Long Non-Coding RNAs ASB16-AS1 and AFAP1-AS1: Diagnostic, Prognostic Impact and Survival Analysis in Colorectal Cancer

Long non‑coding RNAs as diagnostic and prognostic biomarkers for colorectal cancer (Review)

Colorectal cancer (CRC) ranks as the 3rd most common cancer globally and is the 2nd leading cause of cancer-related death. Owing to the lack of specific early symptoms and the limitations of existing early diagnostic methods, most patients with CRC are diagnosed at advanced stages. To overcome these challenges, researchers have increasingly focused on molecular biomarkers, with particular interest in long non-coding RNAs (lncRNAs). These non-protein-coding RNAs, which exceed 200 nucleotides in length, play critical roles in the development and progression of CRC. The stability and detectability of lncRNAs in the circulatory system make them promising candidate biomarkers. The analysis of circulating lncRNAs in peripheral blood represents a potential option for minimally invasive diagnostic tests based on liquid biopsy samples. The present review aimed to evaluate the efficacy of lncRNAs with altered expression levels in peripheral blood as diagnostic markers for CRC. Additionally, the clinical significance of lncRNAs as prognostic markers for this disease were summarized.

The functions and mechanisms of long non-coding RNA in colorectal cancer

CRC poses a significant challenge in the global health domain, with a high number of deaths attributed to this disease annually. If CRC is detected only in its advanced stages, the difficulty of treatment increases significantly. Therefore, biomarkers for the early detection of CRC play a crucial role in improving patient outcomes and increasing survival rates. The development of a reliable biomarker for early detection of CRC is particularly important for timely diagnosis and treatment. However, current methods for CRC detection, such as endoscopic examination, blood, and stool tests, have certain limitations and often only detect cases in the late stages. To overcome these constraints, researchers have turned their attention to molecular biomarkers, which are considered a promising approach to improving CRC detection. Non-invasive methods using biomarkers such as mRNA, circulating cell-free DNA, microRNA, LncRNA, and proteins can provide more reliable diagnostic information. These biomarkers can be found in blood, tissue, stool, and volatile organic compounds. Identifying molecular biomarkers with high sensitivity and specificity for the early and safe, economic, and easily measurable detection of CRC remains a significant challenge for researchers.

Propensity score matching as an effective strategy for biomarker cohort design and omics data analysis

BACKGROUND

Analysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors.

METHODS

We explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results.

RESULTS

Unlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts.

CONCLUSION

PSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery.

DNA methylation fine-tunes pro-and anti-inflammatory signalling pathways in inactive ulcerative colitis tissue biopsies

DNA methylation has been implied to play a role in the immune dysfunction associated with inflammatory bowel disease (IBD) and the disease development of ulcerative colitis (UC). Changes of the DNA methylation and correlated gene expression in patient samples with inactive UC might reveal possible regulatory features important for further treatment options for UC. Targeted bisulfite sequencing and whole transcriptome sequencing were performed on mucosal biopsies from patients with active UC (UC, n = 14), inactive UC (RM, n = 20), and non-IBD patients which served as controls (NN, n = 11). The differentially methylated regions (DMRs) were identified by DMRseq. Correlation analysis was performed between DMRs and their nearest differentially expressed genes (DEGs). Principal component analysis (PCA) was performed based on correlated DMR regulated genes. DMR regulated genes then were functional annotated. Cell-type deconvolutions were performed based on methylation levels. The comparisons revealed a total of 38 methylation-regulated genes in inactive UC that are potentially regulated by DMRs (correlation p value < 0.1). Several methylation-regulated genes could be identified in inactive UC participating in IL-10 and cytokine signalling pathways such as IL1B and STAT3. DNA methylation events in inactive UC seem to be fine-tuned by the balancing pro- and anti- inflammatory pathways to maintain a prevailed healing process to restore dynamic epithelium homeostasis.

Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism

LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A .

Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments

BACKGROUND

Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects.

RECENT FINDINGS

The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy.

CONCLUSION

The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.