Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the world's most common malignancies. Epigenetics is the study of heritable changes in characteristics beyond the DNA sequence. Epigenetic information is essential for maintaining specific expression patterns of genes and the normal development of individuals, and disorders of epigenetic modifications may alter the expression of oncogenes and tumor suppressor genes and affect the development of cancer. This study elucidates the relationship between epigenetics and the prognosis of CRC patients by developing a predictive model to explore the potential value of epigenetics in the treatment of CRC.

METHODS

Gene expression data of CRC patients' tumor tissue and controls were downloaded from GEO database. Combined with the 720 epigenetic-related genes (ERGs) downloaded from EpiFactors database, prognosis-related epigenetic genes were selected by univariate cox and LASSO analyses. The Kaplan-Meier and ROC curve were used to analyze the accuracy of the model. Data of 238 CRC samples with survival data downloaded from the GSE17538 were used for validation. Finally, the risk model is combined with the clinical characteristics of CRC patients to perform univariate and multivariate cox regression analysis to obtain independent risk factors and draw nomogram. Then we evaluated the accuracy of its prediction by calibration curves.

RESULTS

A total of 2906 differentially expressed genes (DEGs) were identified between CRC and control samples. After overlapping DEGs with 720 ERGs, 56 epigenetic-related DEGs (DEERGs) were identified. Combining univariate and LASSO regression analysis, the 8 epigenetic-related genes-based risk score model of CRC was established. The ROC curves and survival difference of high and low risk groups revealed the good performance of the risk score model based on prognostic biomarkers in both training and validation sets. A nomogram with good performance to predict the survival of CRC patients were established based on age, NM stage and risk score. The calibration curves showed that the prognostic model had good predictive performance.

CONCLUSION

In this study, an epigenetically relevant 8-gene signature was constructed that can effectively predict the prognosis of CRC patients and provide potential directions for targeted therapies for CRC.

Bioinformatics-based identification of lipid- and immune-related biomarkers in abdominal aortic aneurysms

Background

Abdominal aortic aneurysm (AAA) manifest as a natural inflammatory process with permanent dilation and terminal rupture. Nevertheless, the pathogenesis of AAA remains a mystery, and treatment is still controversial. Lipid metabolism and immune system are involved in AAA progression, which has been well documented. However, lipid- and immune-related (LIR) biomarkers need to be further elucidated.

Methods

The AAA-related datasets were retrieved from the GEO database, and the datasets were analyzed for differential gene expression by NetworkAnalyst. GO and KEGG pathway enrichment analysis of differentially expressed mRNA (DE-mRNA) was performed using Metscape, and LIR DE-mRNA was further screened. AAA rat model was constructed using porcine pancreatic elastase to verify the differential expression of LIR DE-mRNA.

Results

The GSE47472 and GSE57691 datasets respectively identified 614 (containing 381 down-regulated and 233 up-regulated DE-mRNAs) and 384 (containing 218 down-regulated and 164 up-regulated DE-mRNAs) DE-mRNAs. Intersection and union of DE-mRNAs were 13 and 983, respectively. The main terms involved in the union of DE-mRNAs included "immune system process", "metabolic process", "Chemokine signaling pathway", "hematopoietic cell lineage" and "Cholesterol metabolism". In vivo experiments revealed that LIR DE-mRNAs of PDIA3, TYROBP, and HSPA1A were significantly low expression in AAA abdominal aortic tissues, and HCK and SERPINE1 were significantly high expression, which is consistent with the bioinformatics analysis.

Conclusions

PDIA3, TYROBP, HSPA1A, HCK and SERPINE1 may serve as LIR biomarkers of AAA, which provides new insights and theoretical guidance for the future treatment, early prevention and progression of AAA.

Identification of the prognostic signature based on genomic instability-related alternative splicing in colorectal cancer and its regulatory network

Background: Colorectal cancer (CRC) is a heterogeneous disease with many somatic mutations defining its genomic instability. Alternative Splicing (AS) events, are essential for maintaining genomic instability. However, the role of genomic instability-related AS events in CRC has not been investigated.

Methods: From The Cancer Genome Atlas (TCGA) program, we obtained the splicing profiles, the single nucleotide polymorphism, transcriptomics, and clinical information of CRC. Combining somatic mutation and AS events data, a genomic instability-related AS signature was constructed for CRC. Mutations analyses, clinical stratification analyses, and multivariate Cox regression analyses evaluated this signature in training set. Subsequently, we validated the sensitivity and specificity of this prognostic signature using a test set and the entire TCGA dataset. We constructed a nomogram for the prognosis prediction of CRC patients. Differentially infiltrating immune cells were screened by using CIBERSORT. Inmmunophenoscore (IPS) analysis was used to evaluate the response of immunotherapy. The AS events-related splicing factors (SF) were analyzed by Pearson’s correlation. The effects of SF regulating the prognostic AS events in proliferation and migration were validated in Caco2 cells.

Results: A prognostic signature consisting of seven AS events (PDHA1-88633-ES, KIAA1522-1632-AP, TATDN1-85088-ES, PRMT1-51042-ES, VEZT-23786-ES, AIG1-77972-AT, and PHF11-25891-AP) was constructed. Patients in the high-risk score group showed a higher somatic mutation. The genomic instability risk score was an independent variable associated with overall survival (OS), with a hazard ratio of a risk score of 1.537. The area under the curve of receiver operator characteristic curve of the genomic instability risk score in predicting the OS of CRC patients was 0.733. Furthermore, a nomogram was established and could be used clinically to stratify patients to predict prognosis. Patients defined as high-risk by this signature showed a lower proportion of eosinophils than the low-risk group. Patients with low risk were more sensitive to anti-CTLA4 immunotherapy. Additionally, HSPA1A and FAM50B were two SF regulating the OS-related AS. Downregulation of HSPA1A and FAM50B inhibited the proliferation and migration of Caco2 cells.

Conclusion: We constructed an ideal prognostic signature reflecting the genomic instability and OS of CRC patients. HSPA1A and FAM50B were verified as two important SF regulating the OS-related AS.

Immune-Related lncRNAs to Construct Novel Signatures and Predict the Prognosis of Rectal Cancer

Colorectal cancer (CRC) is one of the most common cancers. Almost 1/3 of CRC are rectal cancer, and 95% of rectal cancers are rectal adenocarcinoma (READ). Increasing evidences indicated that long noncoding RNAs (lncRNAs) have important role in the genesis and development of cancers. The purpose of our present study was to identify the differential expression lncRNAs which potentially related with immune cells infiltration and establish a risk assessment model to predict the clinical outcome for READ patients. We obtained three immune-related differential expression lncRNAs (IR-DELs) (C17orf77, GATA2-AS1, and TPT1-AS1) by differential expression analysis following correlation analysis and Cox regression analysis. A risk assessment model were constructed by integrating these analysis results. We then plotted the 1-, 3-, and 5-year ROC curves depending on our risk assessment model, which suggested that all AUC values were over 0.7. In addition, we found that the risk assessment model was correlated with several immune cells and factors. This study suggested that those three signatures (C17orf77, GATA2-AS1, and TPT1-AS1) screened by pairing IR-DELs could be prognosis markers for READ patients and might benefit them from antitumor immunotherapy.