Development and validation of a risk prediction model and nomogram for colon adenocarcinoma based on methylation-driven genes

A systematic approach introduced some immune system targets in rectal cancer by considering cell-free DNA methylation in response to radiochemotherapy

BACKGROUND

This study aims to investigate cell-free DNA (cfDNA) methylation of genes involved in some immune system targets as biomarkers of radioresistance in patients with non-metastatic rectal cancer.

METHODS

Gene expression (GSE68204, GPL6480, and GSE15781) and DNA methylation profiles (GSE75548 and GSE139404) of rectal cancer patients were obtained from the Gene Expression Omnibus (GEO) database. GEO2R and FunRich software were first used to identify genes with significant expression differences. Enricher softwer was then used to analyze Gene Ontology and detect pathway enrichment of hub genes. Blood samples were then taken from 43 rectal cancer patients. After cfDNA extraction from samples, it was treated with bisulfite and analyzed by methylation-specific PCR.

RESULTS

1088 genes with high and 629 with low expression were identified by GEO2R and FunRich software. A total of five high-expression hub genes, including CDH24, FGF18, CCND1, IFITM1, UBE2V1, and three low-expression hub genes, including CBLN2, VIPR2, and IRF4, were identified from UALCAN and DNMIVD databases. Methylation-specific PCR indicated a significant difference in hub gene methylation between cancerous and non-cancerous individuals. Radiochemotherapy significantly affected hub gene methylation. There was a considerable difference in the methylation rate of hub genes between patients who responded to radiochemotherapy and those who did not.

CONCLUSIONS

Evaluating gene methylation patterns might be an appropriate diagnostic tool to predict radiochemotherapy response and develop targeted therapeutic agents.

Involvement of circRNA Regulators MBNL1 and QKI in the Progression of Esophageal Squamous Cell Carcinoma

OBJECTIVES

To investigate the role of circRNA regulators MBNL1 and QKI in the progression of esophageal squamous cell carcinoma.

BACKGROUND

MBNL1 and QKI are pivotal regulators of pre-mRNA alternative splicing, crucial for controlling circRNA production - an emerging biomarker and functional regulator of tumor progression. Despite their recognized roles, their involvement in ESCC progression remains unexplored.

METHODS

The expression levels of MBNL1 and QKI were examined in 28 tissue pairs from ESCC and adjacent normal tissues using data from the GEO database. Additionally, a total of 151 ESCC tissue samples, from stage T1 to T4, consisting of 13, 43, 87, and 8 cases per stage, respectively, were utilized for immunohistochemical (IHC) analysis. RNA sequencing was utilized to examine the expression profiles of circRNAs, lncRNAs, and mRNAs across 3 normal tissues, 3 ESCC tissues, and 3 pairs of KYSE150 cells in both wildtype (WT) and those with MBNL1 or QKI knockouts. Transwell, colony formation, and subcutaneous tumorigenesis assays assessed the impact of MBNL1 or QKI knockout on ESCC cell migration, invasion, and proliferation.

RESULTS

ESCC onset significantly altered MBNL1 and QKI expression levels, influencing diverse RNA species. Elevated MBNL1 or QKI expression correlated with patient age or tumor invasion depth, respectively. MBNL1 or QKI knockout markedly enhanced cancer cell migration, invasion, proliferation, and tumor growth. Moreover, the absence of either MBNL1 or QKI modulated the expression profiles of multiple circRNAs, causing extensive downstream alterations in the expression of numerous lncRNAs and mRNAs. While the functions of circRNA and lncRNA among the top 20 differentially expressed genes remain unclear, mRNAs like SLCO4C1, TMPRSS15, and MAGEB2 have reported associations with tumor progression.

CONCLUSIONS

This study underscores the tumor-suppressive roles of MBNL1 and QKI in ESCC, proposing them as potential biomarkers and therapeutic targets for ESCC diagnosis and treatment.

Saponin from Platycodi radix inactivates PI3K/AKT signaling pathway to hinder colorectal cancer cell proliferation, invasion, and migration through miR-181c/d-5p/RBM47

Colorectal cancer (CRC) is the third frequent cancer and second leading reason of cancer-related mortality all over the globe. Saponins from Platycodi radix (SPR) and microRNAs (miRNAs) have been reported to regulate CRC cell progression. Real-time quantitative polymerase chain reaction (RT-qPCR) detected miR-181c-5p, miR-181d-5p, and RBM47 expression level. Cell counting kit-8 (CCK-8), 5-ethynyl-20-deoxyuridine (EdU), colony formation, transwell, and wound healing assays validated that miR-181c-5p and miR-181d-5p promote CRC cell proliferation, migration and invasion and SPR exerts opposite effects. Cignal Finder Reporter Array and western blot proved that the activity of PI3K/AKT pathway was decreased by RBM47 overexpression. RNA pulldown, luciferase reporter, and RNA-binding protein immunoprecipitation (RIP) assays proved the interaction between miR-181c/d-5p and RBM47, and RBM47 and PTEN. Rescue experiments were carried out to validate that RBM47 reverses the influence of miR-181c/d-5p on the progression of CRC cells. The stability of PTEN was probed by real-time quantitative polymerase chain reaction in CRC cells treated with Actinomycin D (Act D). To be concluded, SPR inactivates PI3K/AKT signaling pathway to suppress CRC cell proliferation, invasion, and migration via miR-181c/d-5p/RBM47. Elucidating the mechanisms of SPR underlying CRC may offer novel insight into CRC treatment.

Development of an exosome-related and immune microenvironment prognostic signature in colon adenocarcinoma

Background: The correlation between exosomes and the tumor immune microenvironment has been proved to affect tumorigenesis and progression of colon adenocarcinoma (COAD). However, it remained unclear whether exosomes had an impact on the prognostic indications of COAD patients.

Methods: Expression of exosome-related genes (ERGs) and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The ERGs associated with prognosis were identified and exosome-related prognostic signature was constructed. Patients in two risk groups were classified according to the risk score calculation formula: Risk score = 1.0132 * CCKBR + 0.2416 * HOXC6 + 0.7618 * POU4F1. The expression of three ERGs was investigated by qRT-PCR. After that, we developed a nomogram predicting the likelihood of survival and verified its predictive efficiency. The differences of tumor immune microenvironment, immune cell infiltration, immune checkpoint and sensitivity to drugs in two risk groups were analyzed.

Results: A prognostic signature was established based on the three ERGs (CCKBR, HOXC6, and POU4F1) and patients with different risk group were distinguished. Survival analysis revealed the negative associated of risk score and prognosis, ROC curve analyses showed the accuracy of this signature. Three ERGs expression was investigated by qRT-PCR in three colorectal cancer cell lines. Moreover, risk score was positively correlated with tumor mutational burden (TMB), immune activities, microsatellite instability level, the expression of immune checkpoint genes. Meanwhile, the expression level of three ERGs and the risk score were markedly related with the sensitive response to chemotherapy.

Conclusion: The novel signature composed of three ERGs with precise predictive capabilities can be used to predict prognosis and provide a promising therapeutic target for improving the efficacy of immunotherapy.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

A Novel Pyroptosis-Related Gene Signature for Predicting the Prognosis and the Associated Immune Infiltration in Colon Adenocarcinoma

Background

Pyroptosis has been demonstrated to be an inflammatory form of programmed cell death recently. However, the expression of pyroptosis-related genes (PRGs) in colon adenocarcinoma (COAD) and their correlations with prognosis remain unclear.

Methods

Data of COAD patients were obtained from The Cancer Genome Atlas (TCGA) database to screen differentially expressed genes (DEGs). Univariate Cox regression analysis and the LASSO Cox regression analysis were applied to construct a gene signature. All COAD patients in TCGA cohort were separated into low-risk subgroup or high-risk subgroup via the risk score. Kaplan–Meier survival analysis and receiver operator characteristic (ROC) curves were adopted to assess its prognostic efficiency. COAD data from the GSE17537 datasets was used for validation. A prognostic nomogram was established to predict individual survival. The correlation between PRGs and immune cell infiltration in COAD was verified based on TIMER database. CIBERSORT analysis was utilized on risk subgroup as defined by model. The protein and mRNA expression level of PRGs were verified by HPA database and qPCR.

Results

A total of 51 differentially expressed PRGs were identified in TCGA cohort. Through univariate Cox regression analysis and LASSO Cox regression analysis, a prognostic model containing 7 PRGs was constructed. Kaplan–Meier survival analysis indicated that patients in the low-risk subgroup exhibited better prognosis compared to those in the high-risk subgroup. Additionally, the area under the curve (AUC) of ROC is 0.60, 0.63, and 0.73 for 1-, 3-, and 5-year survival in TCGA cohort and 0.63, 0.65, and 0.64 in validation set. TIMER database showed a strong correlation between 7 PRGs and tumor microenvironment in COAD. Moreover, CIBERSORT showed significant differences in the infiltration of plasma cells, M0 macrophages, resting dendritic cells, and eosinophils between low-risk subgroup and high-risk subgroup. HPA database showed that protein expression level of SDHB, GZMA, BTK, EEF2K, and NR1H2 was higher in normal tissues. And the transcriptional level of CASP5, BTK, SDHB, GZMA, and RIPK3 was high in normal tissues.

Conclusions

Our study identified a novel PRGs signature that could be used to predict the prognosis of COAD patients, which might provide a new therapeutic target for the treatment of COAD patients.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Distinct Hypoxia-Related Gene Profiling Characterizes Clinicopathological Features and Immune Status of Mismatch Repair-Deficient Colon Cancer

Despite dramatic responses to immune checkpoint inhibitors (ICIs) in patients with colon cancer (CC) harboring deficient mismatch repair (dMMR), more than half of these patients ultimately progress and experience primary or secondary drug resistance. There is no useful biomarker that is currently validated to accurately predict this resistance or stratify patients who may benefit from ICI-based immunotherapy. As hypoxic and acidic tumor microenvironment would greatly impair tumor-suppressing functions of tumor-infiltrating lymphocytes (TILs), we sought to explore distinct immunological phenotypes by analysis of the intratumoral hypoxia state using a well-established gene signature. Based on the Gene Expression Omnibus (GEO) (n = 88) and The Cancer Genome Atlas (TCGA) (n = 49) databases of patients with CC, we found that dMMR CC patients could be separated into normoxia subgroup (NS) and hypoxia subgroup (HS) with different levels of expression of hypoxia-related genes (lower in NS group and higher in HS group) using NMF package. Tumoral parenchyma in the HS group had a relatively lower level of immune cell infiltration, particularly CD8+ T cells and M1 macrophages than the NS group, and coincided with higher expression of immune checkpoint molecules and C-X-C motif chemokines, which might be associated with ICI resistance and prognosis. Furthermore, three genes, namely, MT1E, MT2A, and MAFF, were identified to be differentially expressed between NS and HS groups in both GEO and TCGA cohorts. Based on these genes, a prognostic model with stable and valuable predicting ability has been built for clinical application. In conclusion, the varying tumor-immune microenvironment (TIME) classified by hypoxia-related genes might be closely associated with different therapeutic responses of ICIs and prognosis of dMMR CC patients.