Single Nucleotide Polymorphisms in microRNA Genes and Colorectal Cancer Risk and Prognosis

There is growing interest in single nucleotide polymorphisms (SNPs) in the genes of microRNAs (miRNAs), which could be associated with susceptibility to colorectal cancer (CRC) and therefore for prognosis of the disease and/or treatment response. Moreover, these miRNAs-SNPs could serve as new, low-invasive biomarkers for early detection of CRC. In the present article, we performed a thorough review of different SNPs, which were investigated for a correlation with the CRC risk, prognosis, and treatment response. We also analyzed the results from different meta-analyses and the possible reasons for reported contradictory findings, especially when different research groups investigated the same SNP in a gene for a particular miRNA. This illustrates the need for more case-control studies involving participants with different ethnic backgrounds. According to our review, three miRNAs-SNPs—miR-146a rs2910164, miR-27a rs895819 and miR-608 rs4919510—appear as promising prognostic, diagnostic and predictive biomarkers for CRC, respectively.

Significance of Acute Phase Reactants as Prognostic Biomarkers for Pneumonia in Children

Community acquired pneumonia (CAP) is a major contributing cause for the increased rate of childhood mortalities and morbidities in the developing countries. Thus, we aimed in this study to appraise the significance of acute phase reactant proteins in correlation with the modified pneumonia prognostic score to assess the disease severity and outcome in children.This study included 40 children with pneumonia ( age between 6 months 2 years ) and other 40 healthy controls ( age and sex matched). They were enrolled in the study and a detailedhistory’s obtained, full clinical examination and assessment of AGP, CRP and ferritin in serum in addition to CBC.AGP, CRP and ferritin showed significant higher levels in children with pneumonia than healthy controls. Also, their values were significantly higher in nonsuvivors than survivors.The present investigation provides a distinct evidence for the prominence of acute phase reactants (ferritin, CRP and AGP) in comparison with the clinical scores in predicting early high risk prognosis of pneumonia in children.

Excavation of a Novel Transcription Factors-related Prognostic Signature for Osteosarcoma

BackgroundTranscription factors (TFs) are involved in the initiation and development of many cancers, regulating cancer-related activities. However, the significance of TF-related genes in predicting the prognosis of osteosarcoma (OS) patients is not yet clear. Risk stratification using prognostic markers can facilitate clinical decision-making and effect in the treatment of cancer.Material and methodsIn the study, we aimed to establish an optimal TF signature for predicting the prognosis of OS patients. We identified 24 differentially expressed TFs in metastatic and non-metastatic OS samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Subsequently, we performed univariate and multivariate cox regression analysis to built a TFs-related prognostic signature (TRPS) confirmed in an independent cohort (GSE39055). The ESTIMATE algorithm was used to estimate the immune/stromal cell score.ResultsWe built a TRPS for OS patients, including MESP1 and ZNF597. Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curve both confirmed the accuracy of the signature. Multivariate analysis proved that this TRPS was an independent prognostic predictor of OS, and it was further confirmed in the GSE39055 dataset and multiple clinical subtypes. In addition, we found a significant negative correlation between stromal score and risk score. Moreover, the relative abundance of NK cells in the low-risk prognosis group was notably higher than that in the high-risk prognosis group. ConclusionIn summary, we established a TFs-related prognostic signature with high diagnostic, prognostic efficacy in OS patients, which may optimize the prognostic management of osteosarcoma patients and help achieve individualized treatment.

Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

Background Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment. Methods We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts. Results Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database. Conclusions This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.