Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer

Exploring TSGA10 Function: A Crosstalk or Controlling Mechanism in the Signaling Pathway of Carcinogenesis?

Cancer-specific antigens have been a significant area of focus in cancer treatment since their discovery in the mid-twentieth century. Cancer germline antigens are a class of antigens specifically overexpressed in germline tissues and cancer cells. Among these, TSGA10 (testis-specific gene antigen 10) is of great interest because of its crucial impact on cancer progression. Early studies explored TSGA10 expression in a variety of cancer types. More recent studies revealed that TSGA10 can suppress tumor progression by blocking cancer cell metabolism, angiogenesis, and metastasis. An open question regarding the TSGA10 is why cancer cells must express a protein that prevents their progression. To answer this question, we conducted a comprehensive review to engage the TSGA10 in the context of the current understanding of "malignant transformation". This review demonstrated that TSGA10 expression level in cancer cells depends on the cancer stage across malignant transformation. In addition, we evaluated how TSGA10 expression can prevent the "cancer hallmarks". Given this information, TSGA10 can be of great interest in developing effective targeted anti-cancer therapies.

Nucleosome reorganisation in breast cancer tissues

BACKGROUND

Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes.

RESULTS

We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X.

CONCLUSIONS

Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.

Noxa inhibits oncogenesis through ZNF519 in gastric cancer and is suppressed by hsa-miR-200b-3p

While Phorbol-12-myristate-13-acetate-induced protein 1 (Noxa/PMAIP1) assumes a pivotal role in numerous tumors, its clinical implications and underlying mechanisms of gastric cancer (GC) are yet enigmatic. In this investigation, our primary objective was to scrutinize the clinical relevance and potential mechanisms of Noxa in gastric cancer. Immunohistochemical analysis was conducted on tissue microarrays comprising samples from a meticulously characterized cohort of 84 gastric cancer patients, accompanied by follow-up data, to assess the expression of Noxa. Additionally, Noxa expression levels in gastric cancer clinical samples and cell lines were measured through quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analysis. The effect of Noxa expression on the prognosis of patients with gastric cancer was evaluated using Kaplan-Meier survival. Further insight into the role of Noxa in driving gastric cancer progression was gained through an array of experimental techniques, including cell viability assays (CCK8), plate cloning assays, transwell assays, scratch assays, and real-time cell analysis (RTCA). Potential upstream microRNAs (miRNAs) that might modulate Noxa were identified through rigorous bioinformatics analysis, substantiated by luciferase reporter assays and Western blot experiments. Additionally, we utilized RNA sequencing, qRT-PCR, and Western blot to identify proteins binding to Noxa and potential downstream target. Finally, we utilized BALB/c nude mice to explore the role of Noxa in vivo. Our investigation unveiled a marked downregulation of Noxa expression in gastric cancer and underscored its significance as a pivotal prognostic factor influencing overall survival (OS). Noxa overexpression exerted a substantial inhibitory effect on the proliferation, migration and invasion of GC cells. Bioinformatic analysis and dual luciferase reporter assays unveiled the capacity of hsa-miR-200b-3p to interact with the 3'-UTR of Noxa mRNA, thereby orchestrating a downregulation of Noxa expression in vitro, consequently promoting tumor progression in GC. Our transcriptome analysis, coupled with mechanistic validation, elucidated a role for Noxa in modulating the expression of ZNF519 in the Mitophagy-animal pathway. The depletion of ZNF519 effectively reversed the oncogenic attributes induced by Noxa. Upregulation of Noxa expression suppressed the tumorigenesis of GC in vivo. The current investigation sheds light on the pivotal role of the hsa-miR-200b-3p/Noxa/ZNF519 axis in elucidating the pathogenesis of gastric cancer, offering a promising avenue for targeted therapeutic interventions in the management of this challenging malignancy.

One-pot method for preparing DNA, RNA, and protein for multiomics analysis

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

Multi-omics reveal neuroprotection of Acer truncatum Bunge Seed extract on hypoxic-ischemia encephalopathy rats under high-altitude

Hypoxic-ischemic encephalopathy (HIE) at high-altitudes leads to neonatal mortality and long-term neurological complications without effective treatment. Acer truncatum Bunge Seed extract (ASO) is reported to have effect on cognitive improvement, but its molecular mechanisms on HIE are unclear. In this study, ASO administration contributed to reduced neuronal cell edema and improved motor ability in HIE rats at a simulated 4500-meter altitude. Transcriptomics and WGCNA analysis showed genes associated with lipid biosynthesis, redox homeostasis, neuronal growth, and synaptic plasticity regulated in the ASO group. Targeted and untargeted-lipidomics revealed decreased free fatty acids and increased phospholipids with favorable ω-3/ω-6/ω-9 fatty acid ratios, as well as reduced oxidized glycerophospholipids (OxGPs) in the ASO group. Combining multi-omics analysis demonstrated FA to FA-CoA, phospholipids metabolism, and lipid peroxidation were regulated by ASO treatment. Our results illuminated preliminary metabolism mechanism of ASO ingesting in rats, implying ASO administration as potential intervention strategy for HIE under high-altitude.

The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity

Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients. Boolean implication networks were used to construct multimodal networks using patient DNA copy number variation, mRNA, and protein expression profiles. T statistics of differential gene/protein expression in tumors versus non-cancerous adjacent tissues, dependency scores in in vitro CRISPR-Cas9/RNA interference (RNAi) screening of human NSCLC cell lines, and hazard ratios in univariate Cox modeling of the Cancer Genome Atlas (TCGA) NSCLC patients were correlated with graph theory centrality metrics. Hub genes in multi-omics networks involving gene/protein expression were associated with oncogenic, proliferative potentials and poor patient survival outcomes (p < 0.05, Pearson's correlation). Immunotherapy targets PD1, PDL1, CTLA4, and CD27 were ranked as top hub genes within the 10th percentile in most constructed multi-omics networks. BUB3, DNM1L, EIF2S1, KPNB1, NMT1, PGAM1, and STRAP were discovered as important hub genes in NSCLC proliferation with oncogenic potential. These results support the importance of hub genes in NSCLC tumorigenesis, proliferation, and prognosis, with implications in prioritizing therapeutic targets to improve patient survival outcomes.

Silencing eL31 suppresses the progression of colorectal cancer via targeting DEPDC1

Background

Colorectal cancer (CRC) is one of the most commonly diagnosed human malignancies. Ribosomal protein L31 (RPL31, aka eL31) is a component of the 60S large ribosomal subunit, and its expression pattern and functional role in CRC have not been reported.

Methods

Herein, we identified that eL31 protein level was dramatically increased in CRC tissues through using IHC analysis. More notably, elevated eL31 was associated with larger tumor size and shorter overall survival. Besides, we evaluated the effects of eL31 depletion on CRC cell phenotypes in vitro.

Results

The data indicated that eL31 knockdown restricted CRC cell proliferation, migration and colony formation whilst enhancing cell apoptosis. Importantly, eL31 was also essential for CRC tumor growth in vivo, as demonstrated by impaired tumor growth markers and reduced Ki67 levels in xenografts from eL31-depleted cells. In addition, our evidence indicated that DEP domain containing 1 (DEPDC1) was a potential downstream target of eL31 in regulating CRC. Consistently, DEPDC1 depletion restrained CRC cell proliferation and migration, as well as facilitated cell apoptosis. More interestingly, DEPDC1 depletion could reverse the promotion effects of eL31 elevation on CRC cells.

Conclusions

Identification of eL31’s function in CRC may pave the way for future development of more specific and more effective targeted therapy strategies against CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03663-6.