cancer cell lines. It can promote the migration, invasion, and proliferation of ovarian cancer cells, and CARD11 is regulated by CTD-2020K17.1.

Genetic variants as ovarian cancer first-line treatment hallmarks: A systematic review and meta-analysis

BACKGROUND

The potential predictive value of genetic polymorphisms in ovarian cancer first-line treatment is inconsistently reported. We aimed to review ovarian cancer pharmacogenetic studies to update and summarize the available data and to provide directions for further research.

METHODS

A systematic review followed by a meta-analysis was conducted on cohort studies assessing the involvement of genetic polymorphisms in ovarian cancer first-line treatment response retrieved through a MEDLINE database search by November 2016. Studies were pooled and summary estimates and 95% confidence intervals (CI) were calculated using random or fixed-effects models as appropriate.

RESULTS

One hundred and forty-two studies gathering 106871 patients were included. Combined data suggested that GSTM1-null genotype patients have a lower risk of death compared to GSTM1-wt carriers, specifically in advanced stages (hazard ratio (HR), 0.68; 95% CI, 0.48-0.97) and when submitted to platinum-based chemotherapy (aHR, 0.61; 95% CI, 0.39-0.94). ERCC1 rs11615 and rs3212886 might have also a significant impact in treatment outcome (aHR, 0.67; 95% CI, 0.51-0.89; aHR, 1.28; 95% CI, 1.01-1.63, respectively). Moreover, ERCC2 rs13181 and rs1799793 showed a distinct ethnic behavior (Asians: aHR, 1.41; 95% CI, 0.80-2.49; aHR, 1.07; 95% CI, 0.62-1.86; Caucasians: aHR, 0.10; 95% CI, 0.01-0.96; aHR, 0.18; 95% CI, 0.05-0.68, respectively).

CONCLUSION(S)

The definition of integrative predictive models should encompass genetic information, especially regarding GSTM1 homozygous deletion. Justifying additional pharmacogenetic investigation are variants in ERCC1 and ERCC2, which highlight the DNA Repair ability to ovarian cancer prognosis. Further knowledge could aid to understand platinum-treatment failure and to tailor chemotherapy strategies.

Microbial infection, inflammation and epithelial ovarian cancer

Ovarian cancer is the most common, and life-threatening, type of female gynecological cancer. The etiology of ovarian cancer remains unclear, and there are currently no effective screening or treatment methods for the disease. Microbial infection serves a marked function in inducing carcinogenesis. A number of studies have identified pelvic inflammatory disease as a risk factor for epithelial ovarian cancer. Thus, it is hypothesized that microbial infection may contribute to ovarian cancer. In the present review, the microorganisms that have been identified to be associated with ovarian cancer and the underlying molecular mechanisms involved are discussed. Infection-induced chronic inflammation is considered an important process for carcinogenesis, cancer progression and metastasis. Therefore, the pathological process and associated inflammatory factors are reviewed in the present paper.

A targeted genetic association study of epithelial ovarian cancer susceptibility

BACKGROUND

Genome-wide association studies have identified several common susceptibility alleles for epithelial ovarian cancer (EOC). To further understand EOC susceptibility, we examined previously ungenotyped candidate variants, including uncommon variants and those residing within known susceptibility loci.

RESULTS

At nine of eleven previously published EOC susceptibility regions (2q31, 3q25, 5p15, 8q21, 8q24, 10p12, 17q12, 17q21.31, and 19p13), novel variants were identified that were more strongly associated with risk than previously reported variants. Beyond known susceptibility regions, no variants were found to be associated with EOC risk at genome-wide statistical significance (p <5x10(-8)), nor were any significant after Bonferroni correction for 17,000 variants (p< 3x10-6).

METHODS

A customized genotyping array was used to assess over 17,000 variants in coding, non-coding, regulatory, and known susceptibility regions in 4,973 EOC cases and 5,640 controls from 13 independent studies. Susceptibility for EOC overall and for select histotypes was evaluated using logistic regression adjusted for age, study site, and population substructure.

CONCLUSION

Given the novel variants identified within the 2q31, 3q25, 5p15, 8q21, 8q24, 10p12, 17q12, 17q21.31, and 19p13 regions, larger follow-up genotyping studies, using imputation where necessary, are needed for fine-mapping and confirmation of low frequency variants that fall below statistical significance.

Cross Cancer Genomic Investigation of Inflammation Pathway for Five Common Cancers: Lung, Ovary, Prostate, Breast, and Colorectal Cancer

BACKGROUND

Inflammation has been hypothesized to increase the risk of cancer development as an initiator or promoter, yet no large-scale study of inherited variation across cancer sites has been conducted.

METHODS

We conducted a cross-cancer genomic analysis for the inflammation pathway based on 48 genome-wide association studies within the National Cancer Institute GAME-ON Network across five common cancer sites, with a total of 64 591 cancer patients and 74 467 control patients. Subset-based meta-analysis was used to account for possible disease heterogeneity, and hierarchical modeling was employed to estimate the effect of the subcomponents within the inflammation pathway. The network was visualized by enrichment map. All statistical tests were two-sided.

RESULTS

We identified three pleiotropic loci within the inflammation pathway, including one novel locus in Ch12q24 encoding SH2B3 (rs3184504), which reached GWAS significance with a P value of 1.78 x 10(-8), and it showed an association with lung cancer (P = 2.01 x 10(-6)), colorectal cancer (GECCO P = 6.72x10(-6); CORECT P = 3.32x10(-5)), and breast cancer (P = .009). We also identified five key subpathway components with genetic variants that are relevant for the risk of these five cancer sites: inflammatory response for colorectal cancer (P = .006), inflammation related cell cycle gene for lung cancer (P = 1.35x10(-6)), and activation of immune response for ovarian cancer (P = .009). In addition, sequence variations in immune system development played a role in breast cancer etiology (P = .001) and innate immune response was involved in the risk of both colorectal (P = .022) and ovarian cancer (P = .003).

CONCLUSIONS

Genetic variations in inflammation and its related subpathway components are keys to the development of lung, colorectal, ovary, and breast cancer, including SH2B3, which is associated with lung, colorectal, and breast cancer.

Discover the molecular biomarker associated with cell death and extracellular matrix module in ovarian cancer

High throughput technologies have provided many new research methods for ovarian cancer investigation. In tradition, in order to find the underlying functional mechanisms of the survival-associated genes, gene sets enrichment analysis (GSEA) is always regarded as the important choice. However, GSEA produces too many candidate genes and cannot discover the signaling transduction cascades. In this work, we have used a network-based strategy to optimize the discovery of biomarkers using multifactorial data, including patient expression, clinical survival, and protein-protein interaction (PPI) data. The biomarkers discovered by this strategy belong to the network-based biomarker, which is apt to reveal the underlying functional mechanisms of the biomarker. In this work, over 400 expression arrays in ovarian cancer have been analyzed: the results showed that cell death and extracellular module are the main themes related to ovarian cancer progression.