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High SEC61G Expression Predicts Poor Prognosis

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High SEC61G Expression Predicts Poor Prognosis 1 High SEC61G Expression Predicts Poor Prognosis in Patients with Head and 2 Neck Squamous Cell Carcinomas 3 4 Leifeng Liang1,*, Qingwen Huang2,*, Mei Gan1, Liujun Jiang1, Haolin Yan1, Zhan 5 Lin1, Haisheng Zhu1, Rensheng Wang3, Kai Hu3 6 7 1. Department of Oncology, The Sixth Affiliated Hospital of Guangxi Medical 8 University, Yulin, Guangxi, China 9 2. Department of Pathology, The Sixth Affiliated Hospital of Guangxi Medical 10 University, Yulin, Guangxi, China 11 3. Department of Radiation Oncology, The First Affiliated Hospital of Guangxi 12 Medical University, Nanning, Guangxi, China 13 14 *These authors contributed equally to this work. 15 16 Corresponding authors: Kai Hu, M.D and Prof. Rensheng Wang. Department of 17 Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, 6 18 Shuangyong Road, Nanning 530021, Guangxi, China; Email: 19 [email protected] (RW); [email protected] (KH) 20 21 Keywords: SEC61G; prognosis; biomarker; head and neck squamous cell carcinoma 22 23 Abstract 24 Background: Overexpression of the membrane protein SEC61 translocon gamma 25 subunit (SEC61G) has been observed in a variety of cancers; however, its role in head 26 and neck squamous cell carcinomas (HNSCC) is unknown. This study aimed to 27 elucidate the relationship between SEC61G and HNSCC based on data from The 28 Cancer Genome Atlas (TCGA) database. 29 Methods: Data for HNSCC patients were collected from TCGA and the expression 30 level of SEC61G was compared between paired HNSCC and normal tissues using the 31 Wilcoxon rank-sum test. The relationship between clinicopathologic features and 32 SEC61G expression was also analyzed using the Wilcoxon rank-sum test and logistic 33 regression. Receiver operating characteristic (ROC) curves were generated to evaluate 34 the value of SEC61G as a binary classifier using the area under the curve (AUC) value. 35 The association of clinicopathologic characteristics with prognosis in HNSCC patients 36 was assessed using Cox regression and the Kaplan–Meier method. A nomogram, based 37 on Cox multivariate analysis, was used to predict the impact of SEC61G on prognosis. 38 Functional enrichment analysis was performed to determine the hallmark pathways 39 associated with differentially expressed genes in HNSCC patients exhibiting high and 40 low SEC61G expression. 41 Results: The expression of SEC61G was significantly elevated in HNSCC tissues 42 compared to normal tissues (P < 0.001). High-level expression of SEC61G was 43 significantly correlated with the T stage, M stage, clinical stage, TP53 mutation status, 44 PIK3CA mutation status, primary therapy outcome, and lymph node neck dissection 45 (all P < 0.05). Meanwhile, ROC curves suggested the significant diagnostic ability of 46 SEC61G for HNSCC (AUC = 0.923). Kaplan–Meier survival analysis showed that 47 patients with HNSCC characterized by high SEC61G expression had a poorer prognosis 48 than patients with low SEC61G expression (hazard ratio = 1.95, 95% confidence 49 interval 1.48–2.56, P < 0.001). Univariate and multivariate analyses revealed that 50 SEC61G was independently associated with overall survival (P = 0.027). Functional 51 annotations indicated that SEC61G is involved in pathways related to translation and 52 regulation of SLITs/ROBOs expression, SRP-dependent co-translational protein 53 targeting to the membrane, nonsense-mediated decay, oxidative phosphorylation, and 54 Parkinson’s disease. 55 Conclusion: SEC61G plays a vital role in HNSCC progression and prognosis; it may, 56 therefore, serve as an effective biomarker for the prediction of patient survival. 57 58 INTRODUCTION 59 HNSCC, including oral, oropharyngeal, and laryngeal cancers, is among the top 10 60 most common diseases worldwide, with more than 65,000 new cases and 61 approximately 15,000 deaths, each year in the United States alone [1]. Despite active 62 comprehensive treatment and recent advances in therapeutic options, the prognosis for 63 HNSCC patients remains unsatisfactory, with a 5-year survival rate estimated at 65%, 64 or lower in the case of advanced disease [1]. The low survival rates associated with 65 HNSCC are due in part to the failure in early diagnosis, which is primarily attributed 66 to a lack of appropriate screening and diagnostic biomarkers [2, 3]. Although various 67 biomarkers have been applied for HNSCC, such as human papillomavirus status and 68 programmed cell death protein 1 [4], their reliability remains controversial. Thus, 69 identification of new biomarkers related to tumor stage and prognosis is exceedingly 70 important to facilitate early diagnosis, prognosis evaluation, and treatment of HNSCC. 71 SEC61G is one of three subunits comprising the SEC61 membrane protein complex 72 [5], which represents the central module of the protein translocation apparatus in the 73 endoplasmic reticulum membrane [6]. SEC61G participates in protein folding, 74 modification, translocation, and the unfolded protein response, particularly under 75 conditions of hypoxia and nutrient deprivation in the tumor microenvironment [7-9]. 76 SEC61G was also found to be overexpressed in gastric cancer [10], breast cancer [11], 77 and glioblastoma [12]. However, the association of SEC61G with HNSCC has not yet 78 been characterized. 79 In this study, we sought to demonstrate the correlation between SEC61G and 80 HNSCC and to analyze the prognostic role of SEC61G in HNSCC based on RNA- 81 sequencing (RNA-seq) data from TCGA. Moreover, we analyzed the expression level 82 of SEC61G in HNSCC and normal tissues and determined the correlation between 83 SEC61G expression and patient prognosis in terms of overall survival (OS). Further, 84 we performed prognosis and clinical correlations to explore the potential diagnostic 85 and prognostic value of SEC61G; while its biological significance was defined using 86 enrichment analysis, molecular interaction network analysis and immune infiltration 87 correlation analysis. Taken together, our study suggests that SEC61G represents a 88 significant independent predictor for HNSCC. 89 90 MATERIALS AND METHODS 91 92 RNA-seq data source 93 A total of 501 HNSCC cases with gene expression data (HTSeq-FPKM) were 94 collected from TCGA. Samples with RNA-seq data that were lacking corresponding 95 clinical data were excluded from the analysis. Level-3 HTSeq-FPKM data were 96 transformed into transcripts per million reads (TPM) for subsequent analyses. After 97 filtering, the TPM data for 500 HNSCC patients were further analyzed. Gene 98 expression data were divided into a high expression group and a low expression group 99 according to the median SEC61G expression level. This study met the publication 100 guidelines stated by TCGA (https://cancer genome.nih.gov/publications/publication 101 guidelines). All data used were acquired from TCGA, and hence, ethical approval and 102 informed consent of the patients was not required. 103 104 Pathological sample collection 105 Samples were collected for paired tumor and normal tissues from 60 patients with 106 HNSCC diagnosed between January 2015 and December 2015. All patient-derived 107 information and specimens were collected and archived under the protocols approved 108 by the Institutional Review Boards of The Sixth Affiliated Hospital of Guangxi 109 Medical University(Approve No.YLSY-IRB-CR-2020087). This study was 110 performed in accordance with the principles of the Declaration of Helsinki. 111 112 Immunohistochemistry study 113 Immunohistochemistry (IHC) was performed on formalin-fixed, paraffin-embedded 114 tissue sections. SEC61G was detected with the rabbit anti-SEC61G polyclonal 115 antibody (11147-2-AP, Proteintech Group Inc., Wuhan Sanying Biotechnology 116 Development Co. Ltd.). Paraffin slices were dewaxed in xylene. A gradient alcohol 117 dehydration process was then performed consisting of 100%, 95%, 80%, and 70% 118 ethanol (2 min each). The tissues were subsequently rinsed with distilled water twice 119 for 5 min each, and with phosphate-buffered saline (PBS) thrice, for 5 min each. Next, 120 pressure cooker–mediated antigen retrieval was performed in EDTA buffer, pH 9.0 121 (Fuzhou Maixin Biotechnology Development Co. Ltd.) for 30 min. Sections were 122 incubated with a 1:100 dilution of anti-SEC61G antibody for 30 min at 37 ℃ and 123 subsequently incubated with enzyme-labeled anti-mouse/rabbit lgG polymer (Fuzhou 124 Maixin Biotechnology Development Co. Ltd.) for 30 min at 25 ℃. After rinsing with 125 PBS three times for 5 min each, the sections were incubated with DAB chromogenic 126 reagent (Fuzhou Maixin Biotechnology Development Co., Ltd.) for 5 min, followed 127 by counterstaining with Mayer’s hematoxylin, dehydration, and mounting. 128 Two independent single-blinded pathologists assessed the sections and applied a 129 semiquantitative scoring system [13] to evaluate staining intensity (0, no staining; 1+, 130 weak staining; 2+, moderate staining; 3+, strong staining) and the percentage of 131 stained cells (0, < 5%; 1, 5%–25%; 2, 26%–50%; 3, 51%–75%; and 4, > 75%).The 132 scores for staining intensity and the percentage of positive cells were multiplied to 133 generate the immunoreactivity score for each case [14]. All cases were sorted into two 134 groups according to the immunoreactivity score. Positive of SEC61G was defined as 135 detectable immunoreactions in the cytoplasmic or membrane with an 136 immunoreactivity score of ≥ 1. 137 138 Enrichment analysis 139 The Search Tool for the Retrieval of Interacting Genes (STRING; http://strin g- 140 db.org; version 10.0) online database was used to predict the protein-protein 141 interaction network of SEC61G co-expressed genes and to analyze the functional 142 interactions among proteins [15]. An interaction with a combined score > 0.4 was 143 considered statistically significant. Expression profiles (HTSeq-Counts) were 144 compared between the high and low SEC61G expression groups to identify 145 differentially expressed genes (DEGs) using the Wilcoxon rank-sum test within 146 DESeq2 (3.8) software using R [16]. Differences with a |log fold change (FC)|>1.5 147 and adjusted P-value < 0.05 were considered as threshold values for identifying DEGs 148 [17-20].
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