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To investigate the expression level of microRNA-101-3p (miR-101-3p) and its possible association with progression, prognosis and chemotherapy in patients with non-small cell lung cancer (NSCLC), the Gene Expression Omnibus (GEO) database was used. Quantitative polymerase chain reaction was used to verify the expression in 327 NSCLC and 42 adjacent normal lung tissues, of which 42 viable tissues were paired with nearby normal lung tissues. Based on the Cox regression model, univariate and multivariate analyses were used to address the factors that had effects on overall survival (OS) and disease-free survival (DFS) rate. Data from the GEO database demonstrated that the miR-101-3p expression in NSCLC was downregulated, compared with normal lung cancer. Survival analysis through univariate and multivariate models indicated that the miR-101-3p expression level was a crucial risk factor for OS and DFS in patients with NSCLC. A number of clinical parameters were determined to be associated with miR-101-3p expression, including tumor diameter, lymph node metastasis and tumor-node-metastasis stage. Adjuvant chemotherapy with high expression of miR-101-3p was determined to increase OS and DFS in patients with NSCLC, compared with patients with de novo or low expression of miR-101-3p. The present results demonstrated that miR-101-3p expression levels were associated with NSCLC progression and prognosis, which indicated that miR-101-3p may serve as a biomarker for patients with NSCLC who have received adjuvant chemotherapy.

// Ying-Chun Song 1, * , Gai-Xia Lu 1, * , Hong-Wei Zhang 2, * , Xiao-Ming Zhong 3, * , Xian-Ling Cong 4, * , Shao-Bo Xue 1, * , Rui Kong 1, * , Dan Li 1 , Zheng-Yan Chang 5 , Xiao-Feng Wang 6 , Yun-Jie Zhang 6 , Ran Sun 4 , Li Chai 1 , Ru-Ting Xie 5 , Ming-Xiang Cai 1 , Ming Sun 1 , Wei-Qing Mao 1 , Hui-Qiong Yang 5 , Yun-Chao Shao 6 , Su-Yun Fan 1 , Ting-Miao Wu 1 , Qing Xia 6 , Zhong-Wei Lv 1 , David A. Fu 6 and Yu-Shui Ma 1, 7 1 Department of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China 2 Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China 3 Department of Radiology, Jiangxi Provincial Tumor Hospital/Ganzhou City People’s Hospital, Nanchang 330029, China 4 Department of Biobank, China-Japan Unoin Hospital, Jilin University, Changchun 130033, China 5 Department of Pathology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China 6 Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China 7 Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, College of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China * These authors have contributed equally to this work Correspondence to: Zhong-Wei Lv, email: shtjnmd@163.com David A. Fu, email: 061101021@fudan.edu.cn Yu-Shui Ma, email: mayushui2015@126.com Keywords: GBM, glioma, proteomics analysis, gene expression analysis, Bioinformatics Analysis Received: May 24, 2017 Accepted: August 26, 2017 Published: October 19, 2017 ABSTRACT Glioblastoma multiforme (GBM), the most aggressive and lethal primary brain tumor, is characterized by very low life expectancy. Understanding the genomic and proteogenomic characteristics of GBM is essential for devising better therapeutic approaches.Here, we performed proteomic profiling of 8 GBM and paired normal brain tissues. In parallel, comprehensive integrative genomic analysis of GBM was performed in silico using mRNA microarray and sequencing data. Two whole transcript expression profiling cohorts were used - a set of 3 normal brain tissues and 22 glioma tissue samples and a cohort of 5 normal brain tissues and 49 glioma tissue samples. A validation cohort included 529 GBM patients from The Cancer Genome Atlas datasets. We identified 36 molecules commonly changed at the level of the gene and protein, including up-regulated TGFBI and NES and down-regulated SNCA and HSPA12A. Single amino acid variant analysis identified 200 proteins with high mutation rates in GBM samples. We further identified 14 differentially expressed genes with high-level protein modification, among which NES and TNC showed differential expression at the protein level. Moreover, higher expression of NES and TNC mRNAs correlated with shorter overall survival, suggesting that these genes constitute potential biomarkers for GBM.