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Identification of potential plasma biomarkers for esophageal squamous cell carcinoma by a proteomic method.

ABSTRACT: Among malignant tumors, the mortality rate of esophageal squamous cell carcinoma (ESCC) ranks sixth in the world. Late-stage diagnosis of ESCC increases the mortality. Therefore, more effective biomarkers for early diagnosis of ESCC are necessary. Unfortunately, appropriate biomarkers for clinical diagnosis and prognosis have not been identified yet. However, recent progresses in quantitative proteomics have offered opportunities to identify plasma proteins as biomarkers for ESCC. In the present study, plasma samples were analyzed by differential in-gel electrophoresis (DIGE) and differentially expressed proteins were identified by matrix assisted laser desorption ionization-time of flight/time of flight mass spectrometry (MALDI-TOF/TOF MS). A total of 31 proteins representing 12 unique gene products were identified, in which 16 proteins were up-regulated and 15 down-regulated in tumors. The up-regulated proteins were alpha-2-HS-glycoprotein (AHSG), leucine-rich alpha-2-glycoprotein (LRG), zinc-alpha-2-glycoprotein, alpha-1-antichymotrypsin, complement factor I and complement C4-B, whereas the down-regulated proteins were serum albumin, Ig alpha-2 chain C region, alpha-1-antitrypsin, fibrinogen gamma chain, haptoglobin and hemoglobin subunit alpha. Among all the differentially expressed proteins, AHSG and LRG were validated by ELISA. The results were consistent with the data from the proteomics results, further suggesting that AHSG and LRG may be employed as potential biomarkers for the early diagnosis of ESCC. In summary, this study was the first time to use DIGE combined MALDI-TOF/TOF platform to identify the potential plasma biomarkers for ESCC. The plasma AHSG and LRG showed great potential for ESCC screening.

SUBMITTER: Zhao J

PROVIDER: S-EPMC4396278 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Project description:The poor outcome of patients with esophageal squamous cell carcinoma (ESCC) highlights the importance of the identification of novel effective prognostic biomarkers. Long non‑coding RNAs (lncRNAs) serve regulatory roles in various types of cancer. The aim of the present study was to investigate the lncRNA expression profile in ESCC and to identify lncRNAs associated with the prognosis of ESCC by performing comprehensive bioinformatics analyses. The RNA‑sequencing (Seq) expression dataset GSE53625 generated from ESCC samples was used as a training dataset. Additional RNA‑Seq datasets relative to ESCC samples were downloaded from The Cancer Genome Atlas and used as a validation dataset. Data were screened using the limma package, and differentially expressed lncRNAs between early‑ and late‑stage ESCC were identified. A random forest algorithm was used to select the optimal lncRNA biomarkers, which were then analyzed using the support vector machine (SVM) algorithm with R software. The identified lncRNA biomarkers were examined in the validation dataset by bidirectional hierarchical clustering and using an SVM classifier. Subsequently, univariate and multivariate Cox regression analyses were performed to analyze the potential ability lncRNAs to predict the survival rate of patients with ESCC. By examining the training group, 259 deregulated lncRNAs between early‑ and advanced‑stage ESCC were identified. Further bioinformatics analyses identified a nine‑lncRNA signature, including AC098973, AL133493, RP11‑51M24, RP11‑317N8, RP11‑834C11, RP11‑69C17, LINC00471, LINC01193 and RP1‑124C. This nine‑lncRNA signature was used to predict the tumor stage and patient survival rate with high reliability and accuracy in the training and validation datasets. Furthermore, these nine lncRNA biomarkers were primarily involved in regulating the cell cycle and DNA replication, and these processes were previously identified to be associated with the progression of ESCC. The identified nine‑lncRNA signature was identified to be associated with the tumor stage, and could be used as predictor of the survival rate of patients with ESCC.

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Identification of potential plasma biomarkers for esophageal squamous cell carcinoma by a proteomic method.

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