Functional identification and prediction of lncRNAs in esophageal cancer

Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites

5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cost, labor, and time. As such, there is a pressing need for efficient computational approaches. Many existing computational methods rely on intricate hand-crafted features, requiring unavailable features, often leading to suboptimal prediction accuracy. Addressing these challenges, we introduce a novel deep-learning method, Trans-m5C. We first categorize m5C sites into NSUN2-dependent and NSUN6-dependent types for independent feature extraction. Subsequently, meticulously crafted transformer neural networks are employed to distill global features. The prediction of m5C sites is then accomplished using a discriminator built from a multi-layer perceptron. A rigorous evaluation for the performance of Trans-m5C on experimentally validated m5C data from human and mouse species reveals that our method offers a competitive edge over both baseline and existing methodologies.

Detection of a novel DNA methylation marker panel for esophageal cancer diagnosis using circulating tumor DNA

BACKGROUND

Esophageal cancer (ECa) is one of the most deadly cancers, with increasing incidence worldwide and poor prognosis. While endoscopy is recommended for the detection of ECa in high-risk individuals, it is not suitable for large-scale screening due to its invasiveness and inconvenience.

METHODS

In this study, a novel gene methylation panel was developed for a blood-based test, and its diagnostic efficacy was evaluated using a cohort of 304 participants (203 cases, 101 controls). The assessment focused on the DNA methylation levels of SEPTIN9, tissue factor pathway inhibitor 2 (TFPI2), and the fragile histidine triad gene (FHIT) in patients with ECa, benign esophageal disease, and healthy controls. The receiver operating characteristic (ROC) curve was generated for the panel to calculate the area under the curve (AUC), sensitivity, specificity, and 95% confidence intervals (CIs), along with a comparison to the gold standard of pathological examination. The consistency between biomarker and pathological diagnosis was evaluated with kappa analysis conducted with IBM SPSS Statistics. The Chi-square test or Fisher's exact test was utilized to assess the association of test positivity with demographic characteristics.

RESULTS

In patients with ECa, SEPTIN9, TFPI2, and FHIT DNA methylation levels were significantly higher compared to those with benign esophageal disease or healthy controls. The panel demonstrated promising potential as a noninvasive tool for distinguishing malignant tumors from both healthy controls and benign esophageal diseases, achieving an area under the ROC curve of 0.925 (95% CI: 0.889-0.952), with a sensitivity of 79.8% [95% CI 73.6-85.1%] and specificity of 95.0% [95% CI 88.8-98.4%]. In particular, the panel showed exceptional diagnostic efficiency for stage 0, I, and II cancer patients with sensitivity at 69.0, 75.5%, and 78.9%, respectively. The comparison revealed a Kappa value of 0.725 between RT-PCR testing and the established gold standard of pathological examination, indicating a high level of consistency. Additionally, there was no bias in diagnostic efficiency based on age, gender, or the presence of other malignancies (non-esophageal cancers).

CONCLUSIONS

The study's findings suggested that the DNA methylation biomarkers panel holds promise as a non-invasive and convenient diagnostic test for ECa. The panel's ability to distinguish malignant tumors from benign esophageal diseases, coupled with its high sensitivity and specificity, presented opportunities to enhance the over-all diagnosis of high-risk population when in conjunction with existing detection methods.

Long noncoding RNAs as potential targets for overcoming chemoresistance in upper gastrointestinal cancers

In the last decade, researchers have paid much attention to the role of noncoding RNA molecules in human diseases. Among the most important of these molecules are LncRNAs, which are RNA molecules with a length of more than 200 nucleotides. LncRNAs can regulate gene expression through various mechanisms, such as binding to DNA sequences and interacting with miRNAs. Studies have shown that LncRNAs may be valuable therapeutic targets in treating various cancers, including upper-gastrointestinal cancers. Upper gastrointestinal cancers, mainly referring to esophageal and gastric cancers, are among the deadliest gastrointestinal cancers. Despite notable advances, traditional chemotherapy remains a common strategy for treating these cancers. However, chemoresistance poses a significant obstacle to the effective treatment of upper gastrointestinal cancers, resulting in a low survival rate. Chemoresistance arises from various events, such as the enhancement of efflux and detoxification of chemotherapy agents, reduction of drug uptake, alteration of drug targeting, reduction of prodrug activation, strengthening of EMT and stemness, and the attenuation of apoptosis in cancerous cells. Tumor microenvironment also plays an important role in chemoresistance. Interestingly, a series of studies have revealed that LncRNAs can influence important mechanisms associated with some of the aforementioned events and may serve as promising targets for mitigating chemoresistance in upper gastrointestinal cancers. In this review paper, following a concise overview of chemoresistance mechanisms in upper gastrointestinal cancers, we will review the most intriguing findings of these investigations in detail.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

Identification of cuproptosis-related lncRNAs with the significance in prognosis and immunotherapy of oral squamous cell carcinoma

Cuproptosis, a recently characterized programmed cell death mechanism, has emerged as a potential contributor to tumorigenesis, metastasis, and immune modulation. Long non-coding RNAs (lncRNAs) have demonstrated diverse regulatory roles in cancer and hold promise as biomarkers. However, the involvement and prognostic significance of cuproptosis-related lncRNAs (CRLs) in oral squamous cell carcinoma (OSCC) remain poorly understood. Based on TCGA-OSCC data, we integrated single-sample gene set enrichment analysis (ssGSEA), the LASSO algorithm, and the tumor immune dysfunction and exclusion (TIDE) algorithm. We identified 11 CRLs through differential expression, Spearman correlation, and univariate Cox regression analyses. Two distinct CRL-related subtypes were unveiled, delineating divergent survival patterns, tumor microenvironments (TME), and mutation profiles. A robust CRL-based signature (including AC107027.3, AC008011.2, MYOSLID, AC005785.1, AC019080.5, AC020558.2, AC025265.1, FAM27E3, and LINC02367) prognosticated OSCC outcomes, immunotherapy responses, and anti-tumor strategies. Superior predictive power compared to other lncRNA models was demonstrated. Functional assessments confirmed the influence of FAM27E3, LINC02367, and MYOSLID knockdown on OSCC cell behaviors. Remarkably, the CRLs-based signature maintained stability across OSCC patient subgroups, underscoring its clinical potential for survival prediction. This study elucidates CRLs' roles in TME of OSCC and establishes a potential signature for precision therapy.

Potential biomarkers: The hypomethylation of cg18949415 and cg22193385 sites in colon adenocarcinoma

Overall cancer hypomethylation had been identified in the past, but it is not clear exactly which hypomethylation site is the more important for the occurrence of cancer. To identify key hypomethylation sites, we studied the effect of hypomethylation in twelve regions on gene expression in colon adenocarcinoma (COAD). The key DNA methylation sites of cg18949415, cg22193385 and important genes of C6orf223, KRT7 were found by constructing a prognostic model, survival analysis and random combination prediction a series of in-depth systematic calculations and analyses, and the results were validated by GEO database, immune microenvironment, drug and functional enrichment analysis. Based on the expression values of C6orf223, KRT7 genes and the DNA methylation values of cg18949415, cg22193385 sites, the least diversity increment algorithm were used to predict COAD and normal sample. The 100 % reliability and 97.12 % correctness of predicting tumor samples were obtained in jackknife test. Moreover, we found that C6orf223 gene, cg18949415 site play a more important role than KRT7 gene, cg22193385 site in COAD. In addition, we investigate the impact of key methylation sites on three-dimensional chromatin structure. Our results will be help for experimental studies and may be an epigenetic biomarker for COAD.