A novel serum m7G-harboring microRNA signature for cancer detection.

Background: Emerging evidence points to the exceptional importance and value of m7G alteration in the diagnosis and prognosis of cancers. Nonetheless, a biomarker for precise screening of various cancer types has not yet been developed based on serum m7G-harboring miRNAs. Methods: A total of 20,702 serum samples, covering 12 cancer types and consisting of 7,768 cancer samples and 12,934 cancer-free samples were used in this study. A m7G target miRNA diagnostic signature (m7G-miRDS) was established through the least absolute shrinkage and selection operator (LASSO) analyses in a training dataset (n = 10,351), and validated in a validation dataset (n = 10,351). Results: The m7G-miRDS model, a 12 m7G-target-miRNAs signature, demonstrated high accuracy and was qualified for cancer detection. In the training and validation cohort, the area under the curve (AUC) reached 0.974 (95% CI 0.971-0.977) and 0.972 (95% CI 0.969-0.975), respectively. The m7G-miRDS showed superior sensitivity in each cancer type and had a satisfactory AUC in identifying bladder cancer, lung cancer and esophageal cancer. Additionally, the diagnostic performance of m7G-miRDS was not interfered by the gender, age and benign disease. Conclusion: Our results greatly extended the value of serum circulating miRNAs and m7G in cancer detection, and provided a new direction and strategy for the development of novel biomarkers with high accuracy, low cost and less invasiveness for mass cancer screening, such as ncRNA modification.

Corrigendum: Development and validation of a m6A -regulated prognostic signature in lung adenocarcinoma.

[This corrects the article DOI: 10.3389/fonc.2022.947808.].

ESCCdb: A comprehensive database and key regulator exploring platform based on cross dataset comparisons for esophageal squamous cell carcinoma.

Esophageal cancer is the seventh most prevalent and the sixth most lethal cancer. Esophageal squamous cell carcinoma (ESCC) is one of the major esophageal cancer subtypes that accounts for 87 % of the total cases. However, its molecular mechanism remains unclear. Here, we present an integrated database for ESCC called ESCCdb, which includes a total of 56 datasets and published studies from the GEO, Xena or SRA databases and related publications. It helps users to explore a particular gene with multiple graphical and interactive views with one click. The results comprise expression changes across 20 datasets, copy number alterations in 11 datasets, somatic mutations from 12 papers, related drugs derived from DGIdb, related pathways, and gene correlations. ESCCdb enables directly cross-dataset comparison of a gene's mutations, expressions and copy number changes in multiple datasets. This allows users to easily assess the alterations in ESCC. Furthermore, survival analysis, drug-gene relationships, and results from whole-genome CRISPR/Cas9 screening can help users determine the clinical relevance, derive functional inferences, and identify potential drugs. Notably, ESCCdb also enables the exploration of the correlation structure and identification of potential key regulators for a process. Finally, we identified 789 consistently differential expressed genes; we summarized recurrently mutated genes and genes affected by significant copy number alterations. These genes may be stable biomarkers or important players during ESCC development. ESCCdb fills the gap between massive omics data and users' needs for integrated analysis and can promote basic and clinical ESCC research. The database is freely accessible at http://cailab.labshare.cn/ESCCdb.

CASA: a comprehensive database resource for the COVID-19 Alternative Splicing Atlas.

BACKGROUND: As a key process in transcriptional regulatory mechanisms, alternative splicing (AS) plays a crucial role in maintaining the diversity of RNA and protein expression, and mediates the immune response in infectious diseases, especially for the COVID-19. Therefore, urgent data gathering and more research of AS profiles in microbe-infected human cells are needed to improve understanding of COVID-19 and related infectious diseases. Herein, we have created CASA, the COVID-19 Alternative Splicing Atlas to provide a convenient computing platform for studies of AS in COVID-19 and COVID-19-related infectious diseases. METHODS: In CASA, we reanalyzed thousands of RNA-seq datasets generated from 65 different tissues, organoids and cell lines to systematically obtain quantitative data on AS events under different conditions. A total of 262,994 AS events from various infectious diseases with differing severity were detected and visualized in this database. In order to explore the potential function of dynamics AS events, we performed analysis of functional annotations and drug-target interactions affected by AS in each dataset. RNA-binding proteins (RBPs), which may regulate these dynamic AS events are also provided for users in this database. RESULTS: CASA displays microbe-induced alterations of the host cell splicing landscape across different virus families and helps users identify condition-specific splicing patterns, as well as their potential regulators. CASA may greatly facilitate the exploration of AS profiles and novel mechanisms of host cell splicing by viral manipulation. CASA is freely available at http://www.splicedb.net/casa/ .

Development and validation of a m6A -regulated prognostic signature in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer, with a 5-year survival rate of less than 20%. N6-methyladenosine (m6A) is the most prevalent RNA epigenetic modification in eukaryotic cells, and post-transcriptionally regulates gene expression and function by affecting RNA metabolism. The alterations of functionally important m6A sites have been previously shown to play vital roles in tumor initiation and progression, but little is known about the extent to which m6A-regulated genes play in prognostic performance for patients with LUAD. Here, we presented an overview of the m6A methylome in LUAD tissues using transcriptome-wide m6A methylation profiles, and found that differentially methylated transcripts were significantly enriched in tumor-related processes, including immune response, angiogenesis and cell-substrate adhesion. Joint analysis of m6A modification and gene expression suggested that 300 genes were regulated by m6A. Furthermore, we developed a m6A-regulated prognosis-associated signature (m6A-PPS) by performing a multi-step process. The m6A-PPS model, a 15-gene set, was qualified for prognosis prediction for LUAD patients. By regrouping the patients with this model, the OS of the high-risk group was shorter than that of the low-risk group across all datasets. Importantly, patients with high m6A-PPS scores respond better to immunotherapeutic. Our results provide a valuable resource for understanding the important role of epitranscriptomic modifications in the pathogenesis of LUAD, and obtain potential prognostic biomarkers.

Potential applications of clickable probes in EGFR activity visualization and prediction of EGFR-TKI therapy response for NSCLC patients.

The epithelial growth factor receptor (EGFR) is abnormally overexpressed on the cell surface of cancer cells and is strongly associated with cancer cell proliferation, migration, differentiation, apoptosis, and angiogenesis. Tools enabling the visualization of EGFR in a structure-function approach are highly desirable to predict EGFR mutations and guide EGFR tyrosine kinase inhibitor (TKI) treatment making. Here, we describe the design, synthesis, and application of new, potent and selective clickable probes 13 (HX03), 20 (HX04) and 24 (HX05) by introducing an alkyne ligation handle to visualize EGFR activity in living cancer cells and tissue slices. These clickable probes are versatile chemical tools based on the key pharmacophore (4-anilinoquinazoline) of EGFR-TKIs (e.g., canertinib, dacomitinib and afatinib) and are able to irreversibly target the kinase domain of EGFR. Among them, 13 exhibits the highest reactivity towards EGFR kinase, particularly to EGFR kinase with primary mutations. Using activity-based protein profiling strategy, 13 showed high sensitivity and selectivity in labeling of endogenous EGFR in a native cellular context. Moreover, 13 was applied to visualize EGFR mutant activity in tumour tissues from non-small-cell lung cancer (NSCLC) xenograft mouse models, and patients with NSCLC for the prediction of EGFR-TKI sensitivity. These results demonstrate that strategically designed EGFR-TKI-based probes allow discriminating EGFR mutations in human tissues and hold promise as useful diagnostic tools in predicting EGFR-TKI therapy response.