BNC2 is a putative tumor suppressor gene in high-grade serous ovarian carcinoma and impacts cell survival after oxidative stress

Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pretrained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Efficient detection and characterization of targets of natural selection using transfer learning

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Single-cell transcriptomics reveals the multidimensional dynamic heterogeneity from primary to metastatic gastric cancer

Reprogramming of the tumor microenvironment (TME) plays a critical role in gastric cancer (GC) progression and metastasis. However, the multidimensional features between primary tumors and organ-specific metastases remain poorly understood. In this study, we characterized the dynamic heterogeneity of GC from primary to metastatic stages. We identified seven major cell types and 27 immune and stromal subsets. Immune cells decreased, while immunosuppressive cells increased in ovarian and peritoneal metastases. A 30-gene signature for ovarian metastasis was validated in GC cohorts. Additionally, critical ligand-receptor interactions, including LGALS9-MET in liver metastasis and PVR-TIGIT in lymph node metastasis, were identified as potential therapeutic targets. Furthermore, CLOCK, a transcription factor, was associated with poor prognosis and influenced immune cell interactions and migration. Collectively, this study provides valuable insights into TME dynamics in GC and highlights potential avenues for targeted therapies.

Overexpression of Basonuclin Zinc Finger Protein 2 in stromal cell is related to mesenchymal phenotype and immunosuppression of mucinous colorectal adenocarcinoma

BACKGROUND

Mucinous carcinoma (MC) is a distinct histologic subtype of colorectal cancer (CRC) that is less studied and associated with poor prognosis. This study aimed to identify MC-specific therapeutic targets and biomarkers to improve the prognosis of this aggressive disease.

METHODS

CRC samples from The Cancer Genome Atlas (TCGA) were categorized into MC and non-MC (NMC) groups based on histologic type. A multi-scale embedded gene co-expression network analysis (MEGENA) was constructed to identify gene modules associated with the MC group. The potential functions of Basonuclin Zinc Finger Protein 2 (BNC2) were further analyzed using the Biomarker Exploration for Solid Tumors (BEST) database. In vivo and in vitro experiments were conducted to validate the predicted results.

RESULTS

We identified the stromal component-related gene, BNC2, in the MC population. This gene is associated with a shorter progression-free interval (PFI) in CRC patients. BNC2 promotes FAP (encoding Fibroblast Activation Protein Alpha) transcription in cancer-associated fibroblasts (CAFs) and is involved in angiogenesis through two pathways. Additionally, BNC2 enhances tumor cell invasiveness in a CAF-dependent manner. Patients with high BNC2 expression benefited less from immunotherapy compared to those with low BNC2 expression.

CONCLUSIONS

Our study highlights the clinical importance of BNC2 in MC, and targeting BNC2 on stromal cells (fibroblasts and endothelial cells) may be an effective strategy for treating MC.

MCPIP1 modulates the miRNA‒mRNA landscape in keratinocyte carcinomas

BACKGROUND

Monocyte Chemotactic Protein 1-Induced Protein 1 (MCPIP1, also called Regnase-1) is a negative modulator of inflammation with tumor-suppressive properties. Mice with keratinocyte-specific deletion of the Zc3h12a gene, encoding MCPIP1, (Mcpip1eKO mice) are more susceptible to the development of epidermal papillomas initiated by 7,12-dimethylbenz[a]-anthracene (DMBA) and promoted by 2-O-tetradecanoylphorbol-13-acetate (TPA).

METHODS

The aim of this study was to investigate the MCPIP1 RNase-dependent microRNA (miRNA)‒mRNA regulatory network in chemically induced squamous cell carcinoma (SCC)-like skin papillomas. Next-generation sequencing (NGS) coupled with bioinformatic analysis was used to shortlist the MCPIP1-dependent changes in protein-coding genes and miRNAs. The expression levels of the selected miRNAs were analyzed by quantitative PCR in human keratinocytes with MCPIP1 silencing. Functional studies were performed in human keratinocytes transfected with appropriate miRNA mimics. The DIANA-microT-CDS algorithm and DIANA-TarBase v7 database were used to predict potential target genes and identify the experimentally validated targets of differentially expressed (DE) miRNAs.

RESULTS

RNA sequencing (RNA-Seq) analysis of control and Mcpip1eKO DMBA/TPA-induced papillomas revealed transcriptome changes, with 2400 DE protein-coding genes and 33 DE miRNAs. The expression of miR-223-3p, miR-376c-3p, and miR-139-5p was confirmed to be dependent on MCPIP1 activity in both murine and human models. We showed that MCPIP1 directly regulates the expression of miR-376c-3p via direct cleavage of the corresponding precursor miRNA. The pro-proliferative activity of miR-223-3p, miR-376c-3p, and miR-139-5p was experimentally confirmed in SCC-like keratinocytes. Bioinformatic prediction of the mRNA targets of the DE-miRNAs revealed 416 genes as putative targets of the 18 upregulated miRNAs and 425 genes as putative targets of the 15 downregulated miRNAs. Further analyses revealed the murine interactions that are conserved in humans. Functional analysis indicated that during the development of cutaneous SCC, the most important pathways/processes mediated by the miRNA‒mRNA MCPIP1-dependent network are the regulation of inflammatory processes, epithelial cell proliferation, Wnt signaling, and miRNA transcription.

CONCLUSIONS

Loss of MCPIP1 modulates the expression profiles of 33 miRNAs in chemically induced Mcpip1eKO papillomas, and these changes directly affect the miRNA‒mRNA network and the modulation of pathways and processes related to carcinogenesis.

Hsa_circ_0086414/transducer of ERBB2 (TOB2) axis-driven lipid elimination and tumor suppression in clear cell renal cell cancer via perilipin 3

BACKGROUND

Renal cell cancer (RCC) is characterized by abnormal lipid accumulation. However, the specific mechanism by which such lipid deposition is eliminated remains unclear. Circular RNAs (circRNAs) widely regulate various biological processes, but the effect of circRNAs on lipid metabolism in cancers, especially clear cell renal cell carcinoma (ccRCC), remains poorly understood.

METHODS

The downregulated circRNA, hsa_circ_0086414, was identified from high-throughput RNA-sequencing data of human ccRCC and pair-matched normal tissues. The target relationship between circRNA_0086414 and miR-661, and the transducer of ERBB2 (TOB2) was predicted using publicly available software programs and verified by luciferase reporter assays. The clinical prognostic value of TOB2 was evaluated by bioinformatic analysis. The expression levels of circRNA_0086414, miR-661, TOB2, and perilipin 3 (PLIN3) were measured by quantitative reverse-transcription polymerase chain reaction or western blot analysis. Cell Counting Kit-8, transwell assays, and xenograft models were employed to assess the biological behaviors of the hsa_circ_0086414/TOB2 axis. Oil Red staining and triglyceride assay was conducted to assess lipid deposition.

RESULTS

Herein, we identified a downregulated circRNA, hsa_circ_0086414. Functionally, the restored hsa_circ_0086414 inhibited ccRCC proliferation, metastasis, and lipid accumulation in vitro and in vivo. Furthermore, the downregulated TOB2 predicted adverse prognosis and promoted cancer progression and lipid deposition in ccRCC. Mechanically, the binding of hsa_circ_0086414 to miR-661, as a miRNA sponge, upregulates the expression of TOB2, wielding an anti-oncogene effect. Importantly, the restored hsa_circ_0086414/TOB2 axis significantly contributed to the elimination of lipid deposition by inhibiting the lipid metabolism regulator PLIN3 in ccRCC cells.

CONCLUSIONS

Our data highlight the importance of the hsa_circ_0086414/TOB2/PLIN3 axis as a tumor suppressor and lipid eliminator in ccRCC. The positive modulation of the hsa_circ_0086414/TOB2 axis might lead to the development of novel treatment strategies for ccRCC.

Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.

Basonuclin-2 regulates extracellular matrix production and degradation

Epithelial-mesenchymal transition is essential for tissue patterning and organization. It involves both regulation of cell motility and alterations in the composition and organization of the ECM-a complex environment of proteoglycans and fibrous proteins essential for tissue homeostasis, signaling in response to chemical and biomechanical stimuli, and is often dysregulated under conditions such as cancer, fibrosis, and chronic wounds. Here, we demonstrate that basonuclin-2 (BNC2), a mesenchymal-expressed gene, that is, strongly associated with cancer and developmental defects across genome-wide association studies, is a novel regulator of ECM composition and degradation. We find that at endogenous levels, BNC2 controls the expression of specific collagens, matrix metalloproteases, and other matrisomal components in breast cancer cells, and in fibroblasts that are primarily responsible for the production and processing of the ECM within the tumour microenvironment. In so doing, BNC2 modulates the motile and invasive properties of cancers, which likely explains the association of high BNC2 expression with increasing cancer grade and poor patient prognosis.

Investigation of circular RNA transcriptome in obesity-related endometrial cancer

The present study has investigated the circular RNA (circRNA) transcriptome of twenty obese and postmenopausal women, recruited in Australia, with endometrial cancer (EC). This paper expands on previous findings which evaluated the circRNA transcriptome of a similar cohort of six women recruited in the United States of America. EC is the most common gynaecological malignancy and the fifth most common cancer in women worldwide with obesity as one of its major risk factors. CircRNAs, a class of non-coding RNAs, are involved in many human diseases including cancer. As such the objective of this study was to investigate the circRNA transcriptome of these twenty women and identify circRNAs of interest. We obtained paired samples (EC and adjacent normal tissue) from the cohort of twenty women. Samples were subjected to ribosomal RNA depletion and sequencing performed using Illumina sequencing technology. CircRNAs were identified through CIRI2 and CIRCexplorer2 and common circRNAs extracted for differential expression with edgeR which met the criteria of counts per million > 0.1 and expressed in ≥ 10. We found that the overall abundance of circRNAs was lower in EC compared to adjacent non-cancerous endometrial tissue. We also identified hotspot genes, genes expressing over 10 distinct circRNA isoforms. There were 82 hotspot genes in normal tissue and 23 hotspot genes in EC. There were 174 significantly differentially expressed circRNAs, of which 172 were down-regulated and 2 were up-regulated in EC. The circRNAs identified from this study may act as diagnostic or prognostic biomarkers for EC in obese women. While the circRNA transcriptome of obesity-related EC has been investigated further work is required to determine their functional significance.

A new intronic quantitative PCR method led to the discovery of transformation from human ascites to murine malignancy in a mouse model

Purpose

To establish a fast and accurate detection method for interspecies contaminations in the patient-derived xenograft (PDX) models and cell lines, and to elucidate possible mechanisms if interspecies oncogenic transformation is detected.

Methods

A fast and highly sensitive intronic qPCR method detecting Gapdh intronic genomic copies was developed to quantify if cells were human or murine or a mixture. By this method, we documented that murine stromal cells were abundant in the PDXs; we also authenticated our cell lines to be human or murine.

Results

In one mouse model, GA0825-PDX transformed murine stromal cells into a malignant tumorigenic murine P0825 cell line. We traced the timeline of this transformation and discovered three subpopulations descended from the same GA0825-PDX model: epithelium-like human H0825, fibroblast-like murine M0825, and main passaged murine P0825 displayed differences in tumorigenic capability in vivo. P0825 was the most aggressive and H0825 was weakly tumorigenic. Immunofluorescence (IF) staining revealed that P0825 cells highly expressed several oncogenic and cancer stem cell markers. Whole exosome sequencing (WES) analysis revealed that TP53 mutation in the human ascites IP116-generated GA0825-PDX may have played a role in the human-to-murine oncogenic transformation.

Conclusion

This intronic qPCR is able to quantify human/mouse genomic copies with high sensitivity and within a time frame of a few hours. We are the first to use intronic genomic qPCR for authentication and quantification of biosamples. Human ascites transformed murine stroma into malignancy in a PDX model.

A genome-wide cross-cancer meta-analysis highlights the shared genetic links of five solid cancers

Breast, ovarian, prostate, lung, and head/neck cancers are five solid cancers with complex interrelationships. However, the shared genetic factors of the five cancers were often revealed either by the combination of individual genome-wide association study (GWAS) approach or by the fixed-effect model-based meta-analysis approach with practically impossible assumptions. Here, we presented a random-effect model-based cross-cancer meta-analysis framework for identifying the genetic variants jointly influencing the five solid cancers. A comprehensive genetic correlation analysis (genome-wide, partitioned, and local) approach was performed by using GWAS summary statistics of the five cancers, and we observed three cancer pairs with significant genetic correlation: breast-ovarian cancer (r _g = 0.221, p = 0.0003), breast-lung cancer (r _g = 0.234, p = 7.6 × 10^-6), and lung-head/neck cancer (r _g = 0.652, p = 0.010). Furthermore, a random-effect model-based cross-trait meta-analysis was conducted for each significant cancer pair, and we found 27 shared genetic loci between breast and ovarian cancers, 18 loci between breast and lung cancers, and three loci between lung and head/neck cancers. Functional analysis indicates that the shared genes are enriched in human T-cell leukemia virus 1 infection (HTLV-1) and antigen processing and presentation (APP) pathways. Our study investigates the shared genetic links across five solid cancers and will help to reveal their potential molecular mechanisms.

Fusion of the HMGA2 and BNC2 Genes in Uterine Leiomyoma With t(9;12)(p22;q14)

BACKGROUND/AIM

The translocation t(9;12) (p22;q14~15) has been reported in lipomas, pleomorphic adenomas, a myolipoma, two chondroid hamartomas, and two uterine leiomyomas. In lipomas and pleomorphic adenomas, the translocation fuses HMGA2 (12q14) with the NFIB gene from 9p22; in myolipoma, it fuses HMGA2 with C9orf92 from 9p22; and in chondroid hamartomas, fluorescence in situ hybridization (FISH) investigations showed the chromosomal aberration to cause intragenic rearrangement of HMGA2. The translocation's molecular consequence in a uterine leiomyoma is described here.

MATERIALS AND METHODS

A typical leiomyoma was investigated using banding cytogenetics, FISH, RNA sequencing, reverse transcription polymerase chain reaction and Sanger sequencing.

RESULTS

A single translocation, t(9;12)(p22;q14) leading to an HMGA2::BNC2 chimera, was found in tumor cells. A sequence of the untranslated part of exon 5 of HMGA2 (nucleotide 1035 in the NCBI reference sequence NM_003483.4) had fused with a sequence from the untranslated part of exon 7 of BNC2 from 9p22 (nucleotide 9284 in reference sequence NM_017637.6).

CONCLUSION

At the molecular level, the t(9;12)(p22;q14~15) found in several benign tumors appears to be heterogeneous fusing HMGA2 with either BNC2, C9orf92 or NFIB which all three map close to one another within a 3 Mbp region in 9p22. Because the fusion point in HMGA2 in the present tumor lays downstream from the first Let-7 miRNA consensus binding site, we conclude that deletion of the first Let-7 miRNA binding site is not important for the transcriptional upregulation of HMGA2 caused by the genomic rearrangement.

Epigenetic analysis in placentas from sickle cell disease patients reveals a hypermethylation profile

Pregnancy in Sickle Cell Disease (SCD) women is associated to increased risk of clinical and obstetrical complications. Placentas from SCD pregnancies can present increased abnormal findings, which may lead to placental insufficiency, favoring adverse perinatal outcome. These placental abnormalities are well known and reported, however little is known about the molecular mechanisms, such as epigenetics. Thus, our aim was to evaluate the DNA methylation profile in placentas from women with SCD (HbSS and HbSC genotypes), compared to uncomplicated controls (HbAA). We included in this study 11 pregnant women with HbSS, 11 with HbSC and 21 with HbAA genotypes. Illumina Methylation EPIC BeadChip was used to assess the whole placental DNA methylation. Pyrosequencing was used for array data validation and qRT-PCR was applied for gene expression analysis. Our results showed high frequency of hypermethylated CpGs sites in HbSS and HbSC groups with 73.5% and 76.2% respectively, when compared with the control group. Differentially methylated regions (DMRs) also showed an increased hypermethylation status for the HbSS (89%) and HbSC (86%) groups, when compared with the control group methylation data. DMRs were selected for methylation validation (4 DMRs-HbSS and 3 DMRs the HbSC groups) and after analyses three were validated in the HbSS group, and none in the HbSC group. The gene expression analysis showed differential expression for the PTGFR (-2.97-fold) and GPR56 (3.0-fold) genes in the HbSS group, and for the SPOCK1 (-2.40-fold) and ADCY4 (1.80-fold) genes in the HbSC group. Taken together, these data strongly suggest that SCD (HbSS and HbSC genotypes) can alter placental DNA methylation and lead to gene expression changes. These changes possibly contribute to abnormal placental development and could impact in the clinical course, especially for the fetus, possibly leading to increased risk of abortion, fetal growth restriction (FGR), stillbirth, small for gestational age newborns and prematurity.

Development of a CAFs-related gene signature to predict survival and drug response in bladder cancer

As one of important components of tumor microenvironment, CAFs (cancer-associated fibroblasts) play a vital role in the development and metastasis of bladder cancer. The present study aimed to develop a CAFs-related gene signature to predict the prognosis of patients and the response to chemotherapy and immunotherapy based on research of multidatabase. Expression data and clinical information were obtained from TCGA and GEO databases. Different bioinformatic and statistical methods were combined to construct the robust CAFs-related gene signature for prognosis. The model was explored from four aspects: single-cell source, immune infiltration, correlation with cancer-related genes and pathways, and prediction of drug response. After screening, five genes (BNC2, LAMA2, MFAP5, NID1, and OLFML1) related to CAFs were used for constructing the signature to divide patients into high- and low-risk groups. Patients in low-risk group had better prognosis and multidatabase analysis confirmed the predictive value. The five genes were mainly expressed by fibroblasts and involved in regulation of pathways related with glycolysis, hypoxia, and epithelial-mesenchymal transition (EMT). BNC2, LAMA2, and NID1 were strongly associated with drug sensitivity. Moreover, the immunological status was different between high- and low-risk groups. High-risk patients had poor response to chemotherapy or immunotherapy. The CAFs-related gene signature might help to optimize risk stratification and provide a new insight in individual treatment for bladder cancer.

Deep neural network improves the estimation of polygenic risk scores for breast cancer

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

Functional Analysis and Fine Mapping of the 9p22.2 Ovarian Cancer Susceptibility Locus

Genome-wide association studies have identified 40 ovarian cancer risk loci. However, the mechanisms underlying these associations remain elusive. In this study, we conducted a two-pronged approach to identify candidate causal SNPs and assess underlying biological mechanisms at chromosome 9p22.2, the first and most statistically significant associated locus for ovarian cancer susceptibility. Three transcriptional regulatory elements with allele-specific effects and a scaffold/matrix attachment region were characterized and, through physical DNA interactions, BNC2 was established as the most likely target gene. We determined the consensus binding sequence for BNC2 in vitro, verified its enrichment in BNC2 ChIP-seq regions, and validated a set of its downstream target genes. Fine-mapping by dense regional genotyping in over 15,000 ovarian cancer cases and 30,000 controls identified SNPs in the scaffold/matrix attachment region as among the most likely causal variants. This study reveals a comprehensive regulatory landscape at 9p22.2 and proposes a likely mechanism of susceptibility to ovarian cancer. SIGNIFICANCE: Mapping the 9p22.2 ovarian cancer risk locus identifies BNC2 as an ovarian cancer risk gene.See related commentary by Choi and Brown, p. 439.

Exploring the Role of Fallopian Ciliated Cells in the Pathogenesis of High-Grade Serous Ovarian Cancer

High-grade serous epithelial ovarian cancer (HGSOC) is the fifth leading cause of cancer death in women and the first among gynecological malignancies. Despite an initial response to standard chemotherapy, most HGSOC patients relapse. To improve treatment options, we must continue investigating tumor biology. Tumor characteristics (e.g., risk factors and epidemiology) are valuable clues to accomplish this task. The two most frequent risk factors for HGSOC are the lifetime number of ovulations, which is associated with increased oxidative stress in the pelvic area caused by ovulation fluid, and a positive family history due to genetic factors. In the attempt to identify novel genetic factors (i.e., genes) associated with HGSOC, we observed that several genes in linkage with HGSOC are expressed in the ciliated cells of the fallopian tube. This finding made us hypothesize that ciliated cells, despite not being the cell of origin for HGSOC, may take part in HGSOC tumor initiation. Specifically, malfunction of the ciliary beat impairs the laminar fluid flow above the fallopian tube epithelia, thus likely reducing the clearance of oxidative stress caused by follicular fluid. Herein, we review the up-to-date findings dealing with HGSOC predisposition with the hypothesis that fallopian ciliated cells take part in HGSOC onset. Finally, we review the up-to-date literature concerning genes that are located in genomic loci associated with epithelial ovarian cancer (EOC) predisposition that are expressed by the fallopian ciliated cells.

Human basonuclin 2 up-regulates a cascade set of interferon-stimulated genes with anti-cancerous properties in a lung cancer model

BACKGROUND

Human basonuclin 2 (BNC2) acts as a tumor suppressor in multiple cancers in an as yet unidentified manner. The role and expression of the BNC2 gene in lung cancer has not yet been investigated.

METHODS

BNC2 expression was studied in the A549 and BEAS-2B cell lines, as well as in lung cancer tissue. Illumina array analysis and a viability assay were used to study the effects of transient transfection of BNC2 in A549 cells. Ingenuity pathway analysis and g:Profiler were applied to identify affected pathways and networks. RT-qPCR was used to validate the array results.

RESULTS

We showed the reduced mRNA expression of BNC2 in non-small cell lung cancer tissue and lung cancer cell line A549 compared to non-cancerous lung tissue and BEAS-2B cells, respectively. Further array analysis demonstrated that the transfection of BNC2 into A549 cells resulted in the increased expression of 139 genes and the down-regulation of 13 genes. Pathway analysis revealed that half of the up-regulated genes were from the interferon/signal transducer and activator of transcription signaling pathways. The differential expression of selected sets of genes, including interferon-stimulated and tumor suppressor genes of the XAF1 and OAS families, was confirmed by RT-qPCR. In addition, we showed that the over-expression of BNC2 inhibited the proliferation of A549 cells.

CONCLUSION

Our data suggest that human BNC2 is an activator of a subset of IFN-regulated genes and might thereby act as a tumor suppressor.