|Year : 2019 | Volume
| Issue : 3 | Page : 47-49
OSMCC: An online survival analysis tool for Merkel cell carcinoma
Umair Ali Khan Saddozai1, Qiang Wang1, Xiaoxiao Sun1, Yifang Dang1, JiaJia Lv2, Junfang Xin1, Wan Zhu3, Yongqiang Li1, Xinying Ji1, Xiangqian Guo1
1 Department of Preventive Medicine, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
2 Department of Preventive Medicine, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng; Department of Thoracic Surgery, The Affiliated Nanshi Hospital of Henan University, Nanyang, China
3 Department of Anesthesia, Stanford University, Pasteur Drive Stanford, CA, USA
|Date of Submission||23-Apr-2019|
|Date of Acceptance||29-Jul-2019|
|Date of Web Publication||30-Sep-2019|
Prof. Xinying Ji
Department of Preventive Medicine, Joint National Laboratory for Antibody Drug Engineering, Cell Signal Transduction Laboratory, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng
Source of Support: None, Conflict of Interest: None
Aims: To develop a free accessible online tool to identify the prognostic markers for Merkel cell carcinoma (MCC) and to estimate the significance of interested gene in a cohort of clinical patients.
Settings and Design: R package is used to calculate and plot the Kaplan–Meier survival curve.
Subjects and Methods: An online search engine was developed by combining MCC datasets with available anatomoclinical data in Gene Expression Omnibus. In current study, genomic expression profile of thirty patients comprising 42985 probes and 21651 genes was evaluated. Patients were divided into first quartile, second quartile, and third quartile. Information about different cancer patients of varying stages (Stage I–IV) was stored using median survival scale of 14.5 months. Data were stored in SQL Server database and hosted on Windows Server 2008 using Apache Tomcat application server.
Statistical Analysis Used: Log-rank test was applied and P < 0.05 was considered statistically significant.
Results: An Online Survival analysis tool for MCC abbreviating as OSMCC was developed, which can assess the expression level relevance of various genes on the clinical outcome in MCC patients. By OSMCC, the survival curve could be displayed, and the hazard ratio with 95% confidence intervals and log-rank P value can also be calculated.
Conclusions: The study demonstrated the ability of OSMCC to identify and analyze transcriptome and clinical datasets for MCC through prognosis significance analysis. So far, OSMCC is the first advanced and specific tool for the prognostic measurement of MCC. Furthermore, OSMCC can prove to be a highly valuable database for the preliminary assessment and identification of potential MCC prognostic biomarkers. OSMCC is accessible at http://bioinfo.henu.edu.cn/MCC/MCCList.jsp.
Keywords: Bioinformatics, Merkel cell carcinoma, microarray, prognosis
|How to cite this article:|
Saddozai UA, Wang Q, Sun X, Dang Y, Lv J, Xin J, Zhu W, Li Y, Ji X, Guo X. OSMCC: An online survival analysis tool for Merkel cell carcinoma. Cancer Transl Med 2019;5:47-9
|How to cite this URL:|
Saddozai UA, Wang Q, Sun X, Dang Y, Lv J, Xin J, Zhu W, Li Y, Ji X, Guo X. OSMCC: An online survival analysis tool for Merkel cell carcinoma. Cancer Transl Med [serial online] 2019 [cited 2019 Oct 23];5:47-9. Available from: http://www.cancertm.com/text.asp?2019/5/3/47/268228
| Introduction|| |
In recent decades, researchers have been exploring the molecular base of disease by gene microarray-based transcriptome analysis. Analyzing transcriptome permits oncologists to evaluate the biochemical pathways and regulatory mechanisms associated with transformation of tumors. Gene microarray can also allow identifying novel diagnostic and prognostic biomarkers and therapeutic targets. As a result, increased amounts of DNA microarray data are being generated by the research community with the help of Microarray Gene Expression Data (MGED) Society. Researchers are encouraged by the MGED Society to deposit their data in public depositories which follow the guidelines of Minimum Information about a Microarray Experiment., Complete microarray datasets, either in the form of supplementary data in publications or in public databases including Gene Expression Omnibus (GEO) or Array Express, are soon going to be available when manuscripts are accepted. Nevertheless, analyses of the data with one specific scientific or biological question in mind make them to be not used to their full potential. Application of these public datasets to start new research is also difficult as access and analysis of them are not always easy. In this study, we developed an online survival analysis tool (OSMCC), which used Kaplan–Meier plot and log-rank test to assess the prognostic potency of human genes in Merkel cell carcinoma (MCC) patients using the gene expression profiling data.
| Subjects and Methods|| |
Collection of data sets related to Merkel cell carcinoma
To find appropriate data sets for the analysis, we explored The Cancer Genome Atlas More Details (TCGA; http://cancergenome.nih.gov) and GEO (GEO; http://www.ncbi.nlm.nih.gov/geo/) using the keywords such as Merkel cell carcinoma, cancer of Markel cell, survival, and prognosis. Each studied sample must have anatomopathological and/or clinical characteristics and gene expression profiling data to be considered for analysis. Clinical survival information and gene expression profiling data of MCC patients were collected. We only found the required datasets in GEO and were carefully selected based on the availability of their respective anatomoclinical data. Patients were grouped according to different parameters. A total of thirty patients, including both males and females, with the probes of 42,985 and 21,651 genes were used for analysis. Tumor location information displayed the highest number of cases on legs (20%) and cheeks (13%). Patients were also categorized into first quartile, second quartile, and third quartile. Furthermore, some other important parameters were gathered with their relation to patients' age, stage at diagnosis, gender, and outcome.
Setup of server for online survival calculation
The gene expression and clinical data were stored in SQL Server database. The server is hosted on Windows Server 2008, and Apache Tomcat was used as application server. The server-side scripts were developed in Java, which controls the analysis requests and delivers the results. The R (https://www.r-project.org/) package was used to calculate and plot Kaplan–Meier survival curves. Hazard ratio (HR and 95% confidence intervals) and log-rank P values were calculated and presented. OSMCC can be reached at http://bioinfo.henu.edu.cn/MCC/MCCList.jsp.
| Results|| |
We identified a data set of thirty patients meeting our criteria in GEO (GSE39612). Of the above, 33.3% patients were Stage I, 23.3% were Stage II, and 33.3% were Stage III tumors. There was no information provided about the stage of remaining 11% of the patients. The age ranged from 53 to 90 years. Seventy-six percent of patients were not found to be immunosuppressed, while 23% were immunosuppressed and no information was provided about the remaining 1%.
The Kaplan–Meier plot and log-rank test showed the association between investigated genes and overall survival in which the samples were grouped according to the median (or upper or lower quartile/trichotomy) expression of the selected gene. Before running the analysis, the patients can be filtered using stage, gender, and site of tumor [Figure 1]a. Representative Kaplan–Meier plots for NAPRT and ADM gene are demonstrated in [Figure 1]b and [Figure 1]c.
|Figure 1: Screenshot of Online Survival analysis tool for Merkel cell carcinoma main interface (a), and Kaplan–Meier plots for representative gene NAPRT (b) and ADM (c)|
Click here to view
| Discussion|| |
MCC is a rare type of skin cancer having low occurrence rate ranging from 240 to 440 patients/100,000 individuals,, and is progressive cancer with low survival rate of 29%–64%.,,,,,, The major systems in identifying prognostic pattern of MCC patients include cancer staging,,,,,,, and various available biomarkers.
The development of prognostic biomarkers is a major bottleneck in skin cancer (MCC) research. In this study, we developed a freely accessible online tool, OSMCC, to estimate the prognostic value of any interested gene in a cohort of clinical patients. A Kaplan–Meier plot is generated after recording patient information on the selected gene. The implemented computations can be performed in real time on our server. This enables seamless extension in the future using new data sets or new filtering options. We plan to continuously incorporate new GEO data sets as well as new TCGA samples in OSMCC. In contrast to other skin cancers where several approved markers are already in clinical use, MCC shows minimal progress in recent years.
| Conclusion|| |
OSmcc has the potential to identify MCC through transcriptome and clinical datasets analysis. It can prove to become an efficient online source for the MCC prognostic measurement due to its database infrastructure, which provides complementary data for the detection of biomarkers responsible for MCC prognosis
Financial Support and Sponsorship
This work was supported by the National Natural Science Foundation of China (No. 81602362 to XG), the program for Science and Technology Development in Henan Province (No. 162102310391 to XG), the program for Young Key Teacher of Henan Province (No. 2016GGJS-214 to XG), the supporting grants of Henan University (No. 2015YBZR048 to XG; No.B2015151 to XG), the program for Innovative Talents of Science and Technology in Henan Province (No. 18HASTIT048 to XG), and Yellow River Scholar Program (No.H2016012 to XG). The funding bodies were not involved in the study design, data collection, analysis, and interpretation of data or in writing of this manuscript.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoecker C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genet
2001; 29: 365–71.
Ball CA, Brazma A, Causton H, Chervitz S, Edgar R, Hingamp P, Matese JC, Parkinson H, Quackenbush J, Ringwald M, Sansone SA, Sherlock G, Spellman P, Stoeckert C, Tateno Y, Taylor R, White J, Winegarden N. Submission of microarray data to public repositories. PLoS Biol
2004; 2: e317.
Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R. NCBI GEO: Mining millions of expression profiles–database and tools. Nucleic Acids Res
2005; 33: D562–6.
Parkinson H, Sarkans U, Shojatalab M, Abeygunawardena N, Contrino S, Coulson R, Farne A, Lara GG, Holloway E, Kapushesky M, Lilja P, Mukherjee G, Oezcimen A, Rayner T, Rocca-Serra P, Sharma A, Sansone S, Brazma A. ArrayExpress: A public repository for microarray gene expression data at the EBI. Nucleic Acids Res
2005; 33: D553–5.
Agelli M, Clegg LX. Epidemiology of primary Merkel cell carcinoma in the United States. J Am Acad Dermatol
2003; 49: 832–41.
Pan D, Narayan D, Ariyan S. Merkel cell carcinoma: 5 case reports using sentinel lymph node biopsy and a review of 110 new cases. Plast Reconstr Surg
2001; 110: 1259–65.
Hodgson NC. Merkel cell carcinoma: Changing incidence trends. J Surg Oncol
2005; 89: 1–4.
Dancey AL, Rayatt SS, Soon C, Ilchshyn A, Brown I, Srivastava S. Merkel cell carcinoma: A report of 34 cases and literature review. J Plast Reconstr Aesthet Surg
2006; 59: 1294–9.
Koljonen V, Bohling T, Granhroth G, Tukiainen E. Merkel cell carcinoma: A clinicopathological study of 34 patients. Eur J Surg Oncol
2003; 29: 607–10.
Medina-Franco H, Urist MM, Fiveash J, Heslin MJ, Bland KI, Beenken SW. Multimodality treatment of Merkel cell carcinoma: Case series and literature review of 1024 cases. Ann Surg Oncol
2001; 8: 204–8.
Tai PT, Yu E, Winquist E, Hammond A, Stitt L, Tonita J, Gilchrist J. Chemotherapy in neuroendocrine/Merkel cell carcinoma of the skin: Case series and review of 204 cases. J Clin Oncol
2000; 18: 2493–9.
Lonardo MT, Marone U, Apice G, Ferrara E, De Chiara A, Cerra R, Chiofalo MG, Mozzillo N. Merkel cell carcinoma: Experience of 14 cases and literature review. J Exp Clin Cancer Res
2006; 25: 331–7.
Smith DE, Bielamowicz S, Kagan AR, Anderson PJ, Peddada AV. Cutaneous neuroendocrine (Merkel cell) carcinoma. A report of 35 cases. Am J Clin Oncol
1995; 18: 199–203.
Eng TY, Naguib M, Fuller CD, Jones WE 3rd
, Herman TS. Treatment of recurrent Merkel cell carcinoma: An analysis of 46 cases. Am J Clin Oncol
2004; 27: 576–83.
Allen PJ, Bowne WB, Jaques DP, Brennan MF, Busam K, Coit DG. Merkel cell carcinoma: Prognosis and treatment of patients from a single institution. J Clin Oncol
2005; 23: 2300–9.
Allen PJ, Zhang ZF, Coit DG. Surgical management of Merkel cell carcinoma. Ann Surg
1999; 229: 97–105.
Yiengpruksawan A, Coit DG, Thaler HT, Urmacher C, Knapper WK. Merkel cell carcinoma. Prognosis and management. Arch Surg
1991; 126: 1514–9.
Tai PT, Yu E, Tonita J, Gilchrist J. Merkel cell carcinoma of the skin. J Cutan Med Surg
2000; 4: 186–95.