Association Study Confirmed Three Breast Cancer-Specific Molecular Subtype-Associated Susceptibility Loci in Chinese Han Women

LINC-PINT suppresses breast cancer cell proliferation and migration via MEIS2/PPP3CC/NF-κB pathway by sponging miR-576-5p

BACKGROUND

Breast cancer (BCa) is the most frequent malignant tumor in women. Long non-coding RNAs (lncRNAs) have been acknowledged to exert critical regulating functions in various cancers. Long intergenic non-protein coding RNA, p53 induced transcript (LINC-PINT) has been reported to be a chemosensitizer and a tumor suppressor in BCa. However, its downstream molecular mechanism contributing to its tumor-suppressing role remains to be explored in BCa.

METHODS

LINC-PINT expression in BCa tissues and cells was measured using quantitative real-time polymerase chain reaction (RT-qPCR). The proliferation of transfected BCa cells was examined by counting kit-8 (CCK-8) and EdU assay. The migrating ability of indicate BCa cells was assessed by wound healing assays. Bioinformatics analysis and mechanism experiments such as RNA immunoprecipitation (RIP), RNA pull down assay, and luciferase reporter assay, were applied to demonstrate the downstream targets of LINC-PINT.

RESULTS

LINC-PINT was downregulated in BCa tissues and cell lines. Overexpression of LINC-PINT suppressed BCa cell proliferation and migration. LINC-PINT could interact with miR-576-5p to upregulate Meis homeobox 2 (MEIS2) that positively regulated protein phosphatase 3 catalytic subunit gamma (PPP3CC) by inactivating the nuclear factor-κB (NF-κB) pathway.

CONCLUSIONS

These findings elucidated the anti-tumor role of LINC-PINT in BCa via the miR-576-5p/MEIS2/PPP3CC/NF-κB axis, which suggested that LINC-PINT might serve as a potential therapeutic target for BCa.

Molecular typing and prognostic risk models for ovarian cancer: a study based on cell differentiation trajectory

Ovarian cancer is a heterogeneous disease with different molecular phenotypes. We performed molecular typing of ovarian cancer using cell differentiation trajectory analysis and proposed a prognostic risk scoring model. Using the copy number variation provided by inferCNV, we identified malignant tumor cells. Then, ovarian cancer samples were divided into four subtypes based on differentiation-related genes (DRGs). There were significant differences in survival rates, clinical features, tumor microenvironment scores, and the expression levels of ICGs among the subtypes. Based on nine DRGs, a prognostic risk score model was generated (AUC at 1 year: 0.749; 3 years: 0.651). Then we obtained a nomogram of the prognostic variable combination, including risk scores and clinicopathological characteristics, and predicted the 1-, 3- and 5-year overall survival. Finally, we explored some issues of immune escape using the established risk model. Our study demonstrates the significant influence of cell differentiation on predicting prognosis in OV patients and provides new insights for OV treatment and potential immunotherapeutic strategies.

PINTology: A short history of the lncRNA LINC-PINT in different diseases

LINC-PINT is a p53-induced long intergenic noncoding transcript that plays a crucial role in many diseases, especially cancer. This long noncoding RNA (lncRNA) gene produces in total 102 (LNCipedia) alternatively spliced variants (LINC-PINT:1 to LINC-PINT:102). The functions of known variants include RNA transcripts, host transcripts for circular RNA (circRNA) generation and as sources for the translation of short peptides. In most human tumors, LINC-PINT is down-regulated where it serves as a tumor suppressor. However, the diversity of its functions in other maladies signifies its general clinical importance. Current LINC-PINT molecular functions include RNA-protein interactions, miRNA sponging and epigenetic modulation with these mechanisms operating in different cellular contexts to exert effects on biological processes ranging from DNA damage responses, cell cycle and growth arrest, senescence, cell migration and invasion, and apoptosis. Genetic polymorphisms in LINC-PINT have also been functionally associated with cancer and other pathologies including the autoimmune diseases pemphigus foliaceus and arthritis. Hence, LINC-PINT shows great potential as a clinical biomarker, especially for the diagnosis and prognosis of cancer. In this review, we explore the current knowledge highlighting the distinctive molecular functions of LINC-PINT in specific cancers and other disease states. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Tumor-Associated Macrophages: Critical Players in Drug Resistance of Breast Cancer

Drug resistance is one of the most critical challenges in breast cancer (BC) treatment. The occurrence and development of drug resistance are closely related to the tumor immune microenvironment (TIME). Tumor-associated macrophages (TAMs), the most important immune cells in TIME, are essential for drug resistance in BC treatment. In this article, we summarize the effects of TAMs on the resistance of various drugs in endocrine therapy, chemotherapy, targeted therapy, and immunotherapy, and their underlying mechanisms. Based on the current overview of the key role of TAMs in drug resistance, we discuss the potential possibility for targeting TAMs to reduce drug resistance in BC treatment, By inhibiting the recruitment of TAMs, depleting the number of TAMs, regulating the polarization of TAMs and enhancing the phagocytosis of TAMs. Evidences in our review support it is important to develop novel therapeutic strategies to target TAMs in BC to overcome the treatment of resistance.

The frequency and clinical significance of DNA polymerase beta (POLβ) expression in breast ductal carcinoma in situ (DCIS)

Background

The prediction of clinical behaviour of breast ductal carcinoma in situ (DCIS) and its progression to invasive disease remains a challenge. Alterations of DNA damage repair mechanisms are associated with invasive breast cancer (BC). This study aims to assess the role of base excision repair (BER) DNA Polymerase Beta (POLβ) in DCIS.

Methods

A cohort of DCIS comprising pure DCIS (n = 776) and DCIS coexisting with invasive BC (n = 239) were prepared as tissue microarrays. POLβ protein expression was assessed using immunohistochemistry and correlated with clinicopathological parameters and patient outcome. Preclinically, we investigated the impact of POLβ depletion on stem cell markers in representative DCIS cell line models.

Results

Reduced POLβ expression was associated with aggressive DCIS features including high nuclear grade, comedo necrosis, larger tumour size, hormonal receptor negativity, HER2 overexpression and high Ki67 index. Combined low nuclear/low cytoplasmic POLβ expression showed the strongest association with the features’ characteristics of aggressive behaviour. There was a gradual reduction in the POLβ expression from normal breast tissue, to DCIS, with the lowest expression observed in the invasive BC. Low POLβ expression was an independent predictor of recurrence in DCIS patients treated with breast conserving surgery (BCS). POLβ knockdown was associated with a significant increase in cell stemness markers including SOX2, NANOG and OCT4 levels in MCF10-DCIS cell lines.

Conclusion

Loss of POLβ in DCIS is associated with aggressive behaviour and it can predict recurrence. POLβ expression in DCIS provides an additional feature for patients’ risk stratification for personalised therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06357-7.

rs12537 Is a Novel Susceptibility SNP Associated With Estrogen Receptor Positive Breast Cancer in Chinese Han Population

Genetic testing is widely used in breast cancer and has identified a lot of susceptibility genes and single nucleotide polymorphisms (SNPs). However, for many SNPs, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are not in place. A recent genome-wide long non-coding RNA (lncRNA) association study in Chinese Han has verified a genetic association between rs12537 and breast cancer. This study is aimed at investigating the association between rs12537 and the phenotype. We collected the clinical information of 5,634 breast cancer patients and 6,308 healthy controls in the early study. And χ2 test was used for the comparison between different groups in genotype. The frequency of genotypic distribution among SNP rs12537 has no statistically significant correlation with family history (p = 0.8945), menopausal status (p = 0.3245) or HER-2 (p = 0.2987), but it is statistically and significantly correlated with ER (p = 0.004006) and PR (p = 0.01379). Most importantly, compared to the healthy control, rs12537 variant is significantly correlated with ER positive patients and the p-value has reached the level of the whole genome (p = 1.66E-08 <5.00E-08). Furthermore, we found rs12537 associated gene MTMR3 was lower expressed in breast cancer tissues but highly methylated. In conclusion, our findings indicate that rs12537 is a novel susceptibility gene in ER positive breast cancer in Chinese Han population and it may influence the methylation of MTMR3.

Expression and prognostic significance of thrombospondin gene family in gastric cancer

Background

Thrombospondins (THBSs) are glycoproteins expressed in the extracellular matrix (ECM) and have critical roles in tumor development and metastasis. However, the diverse expression patterns and prognostic roles of distinct THBS genes in gastric cancer have not been fully elucidated. In the current study, we aimed to investigate the expression patterns of THBSs in gastric cancer (GC) and determine whether they are prognostic markers for this malignancy.

Methods

mRNA expression status and genetic mutations of THBS family members were performed by using ONCOMINE, UCSC Xena browser, GEPIA, and cBioPortal databases. Prognostic values and function enrichment analysis of the members were assessed via Kaplan-Meier plotter and Metascape.

Results

we found that the mRNA expression of THBS1, THBS2, THBS4, and COMP were higher in patients with GC tissues than those in normal gastric mucosa and there was no difference in the mRNA expression of THBS3 between GC and normal tissue. Survival analysis revealed that mRNA levels of THBSs were strongly related to worse OS in GC patients (P<0.05). Overexpression of THBSs indicated poor OS in stage III/IV GC and high expression of THBS1, THBS3, THBS4, and COMP were related to worse OS in stage II GC.

Conclusions

Bioinformatics analysis revealed a better understanding the value of THBS family members in GC and suggest that THBSs might serve as potential prognostic biomarkers for GC.

DNA methylation subtypes for ovarian cancer prognosis

Ovarian cancer is one of three major malignancies of the female reproductive system. DNA methylation (MET) is closely related to ovarian cancer occurrence and development, and as such, elucidation of effective MET subtype markers may guide individualized treatment and improve ovarian cancer prognosis. To identify potential markers, we downloaded a total of 571 ovarian cancer MET samples from The Cancer Genome Atlas (TCGA), and established a Cox proportional hazards model using the MET spectrum and clinical pathological parameters. A total of 250 prognosis-related MET loci were obtained by Cox regression, and six molecular subtypes were screened by consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses. There was a remarkable MET difference between most subtypes. Cluster 2 had the highest MET level and demonstrated the best prognosis, while Clusters 4 and 5 had MET levels significantly lower than those of the other subtypes and demonstrated very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of stage IV samples in Cluster 4 was greater than in the other subtypes. We obtained five CpG loci using a coexpression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. Our cluster analysis showed that prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. These MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish the tumor stage and histological grade in patients with ovarian cancer.

Long non-coding RNA LINC-PINT attenuates paclitaxel resistance in triple-negative breast cancer cells via targeting the RNA-binding protein NONO

The treatment of triple-negative breast cancer (TNBC) relies largely on chemotherapies. However, it is frequent that TNBC patients develop resistance to the chemotherapy drugs. Generation of drug-resistant cell lines facilitates the identification of drug resistance. Here, we established two paclitaxel (PTX)-resistant TNBC cancer cell lines using an intermittent and stepwise method and found that long non-coding RNA long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) was significantly decreased in PTX-resistant cancer cells. Ectopic expression of LINC-PINT sensitized both PTX-resistant TNBC and wild-type TNBC to PTX. Moreover, RNA immunoprecipitation showed that LINC-PINT bound to RNA-binding protein NONO. Overexpression of LINC-PINT resulted in the degradation of NONO in a proteasome-dependent manner and vice versa. Knockdown of NONO with siRNA sensitized TNBC to PTX. We further analyzed the expression level of LINC-PINT and NONO in patient samples via online database and found that LINC-PINT and NONO may function antagonistically in all types of breast cancers. Taken together, our data illustrated a tumor suppressor role of LINC-PINT in sensitizing TNBC to chemotherapies via destabilizing NONO.

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

Subtype-specific associations between breast cancer risk polymorphisms and the survival of early-stage breast cancer

BACKGROUND

Limited evidence suggests that inherited predisposing risk variants might affect the disease outcome. In this study, we analyzed the effect of genome-wide association studies-identified breast cancer-risk single nucleotide polymorphisms on survival of early-stage breast cancer patients in a Chinese population.

METHODS

This retrospective study investigated the relationship between 21 GWAS-identified breast cancer-risk single nucleotide polymorphisms and the outcome of 1177 early stage breast cancer patients with a long median follow-up time of 174 months. Cox proportional hazards regression models were used to estimate the hazard ratios and their 95% confidence intervals. Primary endpoints were breast cancer special survival and overall survival while secondary endpoints were invasive disease free survival and distant disease free survival.

RESULTS

Multivariate survival analysis showed only the rs2046210 GA genotype significantly decreased the risk of recurrence and death for early stage breast cancer. After grouping breast cancer subtypes, significantly reduced survival was associated with the variant alleles of rs9485372 for luminal A and rs4415084 for triple negative breast cancer. Importantly, all three single-nucleotide polymorphisms, rs889312, rs4951011 and rs9485372 had remarkable effects on survival of luminal B EBC, either individually or synergistically. Furthermore, statistically significant multiplicative interactions were found between rs4415084 and age at diagnosis and between rs3803662 and tumor grade.

CONCLUSIONS

Our results demonstrate that breast cancer risk susceptibility loci identified by GWAS may influence the outcome of early stage breast cancer patients' depending on intrinsic tumor subtypes in Chinese women.