Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (6): 196-205.doi: 10.16088/j.issn.1001-6600.2021030702

Previous Articles     Next Articles

A Novel Protein Prognostic Model for Melanoma

CHEN Yajing1,2,3, ZHANG Tongwei1,4, ZHOU Jun1,2,3, CHEN Shuman1,2,3, PU Shiming1,2,3*   

  1. 1. College of Life Sciences, Guangxi Normal University, Guilin Guangxi 541006, China;
    2. Guangxi Universities Key Laboratory of Stem Cell and Biopharmaceutical Technology (Guangxi Normal University), Guilin Guangxi 541004, China;
    3. Research Center for Biomedical Science, Guangxi Normal University, Guilin Guangxi 541004, China;
    4. Guangxi Key Laboratory of Rare and Endangered Animal Ecology (Guangxi Normal University), Guilin Guangxi 541006, China
  • Received:2021-03-07 Revised:2021-06-02 Online:2022-11-25 Published:2023-01-17

PDF (PC)

43

Abstract: Melanoma is one of the most aggressive and difficult to treat cancers. Currently, Melanoma is mainly treated by using targeted and immunotherapeutic agents. However, their success is limited by the development of resistance, which curtails long-term response rates. Thus, novel prognostic approaches are needed to guide individualized treatment and improve outcomes. In this paper, a multicox analysis of TCPA and TCGA datasets is used to develop a prognostic model based on P21, YAP, X1433ZETA, CKIT, S6, CD20, LCK, P27, CD49B, GATA6 and SRC_pY416 proteins. Risk score analysis for each patient indicated that those with high risk scores had worse prognosis relative to those with low risk scores. Cox and multi-Cox model analyses revealed that prognosis correlated with risk scores. Co-expression analysis identified various proteins that were co-expressed with the prognostic model’s proteins. Overexpression of the low risk protein, GATA6, and the high risk protein X1433ZETA, in melanoma cells revealed tumor growth suppression by GATA6, and tumor growth promotion by X1433ZETA, in vitro and in vivo. In conclusion, this novel melanoma prognostic model can guide individualized treatment.

Key words: prognostic model, melanoma, proteomics, biological marker

CLC Number: 

  • R739.5
[1] JEMAL A, SARAIYA M, PATEL P, et al. Recent trends in cutaneous melanoma incidence and death rates in the United States, 1992-2006[J]. Journal of the American Academy of Dermatology, 2011, 65(5): S.e1-S17.e11. DOI: 10.1016/j.jaad.2011.04.032.
[2] GRAY-SCHOPFER V, WELLBROCK C, MARAIS R. Melanoma biology and new targeted therapy[J]. Nature, 2007, 445: 851-857. DOI: 10.1038/nature05661.
[3] DOMINGUES B, LOPES J M, SOARES P, et al. Melanoma treatment in review[J]. ImmunoTargets and Therapy, 2018, 7: 35-49. DOI: 10.2147/ITT.S134842.
[4] Van HALLT, ANDRÉ P, HOROWITZ A, et al. Monalizumab: inhibiting the novel immune checkpoint NKG2A[J]. Journal for Immunotherapy of Cancer, 2019, 7(1): 2633. DOI: 10.1186/s40425-019-0761-3.
[5] 王依凝, 魏文斌. 葡萄膜黑色素瘤的预后评估[J]. 中国医学前沿杂志(电子版), 2020, 12(12): 11-17. DOI: 10.12037/YXQY.2020.12-03.
[6] SOONG S J, DING S L, COIT D, et al. Predicting survival outcome of localized melanoma: an electronic prediction tool based on the AJCC Melanoma Database[J]. Annals of Surgical Oncology, 2010, 17(8): 2006-2014. DOI: 10.1245/s10434-010-1050-z.
[7] IPENBURG N A, NIEWEG O E, AHMED T, et al. External validation of a prognostic model to predict survival of patients with sentinel node-negative melanoma[J]. British Journal of Surgery, 2019, 106(10): 1319-1326. DOI: 10.1002/bjs.11262.
[8] BAADE P D, ROYSTON P, YOUL P H, et al. Prognostic survival model for people diagnosed with invasive cutaneous melanoma[J]. BMC Cancer, 2015, 15: 27. DOI: 10.1186/s12885-015-1024-4.
[9] LUO Q Z, ZHANG X B. Construction of protein-related risk score model in bladder. urothelial carcinoma[J]. BioMed Research International, 2020, (2020): 7147824. DOI: 10.1155/2020/7147824.
[10] WU Z H, YUN-TANG, CHENG Q. Data mining identifies six proteins that can act as prognostic markers for head and neck squamous cell carcinoma[J]. Cell Transplantation, 2020, 29: 1-8. DOI: 10.1177/0963689720929308.
[11] RONG D W, DONG Q, QU H J, et al. m6A-induced LINC00958 promotes breast cancer tumorigenesis via themiR-378a-3p/YY1 axis[J].Cell Death Discovery, 2021, 7(1): 27. DOI: 10.1038/s41420-020-00382-z.
[12] ZHU P P, HE F, HOU Y X, et al. A novel hypoxic long noncoding RNA KB-1980E6.3 maintains breast cancer stem cell stemness via interacting with IGF2BP1 to facilitate c-Myc mRNA stability[J]. Oncogene, 2021, 40(9): 1609-1627. DOI: 10.1038/s41388-020-01638-9.
[13] FRIEDMAN E B, SHANG S L, de MIERA E V S, et al. Serum microRNAs as biomarkers for recurrence in melanoma[J]. Journal of Translational Medicine, 2012, 10: 155. DOI: 10.1186/1479-5876-10-155.
[14] FANG X S, LIU X, WENG C Y, et al. Construction and validation of a protein prognostic model for lung squamous cell carcinoma[J]. International Journal of Medical Sciences, 2019, 17(17): 2718-2727. DOI: 10.7150/ijms.47224.
[15] TIAN X, XU W H, ANWAIER A, et al. Construction of a robust prognostic model for adult adrenocortical carcinoma: results from bioinformatics and real-world data[J]. Journal of Celluar and Molecular Medicine, 2021, 25(8): 3898-3911. DOI: 10.1111/jcmm.16323.
[16] 韩未, 沈国良. CDCA5在皮肤恶性黑色素瘤中的表达及其与预后相关性的生物信息学分析[J], 现代肿瘤医学, 2021, 29(7): 1234-1240. DOI: 10.3969/j.issn.1672-4992.2021.07.028.
[17] KOLLMAR O, RUPERTUS K, SCHEUER C, et al. Stromal cell-derived factor-1 promotes cell migration, tumor growth of colorectal metastasis[J]. Neoplasia, 2007, 9(10): 862-870. DOI: 10.1593/neo.07559.
[18] RUSSELL M R, GRAHAM C, D'AMATO A, et al. Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel[J]. British Journal of Cancer, 2019, 121(6): 483-489. DOI: 10.1038/s41416-019-0544-0.
[19] LI W, GAO L N, SONG P P, et al. Development and validation of a RNA binding protein-associated prognostic model for lung adenocarcinoma[J]. Aging, 2020, 12(4): 3558-3573. DOI: 10.18632/aging.102828.
[20] XU W H, ANWAIER A, MA C G, et al. Multi-omics reveals novel prognostic implication of SRC protein expression in bladder cancer and its correlation with immunotherapy response[J]. Annals of Medicine, 2021, 53(1): 596-610. DOI: 10.1080/07853890.2021.1908588.
[21] ZENG Q Z, YAO Y O, ZHAO M W. Development and validation of a nomogram to predict cancer-specific survival of uveal melanoma[J]. BMC Ophthalmology, 2021, 21(1): 230. DOI: 10.1186/s12886-021-01968-6.
[22] ZHANG M G, GAO F, YU X, et al. LINC00261: a burgeoning long noncoding RNA related to cancer[J]. Cancer Cell International, 2021, 21(1): 274. DOI: 10.1186/s12935-021-01988-8.
[23] HAN P H, ZHU J J, FENG G, et al. Characterization of alternative splicing events and prognostic signatures in breast cancer[J]. BMC Cancer, 2021, 21(1): 587. DOI: 10.1186/s12885-021-08305-6.
[24] ABBASPOUR B M, KAMALIDEHGHAN B, SALEEM M, et al. Receptor tyrosine kinase (c-Kit) inhibitors: a potential therapeutic target in cancer cells[J]. Drug Design, Development and Therapy, 2016, 10: 2443-2459. DOI: 10.2147/DDDT.S89114.
[25] KANG H G, MA D, ZHANG J, et al. Long non-coding RNA GATA6-AS1 upregulates GATA6 to regulate the biological behaviors of lung adenocarcinoma cells[J]. BMC Pulmonary Medicine, 2021, 21(1): 166. DOI: 10.1186/s12890-021-01521-7.
[26] HU D, ANSARI D, PAWŁOWSKI K, et al. Proteomic analyses identify prognostic biomarkers for pancreatic ductal adenocarcinoma[J]. Oncotarget, 2018, 9(11): 9789-9807. DOI: 10.18632/oncotarget.23929.
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] WU Lei, HUANG Yun-feng, NONG Dong-xin, XU Wei-bin. New Records of Orchids from Guangxi,China[J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(3): 57 -59 .
[2] RAN Hao, ZHOU Shan-yi. Checklist of Chinese Ants: the Formicomorph Subfamilies(Hymenoptera:Formicidae)(Ⅰ)[J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(3): 65 -73 .
[3] KUANG Xianyan, CHEN Ziru. Mixed Traffic Flow Model of Signalized Intersections Involving Pedestrian Comity[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 1 -15 .
[4] LIU Weiming, CHEN Gangmei, LIN Guanrong, LI Jingning. Coordination Control Method for Toll Station of Freeway and Adjacent Intersection[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 16 -26 .
[5] ZOU Yanli, WANG Yang, LIU Shusheng, YAO Fei. Study of Cascading Failure in the Grid under the Capacity Load Model with Neighbors Degree Information[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 27 -36 .
[6] XIE Lina, JIANG Pinqun, SONG Shuxiang, CEN Mingcan. A Low-Loss, Low-Noise, Wide-Tuned High-Order Cascade N-Path Filter[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 37 -44 .
[7] LUO Lan, ZHOU Nan, SI Jie. New Delay Partition Method for Robust Stability of Uncertain Cellular Neural Networks with Time-Varying Delays[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 45 -52 .
[8] WANG Jian, ZHENG Qifan, LI Chao, SHI Jing. Remote Supervision Relationship Extraction Based on Encoder and Attention Mechanism[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 53 -60 .
[9] XIAO Yiqun, SONG Shuxiang, XIA Haiying. Fast Pedestrian Detection Method Based on Multi-Features    and Implementation[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 61 -67 .
[10] WANG Xun, LI Tinghui, PAN Xiao, TIAN Yu. Image Segmentation Method Based on Improved Fuzzy C-means Clustering and Otsu Maximum Variance[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 68 -73 .
Copyright © Journal of Guangxi Normal University(Natural Science Edition), All Rights Reserved.
Tel: 0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
Powered by Beijing Magtech Co. Ltd