ATF3-CBS signaling axis coordinates ferroptosis and tumorigenesis in colorectal cancer.

The induction of ferroptosis is promising for cancer therapy. However, the mechanisms enabling cancer cells to evade ferroptosis, particularly in low-cystine environments, remain elusive. Our study delves into the intricate regulatory mechanisms of Activating transcription factor 3 (ATF3) on Cystathionine ß-synthase (CBS) under cystine deprivation stress, conferring resistance to ferroptosis in colorectal cancer (CRC) cells. Additionally, our findings establish a positively correlation between this signaling axis and CRC progression, suggesting its potential as a therapeutic target. Mechanistically, ATF3 positively regulates CBS to resist ferroptosis under cystine deprivation stress. In contrast, the suppression of CBS sensitizes CRC cells to ferroptosis through targeting the mitochondrial tricarboxylic acid (TCA) cycle. Notably, our study highlights that the ATF3-CBS signaling axis enhances ferroptosis-based CRC cancer therapy. Collectively, the findings reveal that the ATF3-CBS signaling axis is the primary feedback pathway in ferroptosis, and blocking this axis could be a potential therapeutic approach for colorectal cancer.

METTL17 coordinates ferroptosis and tumorigenesis by regulating mitochondrial translation in colorectal cancer.

Ferroptosis, an iron-dependent lipid peroxidation-induced form of regulated cell death, shows great promise as a cancer therapy strategy. Despite the critical role of mitochondria in ferroptosis regulation, the underlying mechanisms remain elusive. This study reveals that the mitochondrial protein METTL17 governs mitochondrial function in colorectal cancer (CRC) cells through epigenetic modulation. Bioinformatic analysis establishes that METTL17 expression positively correlates with ferroptosis resistance in cancer cells and is up-regulated in CRC. Depletion of METTL17 sensitizes CRC cells to ferroptosis, impairs cell proliferation, migration, invasion, xenograft tumor growth, and AOM/DSS-induced CRC tumorigenesis. Furthermore, suppression of METTL17 disrupts mitochondrial function, energy metabolism, and enhances intracellular and mitochondrial lipid peroxidation and ROS levels during ferroptotic stress. Mechanistically, METTL17 inhibition significantly reduces mitochondrial RNA methylation, including m4C, m5C, m3C, m7G, and m6A, leading to impaired translation of mitochondrial protein-coding genes. Additionally, the interacting proteins associated with METTL17 are essential for mitochondrial gene expression, and their knockdown sensitizes CRC cells to ferroptosis and inhibits cell proliferation. Notably, combined targeting of METTL17 and ferroptosis in a therapeutic approach effectively suppresses CRC xenograft growth in vivo. This study uncovers the METTL17-mediated defense mechanism for cell survival and ferroptosis in mitochondria, highlighting METTL17 as a potential therapeutic target for CRC.

CovidShiny: An Integrated Web Tool for SARS-CoV-2 Mutation Profiling and Molecular Diagnosis Assay Evaluation In Silico.

The coronavirus disease 2019 (COVID-19) pandemic is still ongoing, with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continuing to evolve and accumulate mutations. While various bioinformatics tools have been developed for SARS-CoV-2, a well-curated mutation-tracking database integrated with in silico evaluation for molecular diagnostic assays is currently unavailable. To address this, we introduce CovidShiny, a web tool that integrates mutation profiling, in silico evaluation, and data download capabilities for genomic sequence-based SARS-CoV-2 assays and data download. It offers a feasible framework for surveilling the mutation of SARS-CoV-2 and evaluating the coverage of the molecular diagnostic assay for SARS-CoV-2. With CovidShiny, we examined the dynamic mutation pattern of SARS-CoV-2 and evaluated the coverage of commonly used assays on a large scale. Based on our in silico analysis, we stress the importance of using multiple target molecular diagnostic assays for SARS-CoV-2 to avoid potential false-negative results caused by viral mutations. Overall, CovidShiny is a valuable tool for SARS-CoV-2 mutation surveillance and in silico assay design and evaluation.

A qualitative transcriptional prognostic signature for patients with stage I-II pancreatic ductal adenocarcinoma.

Accurately prognostic evaluation of patients with stage I-II pancreatic ductal adenocarcinoma (PDAC) is of importance to treatment decision and patient management. Most previously reported prognostic signatures were based on risk scores summarized from quantitative expression measurements of signature genes, which are susceptible to experimental batch effects and impractical for clinical applications. Based on the within-sample relative expression orderings of genes, we developed a robust qualitative transcriptional prognostic signature, consisting of 64 gene pairs (64-GPS), to predict the overall survival (OS) of 161 stage I-II PDAC patients in the training dataset who were treated with surgery only. Samples were classified into the high-risk group when at least 25 of 64 gene pairs suggested it was at high risk. The signature was successfully validated in 324 samples from 6 independent datasets produced by different laboratories. All samples in the low-risk group had significantly better OS than samples in the high-risk group. Multivariate Cox regression analyses showed that the 64-GPS remained significantly associated with the OS of patients after adjusting available clinical factors. Transcriptomic analysis of the 2 prognostic subgroups showed that the differential expression signals were highly reproducible in all datasets, whereas the differences between samples grouped by the TNM staging system were weak and irreproducible. The epigenomic analysis showed that the epigenetic alternations may cause consistently transcriptional changes between the 2 different prognostic groups. The genomic analysis revealed that mutation­induced disturbances in several key genes, such as LRMDA, MAPK10, and CREBBP, might lead to poor prognosis for PDAC patients. Conclusively, the 64-GPS can robustly predict the prognosis of patients with stage I-II PDAC, which provides theoretical basis for clinical individualized treatment.

Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer.

The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments.

Identifying reproducible cancer-associated highly expressed genes with important functional significances using multiple datasets.

Identifying differentially expressed (DE) genes between cancer and normal tissues is of basic importance for studying cancer mechanisms. However, current methods, such as the commonly used Significance Analysis of Microarrays (SAM), are biased to genes with low expression levels. Recently, we proposed an algorithm, named the pairwise difference (PD) algorithm, to identify highly expressed DE genes based on reproducibility evaluation of top-ranked expression differences between paired technical replicates of cells under two experimental conditions. In this study, we extended the application of the algorithm to the identification of DE genes between two types of tissue samples (biological replicates) based on several independent datasets or sub-datasets of a dataset, by constructing multiple paired average gene expression profiles for the two types of samples. Using multiple datasets for lung and esophageal cancers, we demonstrated that PD could identify many DE genes highly expressed in both cancer and normal tissues that tended to be missed by the commonly used SAM. These highly expressed DE genes, including many housekeeping genes, were significantly enriched in many conservative pathways, such as ribosome, proteasome, phagosome and TNF signaling pathways with important functional significances in oncogenesis.