Abstract
Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- TCGA:
-
The Cancer Genome Atlas
- MDM:
-
Majority decision-based method
- Th1:
-
T helper 1 type
- M1:
-
Type 1 Macrophages
- RSEM:
-
RNA-Seq by Expectation–Maximization
- DAVID:
-
Database for Annotation, Visualization and Integrated Discovery
- GO:
-
Gene Ontology
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- PPI:
-
Protein–protein interaction network
References
Azarnia R, Loewenstein WR (1971) Intercellular communication and tissue growth: V. A. cancer cell strain that fails to make permeable membrane junctions with normal cells. J Membr Biol 6:368–385. https://doi.org/10.1007/BF02116580
Barderas R et al (2013) In-depth characterization of the secretome of colorectal cancer metastatic cells identifies key proteins in cell adhesion, migration, and invasion. Mol Cell Proteomics 12:1602–1620. https://doi.org/10.1074/mcp.M112.022848
Berardi MJ, Shih WM, Harrison SC, Chou JJ (2011) Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching. Nature 476:109–113. https://doi.org/10.1038/nature10257
Berraondo P et al (2019) Cytokines in clinical cancer immunotherapy. Br J Cancer 120:6–15. https://doi.org/10.1038/s41416-018-0328-y
Bonin-Debs AL, Boche I, Gille H, Brinkmann U (2004) Development of secreted proteins as biotherapeutic agents. Expert Opin Biol Ther 4:551–558. https://doi.org/10.1517/14712598.4.4.551
Bos R, Sherman LA (2010) CD4 + T-cell help in the tumor milieu is required for recruitment and cytolytic function of CD8 + T lymphocytes Cancer Res 70:8368-8377 https://doi.org/10.1158/0008-5472.can-10-1322
Caccia D, Zanetti Domingues L, Micciche F, De Bortoli M, Carniti C, Mondellini P, Bongarzone I (2011) Secretome compartment is a valuable source of biomarkers for cancer-relevant pathways. J Proteome Res 10:4196–4207. https://doi.org/10.1021/pr200344n
Campbell JD et al (2016) Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet 48:607–616. https://doi.org/10.1038/ng.3564
Cancer Genome Atlas N (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487:330–337. https://doi.org/10.1038/nature11252
Cancer Genome Atlas Research N (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507:315–322. https://doi.org/10.1038/nature12965
Cancer Genome Atlas Research Network. Electronic address aadhe, Cancer Genome Atlas Research N (2017) Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32(185–203):e113. https://doi.org/10.1016/j.ccell.2017.07.007
Cao DS, Xu QS, Liang YZ (2013) Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962. https://doi.org/10.1093/bioinformatics/btt072
Cerami E et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404. https://doi.org/10.1158/2159-8290.CD-12-0095
Chandran SS, Paria BC, Srivastava AK, Rothermel LD, Stephens DJ, Kammula US (2015) Tumor-specific effector CD8 + T cells that can establish immunological memory in humans after adoptive transfer are marked by expression of IL7 receptor and c-myc. Cancer Res 75:3216–3226. https://doi.org/10.1158/0008-5472.CAN-15-0584
Chen W, Lei TY, Jin DC, Lin H, Chou KC (2014) PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 456:53–60. https://doi.org/10.1016/j.ab.2014.04.001
Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou KC (2015) PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31:119–120. https://doi.org/10.1093/bioinformatics/btu602
Chen Q et al (2017) High mRNA expression level of IL-6R was associated with better prognosis for patients with ovarian cancer: a pooled meta-analysis. Sci Rep 7:8769. https://doi.org/10.1038/s41598-017-09333-8
Cheng X, Zhao SG, Lin WZ, Xiao X, Chou KC (2017) pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics 33:3524–3531. https://doi.org/10.1093/bioinformatics/btx476
Cheng X, Xiao X, Chou KC (2018) pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34:1448–1456. https://doi.org/10.1093/bioinformatics/btx711
Chou KC (2004) Structural bioinformatics and its impact to biomedical science. Curr Med Chem 11:2105–2134. https://doi.org/10.2174/0929867043364667
Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273:236–247. https://doi.org/10.1016/j.jtbi.2010.12.024
Chou KC (2015) Impacts of bioinformatics to medicinal chemistry. Med Chem 11:218–234. https://doi.org/10.2174/1573406411666141229162834
Chou KC (2019a) Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr Med Chem. https://doi.org/10.2174/0929867326666190507082559
Chou KC (2019b) Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis. Curr Top Med Chem 19:2283–2300. https://doi.org/10.2174/1568026619666191018100141
Chou K-C, Shen H-B (2009) REVIEW: recent advances in developing web-servers for predicting protein attributes. Nat Sci 01:63–92. https://doi.org/10.4236/ns.2009.12011
Chou KC, Cheng X, Xiao X (2019) pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med Chem 15:472–485. https://doi.org/10.2174/1573406415666181218102517
Church SE, Jensen SM, Antony PA, Restifo NP, Fox BA (2014) Tumor-specific CD4 + T cells maintain effector and memory tumor-specific CD8 + T cells. Eur J Immunol 44:69–79. https://doi.org/10.1002/eji.201343718
Ciriello G et al (2015) Comprehensive molecular portraits of invasive lobular breast. Cancer Cell 163:506–519. https://doi.org/10.1016/j.cell.2015.09.033
Cline MS, Craft B, Swatloski T, Goldman M, Ma S, Haussler D, Zhu J (2013) Exploring TCGA Pan-Cancer data at the UCSC Cancer Genomics Browser. Sci Rep 3:2652. https://doi.org/10.1038/srep02652
Crowley E, Di Nicolantonio F, Loupakis F, Bardelli A (2013) Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol 10:472–484. https://doi.org/10.1038/nrclinonc.2013.110
da Huang W, Sherman BT, Lempicki RA (2009a) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13. https://doi.org/10.1093/nar/gkn923
da Huang W, Sherman BT, Lempicki RA (2009b) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211
de Visser KE, Eichten A, Coussens LM (2006) Paradoxical roles of the immune system during cancer development. Nat Rev Cancer 6:24–37. https://doi.org/10.1038/nrc1782
Dev J et al (2016) Structural basis for membrane anchoring of HIV-1 envelope spike. Science 353:172–175. https://doi.org/10.1126/science.aaf7066
Diaz LA Jr, Bardelli A (2014) Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol 32:579–586. https://doi.org/10.1200/JCO.2012.45.2011
Du P, Wang X, Xu C, Gao Y (2012) PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions. Anal Biochem 425:117–119. https://doi.org/10.1016/j.ab.2012.03.015
Du P, Gu S, Jiao Y (2014) PseAAC-General: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 15:3495–3506. https://doi.org/10.3390/ijms15033495
Etzioni R et al (2003) The case for early detection. Nat Rev Cancer 3:243–252. https://doi.org/10.1038/nrc1041
Fagerberg L, Jonasson K, von Heijne G, Uhlen M, Berglund L (2010) Prediction of the human membrane proteome. Proteomics 10:1141–1149. https://doi.org/10.1002/pmic.200900258
Farooqi AA, Fayyaz S, Tahir M, Iqbal MJ, Bhatti S (2012) Breast cancer proteome takes more than two to tango on TRAIL: beat them at their own game. J Membr Biol 245:763–777. https://doi.org/10.1007/s00232-012-9490-y
Fridman WH, Pages F, Sautes-Fridman C, Galon J (2012) The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12:298–306. https://doi.org/10.1038/nrc3245
Guo XE, Ngo B, Modrek AS, Lee WH (2014) Targeting tumor suppressor networks for cancer therapeutics. Curr Drug Targets 15:2–16. https://doi.org/10.2174/1389450114666140106095151
Gyorffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, Szallasi Z (2010) An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat 123:725–731. https://doi.org/10.1007/s10549-009-0674-9
Haabeth OA, Lorvik KB, Yagita H, Bogen B, Corthay A (2016) Interleukin-1 is required for cancer eradication mediated by tumor-specific Th1 cells. Oncoimmunology 5:e1039763. https://doi.org/10.1080/2162402X.2015.1039763
Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674. https://doi.org/10.1016/j.cell.2011.02.013
Heasley LE (2001) Autocrine and paracrine signaling through neuropeptide receptors in human cancer. Oncogene 20:1563–1569. https://doi.org/10.1038/sj.onc.1204183
Kampen KR (2011) Membrane proteins: the key players of a cancer cell. J Membr Biol 242:69–74. https://doi.org/10.1007/s00232-011-9381-7
Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M (2019) New approach for understanding genome variations in KEGG. Nucleic Acids Res 47:D590–D595. https://doi.org/10.1093/nar/gky962
Karagiannis GS, Pavlou MP, Diamandis EP (2010) Cancer secretomics reveal pathophysiological pathways in cancer molecular oncology. Mol Oncol 4:496–510. https://doi.org/10.1016/j.molonc.2010.09.001
Kessenbrock K, Plaks V, Werb Z (2010) Matrix metalloproteinases: regulators of the tumor microenvironment. Cell 141:52–67. https://doi.org/10.1016/j.cell.2010.03.015
Knutson KL, Disis ML (2005) Tumor antigen-specific T helper cells in cancer immunity and immunotherapy. Cancer Immunol Immunother 54:721–728. https://doi.org/10.1007/s00262-004-0653-2
Kulasingam V, Diamandis EP (2008) Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol 5:588–599. https://doi.org/10.1038/ncponc1187
Lawlor K, Nazarian A, Lacomis L, Tempst P, Villanueva J (2009) Pathway-based biomarker search by high-throughput proteomics profiling of secretomes. J Proteome Res 8:1489–1503. https://doi.org/10.1021/pr8008572
Lin J, Zhu Z, Xiao H, Wakefield MR, Ding VA, Bai Q, Fang Y (2017) The role of IL-7 in Immunity and Cancer. Anticancer Res 37:963–967. https://doi.org/10.21873/anticanres.11405
Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC (2015) Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43:W65–W71. https://doi.org/10.1093/nar/gkv458
Liu B, Liu F, Fang L, Wang X, Chou KC (2016) repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genomics 291:473–481. https://doi.org/10.1007/s00438-015-1078-7
Loei H, Tan HT, Lim TK, Lim KH, So JBY, Yeoh KG, Chung MCM (2012) Mining the gastric cancer secretome: identification of GRN as a potential diagnostic marker for early gastric cancer. J Proteome Res. https://doi.org/10.1021/pr201014h
Luckheeram RV, Zhou R, Verma AD, Xia B (2012) CD4(+)T cells: differentiation and functions. Clin Dev Immunol 2012:925135. https://doi.org/10.1155/2012/925135
Ma J, Liu L, Che G, Yu N, Dai F, You Z (2010) The M1 form of tumor-associated macrophages in non-small cell lung cancer is positively associated with survival time. BMC Cancer 10:112. https://doi.org/10.1186/1471-2407-10-112
Makridakis M, Vlahou A (2010) Secretome proteomics for discovery of cancer biomarkers. J Proteomics 73:2291–2305. https://doi.org/10.1016/j.jprot.2010.07.001
Malta TM et al (2018) Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173(338–354):e315. https://doi.org/10.1016/j.cell.2018.03.034
Mantovani A, Allavena P, Sica A, Balkwill F (2008) Cancer-related inflammation. Nature 454:436–444. https://doi.org/10.1038/nature07205
Mbeunkui F, Johann DJ Jr (2009) Cancer and the tumor microenvironment: a review of an essential relationship. Cancer Chemother Pharmacol 63:571–582. https://doi.org/10.1007/s00280-008-0881-9
Melief CJ, van Hall T, Arens R, Ossendorp F, van der Burg SH (2015) Therapeutic cancer vaccines. J Clin Invest 125:3401–3412. https://doi.org/10.1172/JCI80009
Nakanishi Y, Lu B, Gerard C, Iwasaki A (2009) CD8(+) T lymphocyte mobilization to virus-infected tissue requires CD4(+) T-cell help. Nature 462:510–513. https://doi.org/10.1038/nature08511
Oricchio E et al (2011) The Eph-receptor A7 is a soluble tumor suppressor for follicular lymphoma. Cell 147:554–564. https://doi.org/10.1016/j.cell.2011.09.035
Ou Yang B et al (2013) Unusual architecture of the p7 channel from hepatitis C virus. Nature 498:521–525. https://doi.org/10.1038/nature12283
Oxenoid K et al (2016) Architecture of the mitochondrial calcium uniporter. Nature 533:269–273. https://doi.org/10.1038/nature17656
Pages F, Galon J, Dieu-Nosjean MC, Tartour E, Sautes-Fridman C, Fridman WH (2010) Immune infiltration in human tumors: a prognostic factor that should not be ignored. Oncogene 29:1093–1102. https://doi.org/10.1038/onc.2009.416
Pavlou MP, Diamandis EP (2010) The cancer cell secretome: a good source for discovering biomarkers? J Proteomics 73:1896–1906. https://doi.org/10.1016/j.jprot.2010.04.003
Planque C, Kulasingam V, Smith CR, Reckamp K, Goodglick L, Diamandis EP (2009) Identification of five candidate lung cancer biomarkers by proteomics analysis of conditioned media of four lung cancer cell lines. Mol Cell Proteomics 8:2746–2758. https://doi.org/10.1074/mcp.M900134-MCP200
Pocsfalvi G et al (2011) Analysis of secretome changes uncovers an autocrine/paracrine component in the modulation of cell proliferation and motility by c-Myc. J Proteome Res 10:5326–5337. https://doi.org/10.1021/pr200584y
Prassas I, Chrystoja CC, Makawita S, Diamandis EP (2012) Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery. BMC Med 10:39. https://doi.org/10.1186/1741-7015-10-39
Ralhan R, Masui O, Desouza LV, Matta A, Macha M, Siu KW (2011) Identification of proteins secreted by head and neck cancer cell lines using LC-MS/MS: strategy for discovery of candidate serological biomarkers. Proteomics 11:2363–2376. https://doi.org/10.1002/pmic.201000186
Sawyers CL (2008) The cancer biomarker problem. Nature 452:548–552. https://doi.org/10.1038/nature06913
Schaaij-Visser TB, de Wit M, Lam SW, Jimenez CR (2013) The cancer secretome, current status and opportunities in the lung, breast and colorectal cancer context. Biochim Biophys Acta 1834:2242–2258. https://doi.org/10.1016/j.bbapap.2013.01.029
Schnell JR, Chou JJ (2008) Structure and mechanism of the M2 proton channel of influenza A virus. Nature 451:591–595. https://doi.org/10.1038/nature06531
Senovilla L et al (2012) Trial watch: prognostic and predictive value of the immune infiltrate in cancer. Oncoimmunology 1:1323–1343. https://doi.org/10.4161/onci.22009
Shen HB, Chou KC (2008) PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem 373:386–388. https://doi.org/10.1016/j.ab.2007.10.012
Stastna M, Van Eyk JE (2012) Secreted proteins as a fundamental source for biomarker discovery. Proteomics 12:722–735. https://doi.org/10.1002/pmic.201100346
Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676. https://doi.org/10.1016/j.cell.2006.07.024
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45:W98–W102. https://doi.org/10.1093/nar/gkx247
Taube JM et al (2018) Implications of the tumor immune microenvironment for staging and therapeutics. Mod Pathol 31:214–234. https://doi.org/10.1038/modpathol.2017.156
Tsukamoto H et al (2017) Soluble IL6R Expressed by Myeloid Cells Reduces Tumor-Specific Th1 Differentiation and Drives Tumor Progression. Cancer Res 77:2279–2291. https://doi.org/10.1158/0008-5472.CAN-16-2446
Uhlen M et al (2015) Proteomics. Tissue-based map of the human proteome. Science 347:1260419. https://doi.org/10.1126/science.1260419
Vathipadiekal V et al (2015) Creation of a human secretome: a novel composite library of human secreted proteins: validation using ovarian cancer gene expression data and a virtual secretome array. Clin Cancer Res 21:4960–4969. https://doi.org/10.1158/1078-0432.CCR-14-3173
Verbeke H, Struyf S, Laureys G, Van Damme J (2011) The expression and role of CXC chemokines in colorectal cancer. Cytokine Growth Factor Rev 22:345–358. https://doi.org/10.1016/j.cytogfr.2011.09.002
Welsh JB et al (2003) Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum. Proc Natl Acad Sci USA 100:3410–3415. https://doi.org/10.1073/pnas.0530278100
Whiteside TL (2008) The tumor microenvironment and its role in promoting tumor growth. Oncogene 27:5904–5912. https://doi.org/10.1038/onc.2008.271
Wu CC et al (2010) Candidate serological biomarkers for cancer identified from the secretomes of 23 cancer cell lines and the human protein atlas. Mol Cell Proteomics 9:1100–1117. https://doi.org/10.1074/mcp.M900398-MCP200
Xiao X, Cheng X, Chen G, Mao Q, Chou KC (2019a) pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics 111:886–892. https://doi.org/10.1016/j.ygeno.2018.05.017
Xiao X, Cheng X, Chen G, Mao Q, Chou KC (2019b) pLoc_bal-mvirus: predict subcellular localization of multi-label virus proteins by chou’s general PseAAC and IHTS treatment to balance training dataset. Med Chem 15:496–509. https://doi.org/10.2174/1573406415666181217114710
Xu Y, Chou KC (2016) Recent Progress in Predicting Posttranslational Modification Sites in Proteins. Curr Top Med Chem 16:591–603. https://doi.org/10.2174/1568026615666150819110421
Zhang L et al (2018) Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564:268–272. https://doi.org/10.1038/s41586-018-0694-x
Funding
This work was supported by the Chinese Academy of Sciences (CAS) and The World Academy of Sciences (TWAS).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Research Involving Human and Animal Rights
This article does not contain any studies with human or animal subjects.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rehman, A.u., Olof Olsson, P., Khan, N. et al. Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics. J Membrane Biol 253, 257–270 (2020). https://doi.org/10.1007/s00232-020-00122-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00232-020-00122-5