Application of G-quadruplex targets in gastrointestinal cancers: Advancements, challenges and prospects

Genomic instability and inflammation are considered to be two enabling characteristics that support cancer development and progression. G-quadruplex structure is a key element that contributes to genomic instability and inflammation. G-quadruplexes were once regarded as simply an obstacle that can block the transcription of oncogenes. A ligand targeting G-quadruplexes was found to have anticancer activity, making G-quadruplexes potential anticancer targets. However, further investigation has revealed that G-quadruplexes are widely distributed throughout the human genome and have many functions, such as regulating DNA replication, DNA repair, transcription, translation, epigenetics, and inflammatory response. G-quadruplexes play double regulatory roles in transcription and translation. In this review, we focus on G-quadruplexes as novel targets for the treatment of gastrointestinal cancers. We summarize the application basis of G-quadruplexes in gastrointestinal cancers, including their distribution sites, structural characteristics, and physiological functions. We describe the current status of applications for the treatment of esophageal cancer, pancreatic cancer, hepatocellular carcinoma, gastric cancer, colorectal cancer, and gastrointestinal stromal tumors, as well as the associated challenges. Finally, we review the prospective clinical applications of G-quadruplex targets, providing references for targeted treatment strategies in gastrointestinal cancers.

Colorimetric Aptasensor for the Visual and Microplate Determination of Clusterin in Human Urine Based on Aggregation Characteristics of Gold Nanoparticles

Clusterin has the potential to become the biomarker of multiple diseases, but its clinical quantitative detection methods are limited, which restricts its research progress as a biomarker. A rapid and visible colorimetric sensor for clusterin detection based on sodium chloride-induced aggregation characteristic of gold nanoparticles (AuNPs) was successfully constructed. Unlike the existing methods based on antigen-antibody recognition reactions, the aptamer of clusterin was used as the sensing recognition element. The aptamer could protect AuNPs from aggregation caused by sodium chloride, but clusterin bound with aptamer detached it from AuNPs, thereby inducing aggregation again. Simultaneously, the color change from red in the dispersed state to purple gray in the aggregated state made it possible to preliminarily judge the concentration of clusterin by observation. This biosensor showed a linear range of 0.02-2 ng/mL and good sensitivity with a detection limit of 5.37 pg/mL. The test results of clusterin in spiked human urine confirmed that the recovery rate was satisfactory. The proposed strategy is helpful for the development of label-free point-of-care testing equipment for clinical testing of clusterin, which is cost-effective and feasible.

Identification and validation of xenobiotic metabolism-associated prognostic signature based on five genes to evaluate immune microenvironment in colon cancer

BACKGROUND

Xenobiotic metabolism plays an important role in the progression of colon cancer; however, little is known about its related biomarkers. This study sought to construct a prognostic model related to xenobiotic metabolism in colon cancer, and further reveal the characteristics of tumor immune microenvironment based on the prognostic model.

METHODS

Transcriptome data of 41 normal colon tissues and 473 colon tumor tissues and the clinical features of 452 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Data on xenobiotic metabolism genes (XMGs) were obtained from the hallmark xenobiotic metabolism set of the Molecular Signatures Database (MSigDB) and articles. Additionally, data on differential XMGs in colon cancer were acquired for a functional enrichment analysis by R software. An XMG prognostic model was constructed by a Cox regression analysis, and evaluated using Kaplan-Meier survival curves, risk curves, receiver operating characteristic (ROC) curves, and an independent prognostic analysis in a training cohort and validation cohort. Moreover, tumor immune infiltration and negative regulatory immune genes of cancer-immunity cycle (CIC), including immune checkpoints and immune cytokines, were further analyzed between low- and high-risk groups in both the training and validation cohorts. Differences with P value <0.05 were interpreted as statistically significant.

RESULTS

A total of 126 differential XMGs were distinguished in the colon cancer data set, which were mainly enriched in the metabolism pathways of drugs and nutrients. There were 5 optimized genes (i.e., CYP2W1, GSTM1, TGFB2, MPP2, and ACOX1) used to construct the prognosis model, which effectively predicted prognosis and had good ROC curves. Between low- and high-risk groups, there were significant differences in abundance for T cells CD4 memory resting and T cells regulatory (Tregs), and expression of PDCD1, LAG3, NOS3, TGFB1, and ICAM1 in the training cohort and validation cohort.

CONCLUSIONS

The XMGs in the prognostic model have a good prediction effect on the prognosis of colon cancer patients. The T cells CD4 memory resting, and Tregs, immune checkpoints PDCD1 and LAG3, and CIC negative regulatory immune cytokines NOS3, TGFB1, and ICAM1 are closely associated with xenobiotic metabolism.

Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer

Background

Compared with colon cancer, the increase of morbidity is more significant for rectal cancer. The current study set out to identify novel and critical biomarkers or features that may be used as promising targets for early diagnosis and treatment monitoring of rectal cancer.

Methods

Microarray datasets of rectal cancer with a minimum sample size of 30 and RNA-sequencing datasets of rectal adenocarcinoma (READ) were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The method of robust rank aggregation was utilized to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was structured using the STRING platform, and hub genes were identified using the Cytoscape plugin cytoHubba and an UpSet diagram. R software was employed to perform functional enrichment analysis. Receiver operating characteristic (ROC) curves based on the GEO data and Kaplan-Meier curves based on the TCGA data were drawn to assess the diagnostic and prognostic values of the hub genes. Immune cell infiltration analysis was conducted with CIBERSORT, and the diagnostic value and correlations between prognostic genes and infiltrated immune cells were analyzed by principal component analysis (PCA), ROC curves, and correlation scatter plots.

Results

A total of 137 robust DEGs were obtained by integrating datasets in GEO. Twenty-four hub genes, including CHGA, TTR, SAA1, SPP1, MMP1, TGFBI, COL1A1, and PCK1, were identified as a diagnostic gene biomarker group for rectal cancer, and SAA1, SPP1, and SI were identified as potential novel prognostic biomarkers. Functionally, the hub genes were mainly involved in the rectal cancer related interleukin (IL)-17 and proximal tubule bicarbonate reclamation pathways. Twelve sensitive infiltrated immune cells were identified, and were correlated with prognostic genes.

Conclusions

The integrated gene biomarker group combined with immune cell infiltration can effectively indicate rectal cancer.