Prognostic significance of promyelocytic leukemia expression in gastrointestinal stromal tumor; integrated proteomic and transcriptomic analysis

Abstract

Prognostic markers are urgently needed to optimize the postoperative treatment strategies for gastrointestinal stromal tumors (GIST). GIST of the small intestine (I-GIST) show more aggressive behavior than those of the stomach (S-GIST), and the molecular background of the malignancy in I-GIST may include potential prognostic biomarkers. We conducted integrated proteomic and transcriptomic analysis to identify genes showing differential expressions according to the tumor site. We generated protein expression profiles for four cases each of surgically resected I-GIST and S-GIST using label-free proteomic analysis. For proteins showing differential expressions, global mRNA expression was compared between 9 I-GIST and 23 S-GIST. Among the 2555 genes analyzed, we found that promyelocytic leukemia (PML), a tumor suppressor gene, was significantly downregulated in I-GIST at both the protein and mRNA levels (P < 0.01; fold difference ≥2.0). Immunohistochemistry of 254 additional cases from multiple clinical facilities showed that PML-negative cases were significantly frequent in the I-GIST group (P < 0.001). The 5-year recurrence-free survival rate was significantly lower in the PML-negative than in the PML-positive cases (60.1% vs 91.7%; P < 0.001). Multivariate analysis revealed that downregulation of PML was an independent unfavorable prognostic factor (hazard ratio = 2.739; P = 0.001). Our study indicated that prognostication based on PML expression may have potential for optimizing the treatment strategy for GIST patients. Further validation studies of PML for clinical application, and investigation for the mechanistic significance of PML to clarify the molecular backgrounds of malignancy in GIST are warranted.

Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset

“…We thus used a common, simple statistical test for protein classification, based either on the beta-binomial method for spectral count datasets [27], or on a modified t-test for datasets containing peptide intensity-based values (see Experimental section and below). Proteins were classified as variant or non-variant by a combined filtering on the p-value of this statistical test and on the fold change value, as very often performed in "real life " biological studies [31][32][33][34]. Following such classification, the sensitivity of the workflows for the detection of…”

Differential Expression of CADM1 in Gastrointestinal Stromal Tumors of Different Sites and with Different Gene Abnormalities

“…Small intestinal GISTs usually show a higher malignant potential than gastric GISTs [ 11 – 13 ]. Distinct transcription profiles related to the anatomical location in GISTs have been reported [ 14 , 15 ]. Hierarchical clustering analysis of the transcripts shows that GISTs are roughly separated into two groups such as gastric GISTs and small intestinal GISTs, and the result may partially explain why GISTs of the small intestine show more aggressive behavior than those of the stomach, with similar size and mitotic rate.…”

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

“…developed a prognostic model based on Ki-67, CD44 and PTEN expression that showed excellent prediction of disease-specific survival in patients with GISTs ( 20 ). Hiroshi’s team used the proteome and transcriptome to reveal the prognostic features of patients with GISTs at a multiomics level ( 21 ).…”