Dataset Information

Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis.

ABSTRACT: Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset. By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy. Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients. A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC. It might complement clinicopathological features and facilitate personalized management of GC.

SUBMITTER: Tian X

PROVIDER: S-EPMC5595087 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis.

Tian Xianglong X Zhu Xiaoqiang X Yan Tingting T Yu Chenyang C Shen Chaoqin C Hong Jie J Chen Haoyan H Fang Jing-Yuan JY

Journal of Cancer 20170803 13

Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated i ...[more]

PMID: 28900495

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Project description:BackgroundGastric cancer (GC) is a malignancy with high morbidity/mortality, partly due to a lack of reliable biomarkers for early diagnosis. It is important to develop reliable biomarker(s) with specificity, sensitivity and convenience for early diagnosis. The role of tumour-associated macrophages (TAMs) and survival of GC patients are controversial. Macrophage colony stimulating factor (MCSF) regulates monocytes/macrophages. Elevated MCSF is correlated with invasion, metastasis and poor survival of tumour patients. IL-34, a ligand of the M-CSF receptor, acts as a "twin" to M-CSF, demonstrating overlapping and complimentary actions. IL-34 involvement in tumours is controversial, possibly due to the levels of M-CSF receptors. While the IL-34/M-CSF/M-CSFR axis is very important for regulating macrophage differentiation, the specific interplay between these cytokines, macrophages and tumour development is unclear.MethodsA multi-factorial evaluation could provide more objective utility, particularly for either prediction and/or prognosis of gastric cancer. Precision medicine requires molecular diagnosis to determine the specifically mutant function of tumours, and is becoming popular in the treatment of malignancy. Therefore, elucidating specific molecular signalling pathways in specific cancers facilitates the success of a precision medicine approach. Gastric cancer tissue arrays were generated from stomach samples with TNM stage, invasion depth and the demography of these patients (n = 185). Using immunohistochemistry/histopathology, M-CSF, IL-34 and macrophages were determined.ResultsWe found that IL-34 may serve as a predictive biomarker, but not as an independent, prognostic factor in GC; M-CSF inversely correlated with survival of GC in TNM III-IV subtypes. Increased CD68+ TAMs were a good prognostic factor in some cases and could be used as an independent prognostic factor in male T3 stage GC.ConclusionOur data support the potency of IL-34, M-CSF, TAMs and the combination of IL-34/TAMs as novel biological markers for GC, and may provide new insight for both diagnosis and cellular therapy of GC.

Project description:Gastric cancer is a common malignant tumor with high occurrence and recurrence and is the leading cause of death worldwide. However, the prognostic value of protein-coding and non-coding RNAs in stage III gastric cancer has not been systematically analyzed. In this study, using TCGA data, we identified 585 long noncoding RNAs (lncRNAs) and 927 protein-coding genes (PCGs) correlated with the overall survival rate of gastric cancer. Functional enrichment analysis revealed that the prognostic genes positively correlated with death rates were enriched in pathways, including gap junction, focal adhesion, cell adhesion molecules (CAMs), and neuroactive ligand-receptor interaction, that are involved in the tumor microenvironment and cell-cell communications, suggesting that their dysregulation may promote the tumor progression. To evaluate the performance of the prognostic genes in risk prediction, we built three multivariable Cox models based on prognostic genes selected from the prognostic PCGs and lncRNAs. The performance of the three models based on features from only PCGs or lncRNAs or from all prognostic genes were systematically compared, which revealed that the features selected from all the prognostic genes showed higher performance than the features selected only from lncRNAs or PCGs. Furthermore, the multivariable Cox regression analysis revealed that the stratification with the highest performance was an independent prognostic factor in stage III gastric cancer. In addition, we explored the underlying mechanism of the prognostic lncRNAs in the Cox model by predicting the lncRNA and protein interaction. Specifically, CTD-2218G20.2 was predicted to interact with PSG4, PSG5, and PSG7, which could also interact with cancer-related proteins, including KISS1, TIMP2, MMP11, IGFBP1, EGFR, and CDKN1C, suggesting that CTD-2218G20.2 might participate in the cancer progression via these cancer-related proteins. In summary, the systematic analysis of the prognostic lncRNAs and PCGs was of great importance to the understanding of the progression of stage III gastric cancer.

Dataset Information

Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis.

Publications

Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis.

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