Integrating multiple omics data for the discovery of potential Beclin-1 interactions in breast cancer

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

BACKGROUND AND OBJECTIVE

Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes.

METHODS

NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers.

RESULTS

The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes.

CONCLUSIONS

The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Prediction and prognostic significance of ALOX12B and PACSIN1 expression in gastric cancer by genome-wide RNA expression and methylation analysis

Background

Stomach adenocarcinoma (STAD) is one of the common gastrointestinal cancers, characterized by late discovery and metastasis. However, research of gene methylation and expression in gastric cancer (GC) metastasis has been quite limited. This study aimed to investigate the altered gene expression patterns between metastasis and non-metastasis samples using high-throughput RNA and methylation profiles from a large number of patients. Another aim was to identify a specific potential metastasis biomarker, with the ability to predict the metastasis possibility and prognosis of patients with STAD.

Methods

In this study, we integrated The Cancer Genome Atlas (TCGA) program STAD datasets, analyzed the RNA expression and DNA methylation data between non-metastasis (M0) and distant metastasis (M1) samples, and evaluated the candidate biomarker in survival and prognosis of GC.

Results

Among all patients enrolled, 329 with M0 and M1 information were positive for RNA analysis, and 353 with M0 and M1 information were positive for methylation analysis. We found 29 upregulated and 200 downregulated genes in RNA level, and 5,046 hypermethylated and 8,563 hypomethylated probes in methylation level. Among these genes, we found high RNA expression level and low DNA methylation level of ALOX12B and PACSIN1 in GC metastasis samples. Patients with high expression of these 2 genes had poor overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS).

Conclusions

The expression levels of ALOX12B and PACSIN1 were higher in the metastasis than non-metastasis group, and participants with high expression of these 2 genes were found to have poor survival. The genes ALOX12B and PACSIN1 are potential biomarkers of metastasis and poor prognosis, especially in early stage GC, and provide additional information for subsequent comprehensive treatment of GC.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Small-Molecule Drug Discovery in Triple Negative Breast Cancer: Current Situation and Future Directions

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, but an effective targeted therapy has not been well-established so far. Considering the lack of effective targets, where do we go next in the current TNBC drug development? A promising intervention for TNBC might lie in de novo small-molecule drugs that precisely target different molecular characteristics of TNBC. However, an ideal single-target drug discovery still faces a huge challenge. Alternatively, other new emerging strategies, such as dual-target drug, drug repurposing, and combination strategies, may provide new insight into the improvement of TNBC therapeutics. In this review, we focus on summarizing the current situation of a series of candidate small-molecule drugs in TNBC therapy, including single-target drugs, dual-target drugs, as well as drug repurposing and combination strategies that will together shed new light on the future directions targeting TNBC vulnerabilities with small-molecule drugs for future therapeutic purposes.

Beclin-1 expression is associated with prognosis in a Bcl-2-dependent manner in non-small cell lung cancer

Beclin-1 and Bcl-2 expression abnormalities have been confirmed in different types of cancer. As important regulators of autophagy and apoptosis, respectively, these molecules serve a complex role in tumorigenesis. However, limited information is currently available regarding the association between Beclin-1 and Bcl-2 in (NSCLC). In the present study, the expression levels of Beclin-1 and Bcl-2 were detected in lung cancer tissues, and their prognostic significance was analyzed for NSCLC. A total of 120 patients with lung cancer who underwent surgical resection were included in the present study. Beclin-1 and Bcl-2 expression was assessed using immunohistochemistry and their associations with the overall survival (OS) in patients with NSCLC was examined. The expression rate of Beclin-1 was significantly lower in NSCLC tissues compared with that in adjacent tissues, whereas the expression rate of Bcl-2 was significantly higher in lung cancer tissues compared with that in adjacent tissues. Additionally, Beclin-1 and Bcl-2 protein expression was strongly associated (P<0.05) in NSCLC. Patients with NSCLC with low Beclin-1 expression were in more advanced stages, with more lymph node metastasis and more poorly differentiated tumors. Similarly, patients with NSCLC with high Bcl-2 expression were also in a more advanced stage and had more lymph node metastasis. Cox regression analysis revealed that the association between Bcl-2 expression and survival was not significant, while a multivariate analysis revealed that Beclin-1 expression was significantly associated with OS. Notably, Beclin-1 expression was significantly associated with OS only in patients with high Bcl-2 expression. In conclusion, the present data indicated that the autophagy activity is decreased in NSCLC. Beclin-1 expression was downregulated, while Bcl-2 expression was upregulated in NSCLC tissues compared with that in adjacent tissues. Additionally, these two proteins were associated with the occurrence and progression of NSCLC. Beclin-1 may be a promising prognostic marker for patients with NSCLC with high Bcl-2 expression. The present findings provided a more accurate prognostic assessment for patients with NSCLC. Furthermore, they may be used to actively follow-up and promptly treat patients with a poor prognosis, which may benefit a greater number of patients with NSCLC.

Subtype-specific risk models for accurately predicting the prognosis of breast cancer using differentially expressed autophagy-related genes

Emerging evidence suggests that the dysregulation of autophagy-related genes (ARGs) is coupled with the carcinogenesis and progression of breast cancer (BRCA). We constructed three subtype-specific risk models using differentially expressed ARGs. In Luminal, Her-2, and Basal-like BRCA, four- (BIRC5, PARP1, ATG9B, and TP63), three- (ITPR1, CCL2, and GAPDH), and five-gene (PRKN, FOS, BAX, IFNG, and EIF4EBP1) risk models were identified, which all have a receiver operating characteristic > 0.65 in the training and testing dataset. Multivariable Cox analysis showed that those risk models can accurately and independently predict the overall survival of BRCA patients. Comprehensive analysis showed that the 12 identified ARGs were correlated with the overall survival of BRCA patients; six of the ARGs (PARP1, TP63, CCL2, GAPDH, FOS, and EIF4EBP1) were differentially expressed between BRCA and normal breast tissue at the protein level. In addition, the 12 identified ARGs were highly interconnected and displayed high frequency of copy number variation in BRCA samples. Gene set enrichment analysis suggested that the deactivation of the immune system was the important driving force for the progression of Basal-like BRCA. This study demonstrated that the 12 ARG signatures were potential multi-dimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

Construction of a Comprehensive Multiomics Map of Hepatocellular Carcinoma and Screening of Possible Driver Genes

Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC.

Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan–Meier survival curves.

Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value.

Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.

Downregulation of CYB5D2 is associated with breast cancer progression

We report here that CYB5D2 is associated with tumor suppression function in breast cancer (BC). CYB5D2 expression was significantly reduced in tamoxifen resistant MCF7 cells and in MCF7 cell-derived xenografts treated with TAM. CYB5D2 overexpression induced apoptosis in MCF7 cells; CYB5D2 knockdown enhanced MCF7 cell proliferation. Using the TCGA and Curtis datasets within the Oncomine database, CYB5D2 mRNA expression was downregulated in primary BCs vs breast tissues and HER2-positive or triple negative BCs vs estrogen receptor (ER)-positive BCs. Using the TCGA and Metabric datasets (n = 817 and n = 2509) within cBioPortal, 660 and 4891 differentially expressed genes (DEGs) in relation to CYB5D2 were identified. These DEGs were enriched in pathways governing cell cycle progression, progesterone-derived oocyte maturation, oocyte-meiosis, estrogen-mediated S-phase entry, and DNA metabolism. CYB5D2 downregulation decreased overall survival (OS, p = 0.0408). A CYB5D2-derived 21-gene signature was constructed and robustly correlated with OS shortening (p = 5.72e-12), and independently predicted BC deaths (HR = 1.28; 95% CI 1.08–1.52; p = 0.004) once adjusting for known clinical factors. CYB5D2 reductions displayed relationship with mutations in PIK3CA, GATA3, MAP3K1, CDH1, TP53 and RB1. Impressively, 85% (560/659) of TP53 mutations occurred in the 21-gene signature-positive BC. Collectively, we provide the first evidence that CYB5D2 is a candidate tumor suppressor of BC.

Integrative analysis of Paneth cell proteomic and transcriptomic data from intestinal organoids reveals functional processes dependent on autophagy

Paneth cells are key epithelial cells that provide an antimicrobial barrier and maintain integrity of the small-intestinal stem cell niche. Paneth cell abnormalities are unfortunately detrimental to gut health and are often associated with digestive pathologies such as Crohn's disease or infections. Similar alterations are observed in individuals with impaired autophagy, a process that recycles cellular components. The direct effect of autophagy impairment on Paneth cells has not been analysed. To investigate this, we generated a mouse model lacking Atg16l1 specifically in intestinal epithelial cells, making these cells impaired in autophagy. Using three-dimensional intestinal organoids enriched for Paneth cells, we compared the proteomic profiles of wild-type and autophagy-impaired organoids. We used an integrated computational approach combining protein-protein interaction networks, autophagy-targeted proteins and functional information to identify the mechanistic link between autophagy impairment and disrupted pathways. Of the 284 altered proteins, 198 (70%) were more abundant in autophagy-impaired organoids, suggesting reduced protein degradation. Interestingly, these differentially abundant proteins comprised 116 proteins (41%) that are predicted targets of the selective autophagy proteins p62, LC3 and ATG16L1. Our integrative analysis revealed autophagy-mediated mechanisms that degrade key proteins in Paneth cell functions, such as exocytosis, apoptosis and DNA damage repair. Transcriptomic profiling of additional organoids confirmed that 90% of the observed changes upon autophagy alteration have effects at the protein level, not on gene expression. We performed further validation experiments showing differential lysozyme secretion, confirming our computationally inferred downregulation of exocytosis. Our observations could explain how protein-level alterations affect Paneth cell homeostatic functions upon autophagy impairment.This article has an associated First Person interview with the joint first authors of the paper.

What We Learned From Big Data for Autophagy Research

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems.

Stryphnodendron adstringens ("Barbatimão") Leaf Fraction: Chemical Characterization, Antioxidant Activity, and Cytotoxicity Towards Human Breast Cancer Cell Lines

We evaluated the chemical composition, antioxidant activity, and antitumor potential of a fraction that was isolated from Stryphnodendron adstringens (barbatimão) leaf aqueous extract. Fraction is composed by gallic acid, procyanidin dimer B1, and (-)-epicatechin-3-O-gallate and it exhibits antioxidant and cytotoxic activities. Fraction was cytotoxic against two human breast cancer cell lines, ER (+) and MCF-7 and the triple-negative, MDA-MB-435. The sulforhodamine B assay showed that, as compared to normal control cells, the fraction significantly (P < 0.05) decreased cancer cell viability. The morphological alterations noted in the treated cancer cells were cell rounding-up, shrinkage, and nuclear condensation reduction of cell diameter and length. Treatment with fraction increased cancer cell expression of Bax, caspase-9, active caspase-3, caspase-8, LC-3, and beclin-1 and decreased Bcl-2, caspase-3, and pro-caspase-8 expression. Altogether, fraction is cytotoxic to both breast cancer cell lines, induces cell death, and its mechanism of action seems to include the induction of apoptosis. Our data support a positive role of the fraction as a chemopreventive agent for antineoplastic drug development.