Integrative analysis of genomic, epigenomic and transcriptomic data identified molecular subtypes of esophageal carcinoma

Identification of genome-wide copy number variation-driven subtypes for the treatment and prognostic prediction of esophageal carcinoma

Background

Esophageal carcinoma (ESCA) is a frequently detected gastrointestinal cancer. Copy number variants (CNVs) have a dramatic impact on the screening, diagnosis and prognostic prediction of cancers. However, the mechanism of action of CNVs on ESCA occurrence and progression remains unclear.

Methods

ESCA samples from The Cancer Genome Atlas (TCGA) were typed by consensus clustering using CNV-associated genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to section gene modules closely related to the two clusters, and sub-networks were constructed as hub genes. In addition, seven prognosis-correlated genes were further screened and retained by multivariate Cox regression analysis to develop a prognostic assessment model. The ssGSEA algorithm assessed energy metabolism levels in patients from different clusters and risk groups. Finally, quantitative real-time PCR (qRT-PCR) and live-dead cell staining verified the expression of genes associated with CNV risk scores.

Results

ESCA was classified into two subtypes based on CNV values. Compared with cluster 1, cluster 2 had significantly higher level of immune score and tumor-associated immune cell infiltration as well as a noticeably better overall survival. The three modules most associated with the two clusters were identified by WGCNA, and a prognostic model with a strong prediction performance was constructed with their genes. Glycolysis, lactate metabolism, fatty acid synthesis, glutathione, methionine, and tryptophan metabolic pathway enrichment scores were remarkably higher in patients in cluster 1 and the high-risk group than in cluster 2 and the low-risk group. Knockdown PIK3C2A promoted ESCA cells apoptosis and inhibited cell vibiality.

Conclusion

The current research maybe provides new understanding for the pathogenesis of ESCA based on CNV, providing an effective guidance for its clinical diagnosis and prognostic evaluation.

Comprehensive pan-cancer analysis of YBX family reveals YBX2 as a potential biomarker in liver cancer

Background

The Y-box-binding proteins (YBX) act as a multifunctional role in tumor progression, metastasis, drug resistance by regulating the transcription and translation process. Nevertheless, their functions in a pan-cancer setting remain unclear.

Methods

This study examined the clinical features expression, prognostic value, mutations, along with methylation patterns of three genes from the YBX family (YBX1, YBX2, and YBX3) in 28 different types of cancer. Data used for analysis were obtained from Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. A novel YBXs score was created using the ssGSEA algorithm for the single sample gene set enrichment analysis. Additionally, we explored the YBXs score's association with the tumor microenvironment (TME), response to various treatments, and drug resistance.

Results

Our analysis revealed that YBX family genes contribute to tumor progression and are indicative of prognosis in diverse cancer types. We determined that the YBXs score correlates significantly with numerous malignant pathways in pan-cancer. Moreover, this score is also linked with multiple immune-related characteristics. The YBXs score proved to be an effective predictor for the efficacy of a range of treatments in various cancers, particularly immunotherapy. To summarize, the involvement of YBX family genes is vital in pan-cancer and exhibits a significant association with TME. An elevated YBXs score indicates an immune-activated TME and responsiveness to diverse therapies, highlighting its potential as a biomarker in individuals with tumors. Finally, experimental validations were conducted to explore that YBX2 might be a potential biomarker in liver cancer.

Conclusion

The creation of YBXs score in our study offered new insights into further studies. Besides, YBX2 was found as a potential therapeutic target, significantly contributing to the improvement of HCC diagnosis and treatment strategies.

Potent molecular-targeted therapies for advanced esophageal squamous cell carcinoma

Esophageal cancer (EC) remains a public health concern with a high mortality and disease burden worldwide. Esophageal squamous cell carcinoma (ESCC) is a predominant histological subtype of EC that has unique etiology, molecular profiles, and clinicopathological features. Although systemic chemotherapy, including cytotoxic agents and immune checkpoint inhibitors, is the main therapeutic option for recurrent or metastatic ESCC patients, the clinical benefits are limited with poor prognosis. Personalized molecular-targeted therapies have been hampered due to the lack of robust treatment efficacy in clinical trials. Therefore, there is an urgent need to develop effective therapeutic strategies. In this review, we summarize the molecular profiles of ESCC based on the findings of pivotal comprehensive molecular analyses, highlighting potent therapeutic targets for establishing future precision medicine for ESCC patients, with the most recent results of clinical trials.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.