Identification of novel prognostic genes of triple-negative breast cancer using meta-analysis and weighted gene co-expressed network analysis

Filamin A in triple negative breast cancer

Triple-negative breast cancer is a rare but highly heterogeneous breast cancer subtype with a limited choice of specific treatments. Chemotherapy remains the only efficient treatment, but its side effects and the development of resistance consolidate the urgent need to discover new targets. In TNBC, filamin A expression correlates to grade and TNM stage. Accordingly, this protein could constitute a new target for this BC subtype. Even if most of the data indicates its direct involvement in cancer progression, some contrasting results underline the need to deepen the studies. To elucidate a possible function of this protein as a TNBC marker, we summarized the main characteristic of filamin A and its involvement in physiological and pathological processes such as cancer. Lastly, we scrutinized its actions in triple-negative breast cancer and highlighted the need to increase the number of studies useful to better clarify the role of this versatile protein as a marker and target in TNBC, alone or in "collaboration" with other proteins with a relevant role in this BC subgroup.

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

XMR: an explainable multimodal neural network for drug response prediction

Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the "black box" characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice. Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs' structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug's molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer. Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer. Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.

Exosomal delivery of 7SK long non-coding RNA suppresses viability, proliferation, aggressiveness and tumorigenicity in triple negative breast cancer cells

AIMS

RN7SK (7SK), a highly conserved non-coding RNA, serves as a transcription regulator via interaction with a few proteins. Despite increasing evidences which support the cancer-promoting roles of 7SK-interacting proteins, limited reports address the direct link between 7SK and cancer. To test the hypothetic suppression of cancer by overexpression of 7SK, the effects of exosomal 7SK delivery on cancer phenotypes were studied.

MATERIALS AND METHODS

Exosomes derived from human mesenchymal stem cells were loaded with 7SK (Exo-7SK). MDA-MB-231, triple negative breast cancer (TNBC), cell line was treated with Exo-7sk. Expression levels of 7SK were evaluated by qPCR. Cell viability was assessed via MTT and Annexin V/PI assays as well as qPCR assessment of apoptosis-regulating genes. Cell proliferation was evaluated by growth curve analysis, colony formation and cell cycle assays. Aggressiveness of TNBCs was evaluated via transwell migration and invasion assays and qPCR assessment of genes regulating epithelial to mesenchymal transition (EMT). Moreover, tumor formation ability was assessed using a nude mice xenograft model.

KEY FINDINGS

Treatment of MDA-MB-231 cells with Exo-7SK resulted in efficient overexpression of 7SK; reduced viability; altered transcription levels of apoptosis-regulating genes; reduced proliferation; reduced migration and invasion; altered transcription of EMT-regulating genes; and reduced in vivo tumor formation ability. Finally, Exo-7SK reduced mRNA levels of HMGA1, a 7SK interacting protein with master gene regulatory and cancer promoting roles, and its bioinformatically-selected cancer promoting target genes.

SIGNIFICANCE

Altogether, as a proof of the concept, our findings suggest that exosomal delivery of 7SK may suppress cancer phenotypes via downregulation of HMGA1.

Proteomic profiling and ROC analysis identify CD151 and ELAVL1 as potential therapy response markers for the antiviral drug in resistant TNBC

Triple-negative breast cancer is high heterogeneous, aggressive, and metastatic with poor prognosis. Despite of advances in targeted therapies, TNBC has been reported to cause high morbidity and mortality. A rare subpopulation within the tumor microenvironment organized into a hierarchy of cancer stem cells is responsible for therapy resistance and tumor recurrence. Repurposing of antiviral drugs for cancer treatment is gaining momentum due to reduced cost, labour, and research time, but limited due to lack of prognostic, and predictive markers. The present study investigates proteomic profiling and ROC analysis to identify CD151 and ELAVL1 as potential therapy response markers for the antiviral drug 2-thio-6-azauridine (TAU) in resistant TNBC. The stemness of MDA-MB 231 and MDA-MD 468 adherent cells was enriched by culturing them under non-adherent and non-differentiation conditions. Then, CD151⁺ subpopulation was isolated and characterized for the enrichment of stemness. This study found that CD151 has overexpressed in stemness enriched subpopulations, and also showed CD44 high and CD24 low expression along with stem cell-related transcription factors octamer-binding transcription factor 4 (OCT4) and Sex determining Y-box 2 (SOX2). This study also found that TAU induced significant cytotoxicity and genotoxicity in the CD151⁺TNBC subpopulation and inhibited their proliferation by inducing DNA damage, cell cycle arrest at the G₂M phase, and apoptosis. Further, a proteomic profiling study showed that the expression of CD151 along with ELAVL1, an RNA-binding protein, was significantly reduced with TAU treatment. KM plotter showed correlation of CD151 and ELAVL1 gene expression with a poor prognosis of TNBC. ROC analysis predicted and validated CD151 and ELAVL1 as best therapy response marker for TAU in TNBC. These findings provide new insight into repurposing antiviral drug TAU for treatment of metastatic and drug resistant TNBC.

Transcriptomic profiles-based approach to decode the role of miR-122 in triple negative breast cancer

miR-122 has been considered both as tumor suppressor miRNA and oncomiR in breast tumor phenotypes. However, the role of miR-122 in triple-negative breast cancer (TNBC) is still unknown. In this study, the clinical value of miR-122 was used to describe the transcriptomic landscape of TNBC tumors obtained from The Cancer Genome Atlas database. Low expression levels of miR-122 were associated with poor overall survival (OS) of TNBC patients than those with higher expression levels of miR-122. We identified gene expression profiles in TNBC tumors expressed lower or higher miR-122. Gene coexpression networks analysis revealed gene modules and hub genes specific to TNBC tumors with low or high miR-122 levels. Gene ontology and KEGG pathways analysis revealed that gene modules in TNBC with gain of miR-122 were related to cell cycle and DNA repair, while in TNBC with loss of miR-122 were enriched in cell cycle, proliferation, apoptosis and activation of cell migration and invasion. The expression of hub genes distinguished TNBC tumors with gain or loss of miR-122 from normal breast tissues. Furthermore, high levels of hub genes were associated with better OS in TNBC patients. Interestingly, the gene coexpression network related to loss of miR-122 were enriched with target genes of miR-122, but this did not observed in those with gain of miR-122. Target genes of miR-122 are oncogenes mainly associated with cell differentiation-related processes. Finally, 75 genes were identified exclusively associated to loss of miR-122, which are also implicated in cell differentiation. In conclusion, miR-122 could act as tumor suppressor by controlling oncogenes in TNBC.

CTPS1 inhibition suppresses proliferation and migration in colorectal cancer cells

Colorectal cancer (CRC) is now the third most prevalent tumor and one of the deadliest cancers worldwide, with an increasing prevalence every year. Therefore, we urgently need to understand the mechanisms regulating the progression of colorectal cancer and find potential diagnostic biomarkers. In this study, we performed an analysis using the TCGA and GEO databases to find a molecular biomarker for the diagnosis of CRC, namely CTPS1. The results of this analysis revealed that CTPS1 could promote tumor proliferation and metastasis. Furthermore, bioinformatics analysis revealed that CTPS1 promoted CRC progression through cell cycle and p53 pathways. Further investigation demonstrated that CTPS1 might be involved in the regulation of CCNB1, RRM2, GTSE1, CDK2 and CHEK2 genes. Moreover, PCR confirmed that CTPS1 regulated GTSE1 and CDK2 molecules. Then, western blot was used to verify that CTPS1 promoted the expression of GTSE1 and CDK2 by inhibiting the expression of p53. In summary, we identified an important diagnostic biomarker for CRC, namely CTPS1, and its importance was validated at the cellular level. These results suggest that CTPS1 could serve as a candidate biomarker for CRC and CTPS1 inhibitors may be a potential treatment for CRC.

PADI1 and Its Co-Expressed Gene Signature Unveil Colorectal Cancer Prognosis and Immunotherapy Efficacy

Peptidyl arginine deiminase 1 (PADI1) catalyzes protein citrullination and has a role in regulating immune responses. The tumor immune microenvironment has been reported to be important in colorectal cancer (CRC), which was correlated with the ability of CRC patients to benefit from immunotherapy. However, there is a lack of molecular markers for matching CRC immunotherapy. Previously, single-gene risk models have only considered the effect of individual genes on intrinsic tumor properties, ignoring the role of genes and their co-expressed genes as a whole. In this study, we analyzed the differential expression of PADI1 in colorectal cancer (CRC). We found that PADI1 was highly expressed in CRC. Subgroup survival analysis revealed a prognostic survival difference for PADI1 in CRC patients aged less than 65 years, male, T stage, N0, M0, and stage I-II (p < 0.05). In addition, we analyzed the functions and signaling pathways associated with PADI1 in CRC and found that it was highly enriched in several immune-related functions and pathways. Then, a set of PADI1 co-expressed genes (PCGs) risk-prognosis scores was developed with PADI1 as the core, which could accurately predict the prognosis of CRC (p < 0.05). PCGs risk score can be an independent prognostic factor for CRC. A new set of Norman plot models were developed for clinical characteristics with age, sex, and TNM stage, which can accurately predict CRC 1, 3, and 5 years survival, and calibration curves and decision curve analysis (DCA) validated the accuracy of the models. The risk score assessed the immune microenvironment of CRC and found that the immune score was higher in the low-risk group, and CD4+ T cells, helper T cells, and eosinophils were more infiltrated in the low-risk group (p < 0.05). Immunotherapy efficacy was better in the low-risk group (p < 0.05). The underlying mechanism may be that the high-risk group of PCGs was enriched in some pathways that promote immune escape and immune dysfunction. In conclusion, PCGs may better predict CRC prognosis and immunotherapeutic response.

Interactions between 14-3-3 Proteins and Actin Cytoskeleton and Its Regulation by microRNAs and Long Non-Coding RNAs in Cancer

14-3-3s are a family of structurally similar proteins that bind to phosphoserine or phosphothreonine residues, forming the central signaling hub that coordinates or integrates various cellular functions, thereby controlling many pathways important in cancer, cell motility, cell death, cytoskeletal remodeling, neuro-degenerative disorders and many more. Their targets are present in all cellular compartments, and when they bind to proteins they alter their subcellular localization, stability, and molecular interactions with other proteins. Changes in environmental conditions that result in altered homeostasis trigger the interaction between 14-3-3 and other proteins to retrieve or rescue homeostasis. In circumstances where these regulatory proteins are dysregulated, it leads to pathological conditions. Therefore, deeper understanding is needed on how 14-3-3 proteins bind, and how these proteins are regulated or modified. This will help to detect disease in early stages or design inhibitors to block certain pathways. Recently, more research has been devoted to identifying the role of MicroRNAs, and long non-coding RNAs, which play an important role in regulating gene expression. Although there are many reviews on the role of 14-3-3 proteins in cancer, they do not provide a holistic view of the changes in the cell, which is the focus of this review. The unique feature of the review is that it not only focuses on how the 14-3-3 subunits associate and dissociate with their binding and regulatory proteins, but also includes the role of micro-RNAs and long non-coding RNAs and how they regulate 14-3-3 isoforms. The highlight of the review is that it focuses on the role of 14-3-3, actin, actin binding proteins and Rho GTPases in cancer, and how this complex is important for cell migration and invasion. Finally, the reader is provided with super-resolution high-clarity images of each subunit of the 14-3-3 protein family, further depicting their distribution in HeLa cells to illustrate their interactions in a cancer cell.

Screening and Biological Function Analysis of miRNA and mRNA Related to Lung Adenocarcinoma Based on Bioinformatics Technology

Objective. To screen the differentially expressed miRNAs (DEMs) and the differentially expressed gene mRNAs (DEGs) in lung adenocarcinoma (LUAD) from the TCGA database and to explore the relationship between miRNAs and the prognosis of lung adenocarcinoma and their biological functions. Methods. The RNA-seq and miRNA-seq data of lung adenocarcinoma samples were downloaded from the TCGA database for analysis, and the R program was used to screen for differentially expressed miRNAs and mRNAs. Then, the molecular functions, biological processes, cellular components, and signaling pathways involved in the occurrence and development of LUAD were analyzed using the functional accumulation analysis software of GSEA. The relationship between the integrated differentially expressed RNAs was analyzed by miRcode, TargetScan, and miRTarbase databases, and the miRNA-mRNA network was constructed. Result. A total of 516 differentially expressed miRNAs and 5464 differentially expressed mRNAs were identified in LUAD. The GSEA enrichment analysis showed that miRNAs and mRNAs were mainly enriched in extracellular structure organization, external encapsulating structure organization, extracellular matrix organization, and gated channel activity. They were mainly involved in neuroactive ligand-receptor interaction signaling pathway. Some miRNAs and mRNAs in clustering modules were found to be associated with the prognosis of LUAD. Four targeting networks consisting of 22 miRNAs and 531 mRNAs were constructed. Conclusion. The miRNA and mRNA related to the prognosis of LUAD were screened out, which provided a valuable preliminary basis for the follow-upin-depth clinical research and basic experimental research of LUAD.

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

BACKGROUND

Molecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype.

METHODS

Meta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA).

RESULTS

We identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%.

CONCLUSION

Integrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer

INTRODUCTION

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer.

AREAS COVERED

The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.

EXPERT OPINION

In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.