Identification of genomic instability related lncRNA signature with prognostic value and its role in cancer immunotherapy in pancreatic cancer

A novel model incorporating chromatin regulatory factors for risk stratification, prognosis prediction, and characterization of the microenvironment in Wilms tumor

BACKGROUND

Wilms tumor, also known as nephroblastoma, a pediatric most-frequent malignant-kidney tumor, may be regulated and influenced by transcriptional and epigenetic mechanisms. Chromatin regulatory factors (CRs) play key roles in epigenetic regulation. The present study aimed to explore the involvement of CRs in the development of nephroblastoma.

METHODS

RNA-sequencing and clinical information of nephroblastoma samples were obtained by downloading data from the TARGET database. The Limma package was utilized to perform differential expression analysis of genes (DEGs) between the tumor group and the control group. A Venn map was used for intersection of differential genes and CRs and to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of DEGs using the clusterProfiler package. LASSO and Cox analyses were used to construct CR-related risk models and were evaluated based on clinical parameters. A receiver operating characteristic curve was employed to assess the diagnostic performance of risk model. Furthermore, we used a single-sample gene set enrichment analysis algorithm for immune cell infiltration analysis. Finally, to confirm the transcriptome expression of pivotal genes in human nephroblastoma cell lines, a quantitative real-time PCR was employed.

RESULTS

Fifteen key CRs were obtained through analysis in nephroblastoma and then the risk model based on 13 important CRs was constructed using the transcriptome data of nephroblastoma. Using the risk model, pediatric nephroblastoma patients were stratified into high- and low-risk groups based on their individual risk scores. The risk score of CRs can predict adverse outcomes in pediatric nephroblastoma, and this gene cluster is closely related to various immunity characteristics of nephroblastoma. Moreover, the nephroblastoma cell line exhibited higher expression levels of prognostic genes (VRK1, ARNTL, RIT1, PRDM6, and TSPY1) compared to the HEK293 T cell line.

CONCLUSIONS

The risk characteristics derived from CRs have tremendous significance in predicting prognosis and guiding clinical classification and intervention strategies for pediatric nephroblastoma.

Comprehensive characterization of adipogenesis-related genes in colorectal cancer for clinical significance and immunogenomic landscape analyses

OBJECTIVE

Colorectal cancer (CRC) is a major global health concern, necessitating the identification of biomarkers and molecular subtypes for improved clinical management. This study aims to evaluate the clinical value of adipogenesis-related genes and molecular subtypes in CRC.

METHODS

A comprehensive analysis of adipogenesis-related genes in CRC was performed using publicly available datasets (TCGA and GEO database) and bioinformatics tools. Unsupervised cluster analysis was employed to identify the molecular subtypes of CRC, while LASSO regression analysis was utilized to develop a risk prognostic model. The immunogenomic patterns and immunotherapy analysis were used to predict patient response to immunotherapy. Furthermore, qPCR analysis was conducted to confirm the expression of the identified key genes in vitro.

RESULTS

Through the analysis of RNAseq data from normal and tumor tissues, we identified 50 differentially expressed genes. Unsupervised cluster analysis identified two subtypes (Cluster A and Cluster B) with significantly different survival outcomes. Cluster A and B displayed differential immune cell compositions and enrichment in specific biological pathways, providing insights into potential therapeutic targets. A risk-scoring model was developed using five ARGs, which successfully classified patients into high and low-risk groups, showing distinct survival outcomes. The model was validated and showed robust predictive performance. High-risk patients exhibited altered immune cell proportions and gene expression patterns compared to low-risk patients. In qPCR validation, four out of the five key genes were consistent with the results of bioinformatics analysis.

CONCLUSION

Overall, the findings of our investigation offer valuable understanding regarding the clinical relevance of ARGs and molecular subtypes in CRC, laying the groundwork for improved precision medicine applications and personalized treatment modalities.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Molecular and metabolic regulation of immunosuppression in metastatic pancreatic ductal adenocarcinoma

Immunosuppression is a hallmark of pancreatic ductal adenocarcinoma (PDAC), contributing to early metastasis and poor patient survival. Compared to the localized tumors, current standard-of-care therapies have failed to improve the survival of patients with metastatic PDAC, that necessecitates exploration of novel therapeutic approaches. While immunotherapies such as immune checkpoint blockade (ICB) and therapeutic vaccines have emerged as promising treatment modalities in certain cancers, limited responses have been achieved in PDAC. Therefore, specific mechanisms regulating the poor response to immunotherapy must be explored. The immunosuppressive microenvironment driven by oncogenic mutations, tumor secretome, non-coding RNAs, and tumor microbiome persists throughout PDAC progression, allowing neoplastic cells to grow locally and metastasize distantly. The metastatic cells escaping the host immune surveillance are unique in molecular, immunological, and metabolic characteristics. Following chemokine and exosomal guidance, these cells metastasize to the organ-specific pre-metastatic niches (PMNs) constituted by local resident cells, stromal fibroblasts, and suppressive immune cells, such as the metastasis-associated macrophages, neutrophils, and myeloid-derived suppressor cells. The metastatic immune microenvironment differs from primary tumors in stromal and immune cell composition, functionality, and metabolism. Thus far, multiple molecular and metabolic pathways, distinct from primary tumors, have been identified that dampen immune effector functions, confounding the immunotherapy response in metastatic PDAC. This review describes major immunoregulatory pathways that contribute to the metastatic progression and limit immunotherapy outcomes in PDAC. Overall, we highlight the therapeutic vulnerabilities attributable to immunosuppressive factors and discuss whether targeting these molecular and immunological "hot spots" could improve the outcomes of PDAC immunotherapies.