Overexpression of ERCC6L correlates with poor prognosis and confers malignant phenotypes of lung adenocarcinoma

Wnt/β-catenin signaling in the development and therapeutic resistance of non-small cell lung cancer

Wnt/β-catenin signaling is a critical pathway that influences development and therapeutic response of non-small cell lung cancer (NSCLC). In recent years, many Wnt regulators, including proteins, miRNAs, lncRNAs, and circRNAs, have been found to promote or inhibit signaling by acting on Wnt proteins, receptors, signal transducers and transcriptional effectors. The identification of these regulators and their underlying molecular mechanisms provides important implications for how to target this pathway therapeutically. In this review, we summarize recent studies of Wnt regulators in the development and therapeutic response of NSCLC.

In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data

MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA-miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses

Background

Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy.

Methods

The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248.

Results

Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage.

Conclusions

Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

PICH Activates Cyclin A1 Transcription to Drive S-Phase Progression and Chemoresistance in Gastric Cancer

The chemotherapeutic agent 5-fluorouracil (5-FU) remains the backbone of postoperative adjuvant treatment for gastric cancer. However, fewer than half of patients with gastric cancer benefit from 5-FU-based chemotherapies owing to chemoresistance and limited clinical biomarkers. Here, we identified the SNF2 protein Polo-like kinase 1-interacting checkpoint helicase (PICH) as a predictor of 5-FU chemosensitivity and characterized a transcriptional function of PICH distinct from its role in chromosome separation. PICH formed a transcriptional complex with RNA polymerase II (Pol II) and ATF4 at the CCNA1 promoter in an ATPase-dependent manner. Binding of the PICH complex promoted cyclin A1 transcription and accelerated S-phase progression. Overexpressed PICH impaired 5-FU chemosensitivity in human organoids and patient-derived xenografts. Furthermore, elevated PICH expression was negatively correlated with survival in postoperative patients receiving 5-FU chemotherapy. Together, these findings reveal an ATPase-dependent transcriptional function of PICH that promotes cyclin A1 transcription to drive 5-FU chemoresistance, providing a potential predictive biomarker of 5-FU chemosensitivity for postoperative patients with gastric cancer and prompting further investigation into the transcriptional activity of PICH.

SIGNIFICANCE

PICH binds Pol II and ATF4 in an ATPase-dependent manner to form a transcriptional complex that promotes cyclin A1 expression, accelerates S-phase progression, and impairs 5-FU chemosensitivity in gastric cancer.