Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer

An Inflammation-Related lncRNA Signature for Prognostic Prediction in Colorectal Cancer

BACKGROUND

Colorectal cancer (CRC) represents a commonly diagnosed malignancy affecting the digestive system. Mounting evidence shows long noncoding RNAs (lncRNAs) contribute to carcinogenesis. However, inflammation-related lncRNAs (IRLs) regulating CRC are poorly defined.

AIMS

The current study aimed to develop an IRL signature for predicting prognosis in CRC and to examine the involved molecular mechanism.

METHODS AND RESULTS

RNA-seq findings and patient data were retrieved from The Cancer Genome Atlas (TCGA), and inflammation-associated genes were obtained from the GeneCards database. IRLs with differential expression were determined with "limma" in R. Using correlation and univariable Cox analyses, prognostic IRLs were identified. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to construct a prognostic model including 13 IRLs. The model's prognostic value was examined by Kaplan-Meier (K-M) survival curve and receiver operating characteristic (ROC) curve analyses. Furthermore, the association of the signature with the immune profile was assessed. Finally, RT-qPCR was carried out for verifying the expression of inflammation-related lncRNAs in nonmalignant and malignant tissue samples. A model containing 13 inflammation-related lncRNAs was built and utilized to classify cases into two risk groups based on risk score. The signature-derived risk score had a higher value in predicting survival compared with traditionally used clinicopathological properties in CRC cases. In addition, marked differences were detected in immune cells between the two groups, including CD4+ T cells and M2 macrophages. Furthermore, RT-qPCR confirmed the expression patterns of these 13 lncRNAs were comparable to those of the TCGA-CRC cohort.

CONCLUSION

The proposed 13-IRL signature is a promising biomarker and may help the clinical decision-making process and improve prognostic evaluation in CRC.

Identification of inflammation related gene signatures for bladder cancer prognosis prediction

Early diagnosis and treatment of bladder cancer are crucial, and since inflammation plays a role in all stages of bladder cancer, this study aims to develop a model based on inflammation-related genes to accurately predict patient prognosis. The data were initially processed through differential analysis and prognostic correlation analysis, then a Least absolute shrinkage and selection operator (LASSO) regression model was constructed by M-cohort and a nomogram was designed to increase the model readability. The T-cohort was used for internal validation, with the GSE32894 and Imvigor210 cohorts used as external data to verify the model's accuracy. The model's predictive ability was verified for the prognosis of patients of different ages, gender, tumor stage, and tumour grade. The GSE3167, GSE13507 and GeneExpression Profiling Interactive Analysis (GEPIA) datasets and Human Protein Atlas (HPA) database were used to verify the expression of the inflammation-related genes, which were confirmed by real-time Polymerase Chain Reaction (PCR). A comprehensive analysis of the model's inflammation-related genes, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA) enrichment analysis, and immune-related analysis were also performed. Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations. Hierarchical validation results demonstrated that the model's predictive power is reliable for various patient stratifications. The expression of inflammation-related genes was consistent across The Cancer Genome Atlas (TCGA) database, GSE3167 dataset, GSE13507 dataset, Gene Expression Profiling Interactive Analysis (GEPIA) database, and the Human Protein Atlas (HPA) database, and was further validated by real-time Polymerase Chain Reaction (PCR). Pathway enrichment analysis indicated that patients in the high-risk (H-risk) group exhibited a variety of tumors. Meanwhile, patients in the low-risk (L-risk) group may be candidates for immunotherapy, whereas those in the high-risk group are more likely to benefit from chemotherapy. The model of inflammation-related genes can accurately predict bladder cancer patient prognosis, with MEST, FASN, KRT6B, and RGS2 anticipated to become new prognostic bladder cancer markers.

Investigating hub genes in the relationship between septic cardiomyopathy and cuproptosis and potential Chinese herbal drug candidates with bioinformatic tools

BACKGROUND

The aim of this study was using bioinformatic tools to identify hub genes in the relationship between septic cardiomyopathy (SCM) and cuproptosis and predict potential Chinese herbal drug candidates.

METHODS

SCM datasets were downloaded from the gene expression omnibus. Cuproptosis related genes were collected from a research published on Science in March, 2022. The expression profiles of genes related to cuproptosis in SCM were extracted. Differentially expressed genes (DEGs) were analyzed using R package limma. A single-sample gene set enrichment analysis was conducted to measure the correlation between DEGs and immune cell infiltration. Hub genes were screened out by random forest model. Finally, HERB database and COREMINE database were used to predict Chinese herbal drugs for hub genes and carry out molecular docking.

RESULTS

A total of 9 DEGs were identified. Cuproptosis differential genes PDHB, DLAT, DLD, FDX1, GCSH, LIAS were significantly correlated with one or more cells and their functions in immune infiltration. The random forest model screened pyruvate dehydrogenase E1 beta subunit (PDHB) as the hub gene. PDHB was negatively correlated with Plasmacytoid dendritic cell infiltration. Pyruvic acid, rhodioloside and adenosine were predicted with PDHB as the target, and all three components are able to bind to PDHB.

CONCLUSIONS

Cuproptosis related gene PDHB is associated with the occurrence and immune infiltration of septic cardiomyopathy. Rhodioloside and other Chinese herbal drugs may play a role in the treatment of SCM by regulating the expression of PDHB.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

TCGA RNA-Seq and Tumor-Infiltrating Lymphocyte Imaging Data Reveal Cold Tumor Signatures of Invasive Ductal Carcinomas and Estrogen Receptor-Positive Human Breast Tumors

Comparative studies of immune-active hot and immune-deserted cold tumors are critical for identifying therapeutic targets and strategies to improve immunotherapy outcomes in cancer patients. Tumors with high tumor-infiltrating lymphocytes (TILs) are likely to respond to immunotherapy. We used the human breast cancer RNA-seq data from the cancer genome atlas (TCGA) and classified them into hot and cold tumors based on their lymphocyte infiltration scores. We compared the immune profiles of hot and cold tumors, their corresponding normal tissue adjacent to the tumor (NAT), and normal breast tissues from healthy individuals from the Genotype-Tissue Expression (GTEx) database. Cold tumors showed a significantly lower effector T cells, lower levels of antigen presentation, higher pro-tumorigenic M2 macrophages, and higher expression of extracellular matrix (ECM) stiffness-associated genes. Hot/cold dichotomy was further tested using TIL maps and H&E whole-slide pathology images from the cancer imaging archive (TCIA). Analysis of both datasets revealed that infiltrating ductal carcinoma and estrogen receptor ER-positive tumors were significantly associated with cold features. However, only TIL map analysis indicated lobular carcinomas as cold tumors and triple-negative breast cancers (TNBC) as hot tumors. Thus, RNA-seq data may be clinically relevant to tumor immune signatures when the results are supported by pathological evidence.

Roles of non-coding RNAs in the metabolism and pathogenesis of bladder cancer

Bladder cancer (BC) is featured as the second most common malignancy of the urinary tract worldwide with few treatments leading to high incidence and mortality. It stayed a virtually intractable disease, and efforts to identify innovative and effective therapies are urgently needed. At present, more and more evidence shows the importance of non-coding RNA (ncRNA) for disease-related study, diagnosis, and treatment of diverse types of malignancies. Recent evidence suggests that dysregulated functions of ncRNAs are closely associated with the pathogenesis of numerous cancers including BC. The detailed mechanisms underlying the dysregulated role of ncRNAs in cancer progression are still not fully understood. This review mainly summarizes recent findings on regulatory mechanisms of the ncRNAs, long non-coding RNAs, microRNAs, and circular RNAs, in cancer progression or suppression and focuses on the predictive values of ncRNAs-related signatures in BC clinical outcomes. A deeper understanding of the ncRNA interactive network could be compelling framework for developing biomarker-guided clinical trials.