Deubiquitinase USP4 suppresses antitumor immunity by inhibiting IRF3 activation and tumor cell-intrinsic interferon response in colorectal cancer.

Despite the approval of immune checkpoint blockade (ICB) therapy for various tumor types, its effectiveness is limited to only approximately 15% of patients with microsatellite instability-high (MSI-H) or mismatch repair deficiency (dMMR) colorectal cancer (CRC). Approximately 80%-85% of CRC patients have a microsatellite stability (MSS) phenotype, which features a rare T-cell infiltration. Thus, elucidating the mechanisms underlying resistance to ICB in patients with MSS CRC is imperative. In this study, we demonstrate that ubiquitin-specific peptidase 4 (USP4) is upregulated in MSS CRC tumors and negatively regulates the immune response against tumors in CRC. Additionally, USP4 represses the cellular interferon (IFN) response and antigen presentation and impairs PRR signaling-mediated cell death. Mechanistically, USP4 impedes the nuclear localization of interferon regulator Factor 3 (IRF3) by deubiquitinating the K63-polyubiquitin chain of TRAF6 and IRF3. Knockdown of USP4 enhances the infiltration of T cells in CRC tumors and overcomes ICB resistance in an MC38 syngeneic mouse model. Moreover, published datasets revealed that patients showing higher USP4 expression exhibited decreased responsiveness to anti-PD-L1 therapy. These findings highlight an essential role of USP4 in the suppression of antitumor immunity in CRC.

LncRNA MIR200CHG inhibits EMT in gastric cancer by stabilizing miR-200c from target-directed miRNA degradation.

Gastric cancer (GC) is a heterogeneous disease, threatening millions of lives worldwide, yet the functional roles of long non-coding RNAs (lncRNAs) in different GC subtypes remain poorly characterized. Microsatellite stable (MSS)/epithelial-mesenchymal transition (EMT) GC is the most aggressive subtype associated with a poor prognosis. Here, we apply integrated network analysis to uncover lncRNA heterogeneity between GC subtypes, and identify MIR200CHG as a master regulator mediating EMT specifically in MSS/EMT GC. The expression of MIR200CHG is silenced in MSS/EMT GC by promoter hypermethylation, associated with poor prognosis. MIR200CHG reverses the mesenchymal identity of GC cells in vitro and inhibits metastasis in vivo. Mechanistically, MIR200CHG not only facilitates the biogenesis of its intronic miRNAs miR-200c and miR-141, but also protects miR-200c from target-directed miRNA degradation (TDMD) through direct binding to miR-200c. Our studies reveal a landscape of a subtype-specific lncRNA regulatory network, providing clinically relevant biological insights towards MSS/EMT GC.

Identification and validation of a novel stress granules-related prognostic model in colorectal cancer.

Aims: A growing body of evidence demonstrates that Stress granules (SGs), a non-membrane cytoplasmic compartments, are important to colorectal development and chemoresistance. However, the clinical and pathological significance of SGs in colorectal cancer (CRC) patients is unclear. The aim of this study is to propose a new prognostic model related to SGs for CRC on the basis of transcriptional expression. Main methods: Differentially expressed SGs-related genes (DESGGs) were identified in CRC patients from TCGA dataset by limma R package. The univariate and Multivariate Cox regression model was used to construct a SGs-related prognostic prediction gene signature (SGPPGS). The CIBERSORT algorithm was used to assess cellular immune components between the two different risk groups. The mRNA expression levels of the predictive signature from 3 partial response (PR) and 6 stable disease (SD) or progress disease (PD) after neoadjuvant therapy CRC patients' specimen were examined. Key findings: By screening and identification, SGPPGS comprised of four genes (CPT2, NRG1, GAP43, and CDKN2A) from DESGGs is established. Furthermore, we find that the risk score of SGPPGS is an independent prognostic factor to overall survival. Notably, the abundance of immune response inhibitory components in tumor tissues is upregulated in the group with a high-risk score of SGPPGS. Importantly, the risk score of SGPPGS is associated with the chemotherapy response in metastatic colorectal cancer. Significance: This study reveals the association between SGs related genes and CRC prognosis and provides a novel SGs related gene signature for CRC prognosis prediction.

Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms.

BACKGROUND: Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy. METHODS: Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same-sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data. RESULTS: A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706-0.815), and 0.748 (95% CI 0.659-0.837) for the internal validation set of the logistic regression model, which consisted of eight commonly used clinical indicators (including lipid profile) and GDM subtypes. For the prediction models established by the two machine learning algorithms, which included all the variables, the training set and the internal validation set had AUCs of 0.813 (95% CI 0.786-0.839) and 0.779 (95% CI 0.735-0.824) for the decision tree model, and 0.854 (95% CI 0.831-0.877) and 0.808 (95% CI 0.766-0.850) for the random forest model. CONCLUSION: We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and could guide early prevention strategies.