A four-microRNA classifier as a novel prognostic marker for tumor recurrence in stage II colon cancer

Jacob, Havjin; Stanisavljevic, Luka; Storli, Kristian Eeg; Hestetun, Kjersti Elvestad; Dahl, Olav; Myklebust, Mette Pernille

doi:10.1038/s41598-018-24519-4

Cited by 38 publications

(27 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predictors in the nomogram included four independent prognostic factors (age, gender, tumour stage, and race (Fig. 4 a) [ 14 ]. The calibration curve illustrated that the predictions and actual observations matched well, which indicated an accurate prediction via the nomogram (Fig.…”

Section: Resultsmentioning

confidence: 99%

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

et al. 2020

View full text Add to dashboard Cite

Background: Kidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients. Results: We identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set. Conclusion: Our findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.

show abstract

Section: Resultsmentioning

confidence: 99%

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In view of the significant morbidity and mortality of gastrointestinal pan-adenocarcinomas, deeper mining and the development of prognostic signatures is urgent. Several researchers have proposed prognostic signatures for gastrointestinal pan-adenocarcinomas based on several types of molecules, such as lncRNAs [ 45 ], mRNAs [ 46 ], and miRNAs [ 47 , 48 ]. With the advances in high-throughput RNA-seq, TCGA dataset provides multiple resources for the investigation of AS events at the genome-wide level.…”

Section: Discussionmentioning

confidence: 99%

Systematic Analysis of Survival-Associated Alternative Splicing Signatures in Gastrointestinal Pan-Adenocarcinomas

Lin

et al. 2018

EBioMedicine

View full text Add to dashboard Cite

BackgroundGastrointestinal pan-adenocarcinomas, which mainly include adenocarcinomas of the esophagus, stomach, colon, and rectum, place a heavy burden on society owing to their poor prognoses. Since aberrant alternative splicing (AS) are starting to be considered as efficacious signatures for tumor prognosis predicting and therapeutic targets, systematic analysis of AS events is urgent.MethodsPrognosis-related AS events were selected by using univariate COX regression analysis. Gene functional enrichment analysis revealed the pathways enriched by prognosis-related AS. Then, prognostic signatures based on AS events were developed for prognosis prediction. Potential mechanism to regulate splicing events by splicing factors was analyzed via Pearson correlation and regulatory networks were constructed.FindingsA total of 967, 918, 674, and 406 AS events were identified as prognosis-related AS events in esophagus, stomach, colon, and rectum adenocarcinomas, respectively. Survival-associated AS events were distinguishing in the four subtypes of adenocarcinoma. Furthermore, computational algorithm results indicated that perturbation of ribosome and ubiquitin-mediated proteolysis pathways were the potential molecular mechanisms corresponding to inferior prognoses. Most notably, several prognostic signatures based on AS events displayed moderate performance in prognosis predicting. The area under curve values of the time-dependent receiver operating characteristic were 0.961, 0.871, 0.870, and 0.890 in esophagus, stomach, colon, and rectum adenocarcinomas. Survival-associated splicing factors were submitted to construct the AS regulatory network, which could be an underlying mechanism of AS events.InterpretationAS may could be ideal indiactors in the prognosis of gastrointestinal pan-adenocarcinomas. Exploring interesting splicing regulatory networks is conducive to solve the puzzles of AS.

show abstract

“…Candidate prognostic snoRNAs were then subjected to multivariate Cox regression. A survival-predicting algorithm PI, an index calculated for each patient according to their snoRNA expression pattern, was built according to the expression values of each independent snoRNA and weighted by the contribution of each snoRNA to OS (18). The 'survivalROC' package in R (https://CRAN.R-project.org/package=survivalROC) was used to evaluate the performance of the algorithm in predicting the prognosis of the HCC patients.…”

Section: Methodsmentioning

confidence: 99%

Genomic analysis of small nucleolar RNAs identifies distinct molecular and prognostic signature in hepatocellular carcinoma

Yang

Lin

et al. 2018

Oncol Rep

View full text Add to dashboard Cite

As one of the most lethal malignancies worldwide, hepatocellular carcinoma (HCC) has a high mortality rate, which is mainly due to the complex and multi-step aberrations in gene expression associated with it. Small nucleolar RNAs (snoRNAs), non-coding RNAs that are 60-300 nucleotides in length, have been proposed to be closely associated with numerous human diseases, including HCC. However, the current knowledge regarding their clinical significance and mechanistic roles in HCC is limited. The present study comprehensively analyzed the snoRNA expression profiles in HCC and identified several ones that were dysregulated. The potential regulatory mechanisms of these snoRNAs were assessed via gene functional enrichment analyses. Univariate and multivariate Cox regression analyses were performed to identify snoRNAs that are independently associated with the risk of mortality. Subsequently, a prognostic index (PI) for survival prediction was established, which may serve as a prognostic biomarker for patients with HCC (hazard ratio, 3.023; 95% confidence interval: 1.785-5.119; P<0.001). In addition, a series of bioinformatics analyses were performed to identify potential differences in the perturbation of pathways between high-and low-risk groups. The PI developed in the present study was determined to have a moderate predictive value regarding the clinical outcome for HCC patients.

show abstract

A four-microRNA classifier as a novel prognostic marker for tumor recurrence in stage II colon cancer

Cited by 38 publications

References 47 publications

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

Systematic Analysis of Survival-Associated Alternative Splicing Signatures in Gastrointestinal Pan-Adenocarcinomas

Genomic analysis of small nucleolar RNAs identifies distinct molecular and prognostic signature in hepatocellular carcinoma

Contact Info

Product

Resources

About