Importance of gene expression signatures in pancreatic cancer prognosis and the establishment of a prediction model

Abstract

Background and aimPancreatic cancer (PC) is one of the most common tumors with a poor prognosis. The current American Joint Committee on Cancer (AJCC) staging system, based on the anatomical features of tumors, is insufficient to predict PC outcomes. The current study is endeavored to identify important prognosis-related genes and build an effective predictive model.MethodsMultiple public datasets were used to identify differentially expressed genes (DEGs) and survival-related genes (SRGs). Bioinformatics analysis of DEGs was used to identify the main biological processes and pathways involved in PC. A risk score based on SRGs was computed through a univariate Cox regression analysis. The performance of the risk score in predicting PC prognosis was evaluated with survival analysis, Harrell’s concordance index (C-index), area under the curve (AUC), and calibration plots. A predictive nomogram was built through integrating the risk score with clinicopathological information.ResultsA total of 945 DEGs were identified in five Gene Expression Omnibus datasets, and four SRGs (LYRM1, KNTC1, IGF2BP2, and CDC6) were significantly associated with PC progression and prognosis in four datasets. The risk score showed relatively good performance in predicting prognosis in multiple datasets. The predictive nomogram had greater C-index and AUC values, compared with those of the AJCC stage and risk score.ConclusionThis study identified four new biomarkers that are significantly associated with the carcinogenesis, progression, and prognosis of PC, which may be helpful in studying the underlying mechanism of PC carcinogenesis. The predictive nomogram showed robust performance in predicting PC prognosis. Therefore, the current model may provide an effective and reliable guide for prognosis assessment and treatment decision-making in the clinic.

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

“…(Chowdhury et al, ) In the past decade, with the rapid development of microarray and RNA‐sequencing technology, more and more biomarkers of tumor initiation, progression, and prognosis have been identified using bioinformatics analysis. (Sun et al, ; Yan et al, ) In this study, we integrated three microarray datasets from gene expression omnibus (GEO) database (GSE781, GSE6344, and GSE100666) to identify the differentially expressed genes (DEGs) between KIRC and adjacent normal tissues, aiming to explore and determine the promising novel biomarkers associated with pathogenesis and prognosis of KIRC. Meanwhile, we revealed some candidate small molecule drugs that could reverse the gene expression of KIRC based on the CMap database.…”

A Prognostic Prediction Model Developed Based on Four CpG Sites and Weighted Correlation Network Analysis Identified DNAJB1 as a Novel Biomarker for Pancreatic Cancer

“…Therefore, the development of a risk stratification model may help clinicians to design personalized treatment programs for different patients. Previous studies have proposed various risk stratification models for the diagnosis, prognosis, and recurrence of pancreatic cancer, which exhibited better efficiency than the classical TNM stage (43)(44)(45). The rapid development of sequencing techniques has enabled the access to multi-omics data and high-quality clinical information through different databases, such as TCGA, ICGC, and Gene Expression Omnibus (17,46,47).…”

A novel 20-gene prognostic score in pancreatic adenocarcinoma

“…The coefficients supplied by the authors were used and median dichotomization was used to determine the high and low risk groups. Yan's signature [16] includes 4 genes, and the risk groups were calculated as described in the original manuscript. The third signature (Shi et al) [17] was adapted with an approximation method.…”

“…We applied these predictive signatures as they were described in their respective publications with modifications as described in the methods section. Two of these (Chen et al (Moffit) [11] & Yan et al [16]) were predictors of overall survival. The third signature that was compared to PPS20 was Shi et al's signature [17] which predicts recurrence free survival.…”

“…AJCC TNM staging and performance status are critical indicators of prognosis in clinical practice [8,9]. Although multiple gene expression based prognostic prediction methods have been developed [10][11][12][13][14][15][16], no such test is currently used in routine practice and very few of these tests can be used as predictors of response to therapy [17], or in vitro sensitivity to chemotherapeutic agents in cell lines [18]. Therefore there is still much to contribute to the discovery of new risk predictors and new methodologies for prognostic assessments that can guide clinical approaches.…”