Promoter Hypermethylation of CHODL Contributes to Carcinogenesis and Indicates Poor Survival in Patients with Early-stage Colorectal Cancer

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing to Identify an Immunogenic Cell Death-Related 5-Gene Prognostic Signature in Hepatocellular Carcinoma

Introduction

Immunogenic cell death (ICD) can enhance the potency of immunotherapy in cancer treatment. Nevertheless, it is ambiguous how ICD-related genes (ICDRGs) contribute to hepatocellular carcinoma (HCC).

Methods

Single-cell RNA sequencing (scRNA-seq) data were used to distinguish malignant cells from normal cells in the HCC tumor microenvironment(TME). Bulk RNA sequencing data was employed to acquire the landscape of the 33 ICDRGs. Unsupervised clustering identified two ICD molecular subtypes. The cellular infiltration characteristics and biological behavior in different subtypes were analyzed by ssGSEA. Subsequently, differentially expressed genes (DEGs) between the two subtypes were determined, based on which patients were classified into three gene clusters. Then, the prognostic model was constructed by Lasso-Cox analysis. Finally, we investigated the expression of risk genes in cancer cell line encyclopedia (CCLE) and validated the function of NKX3-2 in vitro experiments.

Results

ICD scores and ICDRGs expression in malignant cells were significantly lower than in normal cells by scRNA-seq analysis. ICD-high subtype was characterized by ICD-related gene overexpression and high levels of immune infiltration abundance and immune checkpoints; Three DEGs-related gene clusters were likewise strongly linked to stromal and immunological activation. In the ICD-related prognostic model consisting of NKX3-2, CHODL, MMP1, NR0B1, and CTSV, the low-risk group patients had a better endpoint and displayed increased susceptibility to immunotherapy and chemotherapeutic drugs like 5-Fluorouracil, afatinib, bortezomib, cediratinib, lapatinib, dasatinib, gefitinib and crizotinib. Moreover, NKX3-2 amplification in HCC samples has been verified by experiments, and its disruption suppressed the proliferation and invasion of tumor cells.

Conclusion

Our study highlighted the potential of the ICDRGs risk score as a prognostic indicator to aid in the accurate diagnosis and immunotherapy sensitivity of HCC.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

To Ubiquitinate or Not to Ubiquitinate: TRIM17 in Cell Life and Death

TRIM17 is a member of the TRIM family, a large class of RING-containing E3 ubiquitin-ligases. It is expressed at low levels in adult tissues, except in testis and in some brain regions. However, it can be highly induced in stress conditions which makes it a putative stress sensor required for the triggering of key cellular responses. As most TRIM members, TRIM17 can act as an E3 ubiquitin-ligase and promote the degradation by the proteasome of substrates such as the antiapoptotic protein MCL1. Intriguingly, TRIM17 can also prevent the ubiquitination of other proteins and stabilize them, by binding to other TRIM proteins and inhibiting their E3 ubiquitin-ligase activity. This duality of action confers several pivotal roles to TRIM17 in crucial cellular processes such as apoptosis, autophagy or cell division, but also in pathological conditions as diverse as Parkinson's disease or cancer. Here, in addition to recent data that endorse this duality, we review what is currently known from public databases and the literature about TRIM17 gene regulation and expression, TRIM17 protein structure and interactions, as well as its involvement in cell physiology and human disorders.