Skip to content
2000
Volume 26, Issue 2
  • ISSN: 1871-5206
  • E-ISSN: 1875-5992

Abstract

Background

Cancer is a complex disease marked by changes in the levels and functions of key cellular proteins, including oncogenes and tumor suppressors. Proteomics technology enables the identification of crucial protein targets and signaling pathways involved in cancer cell proliferation and metastasis. Various proteomics techniques have been employed to investigate the molecular mechanisms of cancer, aiding in the confirmation and characterization of heritable disorders.

Methods

A comprehensive literature search was conducted using PubMed, ScienceDirect, and Google Scholar with search terms like “Cancer and proteomics” and “Mass spectrometry in oncology,” utilizing Boolean operators for refinement. Selection criteria included peer-reviewed articles in English on MS-based biomarker detection, tumor-specific proteins, and drug resistance markers, excluding non-peer-reviewed works and pre-2000 publications unless foundational. Extracted data focused on MS methodologies, biomarker sensitivity, and clinical applications, particularly advances in detecting low-abundance biomarkers and monitoring treatment response. Methodological quality was assessed using PRISMA, evaluating study design, sample size, reproducibility, and statistical analysis. Ethical approval was not required, but adherence to systematic review guidelines and proper citation were ensured.

Results

In this review, we highlighted the advanced analytical technique for cancer diagnosis and management of cancer, and described the objective of novel cancer biomarkers. Mass spectrometry (MS) is transforming cancer diagnostics and personalized medicine by enabling precise biomarker detection and monitoring. Unlike traditional antibody-based methods, MS provides high-throughput, quantitative analysis of tumor-specific proteins in clinical samples like blood and tissue. Advanced MS techniques improve sensitivity, allowing for the identification of low-abundance biomarkers and tumor-associated proteoforms, including post-translational modifications and drug resistance markers. In research, MS-based proteomics supports multi-center biomarker validation studies with standardized protocols, enhancing reproducibility. The integration of proteomic data with genomic and transcriptomic datasets through proteogenomics is refining precision oncology strategies. These advancements are bridging the gap between research and clinical application, making MS a critical tool for early cancer detection, prognosis, and therapy selection.

Conclusion

Advancements in technology and analytical techniques have helped to produce more accurate and sensitive cancer-specific biomarkers. These methods are advancing rapidly, and developing high-throughput platforms has yielded great results. However, the substantial variation in protein concentrations makes cancer protein profiling extremely complicated. This shows that more technical developments are required in the future to improve proteome broad screening of cancer cells.

Loading

Article metrics loading...

/content/journals/acamc/10.2174/0118715206377391250526054417
2025-05-29
2026-03-07
Loading full text...

Full text loading...

References

  1. ZhangD.Y. YeF. GaoL. LiuX. ZhaoX. CheY. WangH. WangL. WuJ. SongD. LiuW. XuH. JiangB. ZhangW. WangJ. LeeP. Proteomics, pathway array and signaling network-based medicine in cancer.Cell Div.2009412010.1186/1747‑1028‑4‑20 19863813
    [Google Scholar]
  2. Nass, S.J.; Amankwah, F.K.; Madhavan, G.; Johns, M.M., Eds.; Guiding cancer control: A path to transformation.
    [Google Scholar]
  3. AgrwalA. MittalS. Opportunities with nano-formulations in cancer chemoprevention. Natural Products and Nano-Formulations in Cancer Chemoprevention.Boca Raton, FLCRC Press2023
    [Google Scholar]
  4. IslamiF. Goding SauerA. MillerK.D. SiegelR.L. FedewaS.A. JacobsE.J. McCulloughM.L. PatelA.V. MaJ. SoerjomataramI. FlandersW.D. BrawleyO.W. GapsturS.M. JemalA. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States.CA Cancer J. Clin.2018681315410.3322/caac.21440 29160902
    [Google Scholar]
  5. de MartelC. GeorgesD. BrayF. FerlayJ. CliffordG.M. Global burden of cancer attributable to infections in 2018: A worldwide incidence analysis.Lancet Glob. Health202082e180e19010.1016/S2214‑109X(19)30488‑7 31862245
    [Google Scholar]
  6. WeiderpassE. Lifestyle and cancer risk.J. Prev. Med. Public Health201043645947110.3961/jpmph.2010.43.6.459 21139406
    [Google Scholar]
  7. KushiL.H. ByersT. DoyleC. BanderaE.V. McCulloughM. GanslerT. AndrewsK.S. ThunM.J. ThunM.J. American Cancer Society Guidelines on Nutrition and Physical Activity for cancer prevention: Reducing the risk of cancer with healthy food choices and physical activity.CA Cancer J. Clin.200656525428110.3322/canjclin.56.5.254 17005596
    [Google Scholar]
  8. ColditzG.A. WeiE.K. Preventability of cancer: The relative contributions of biologic and social and physical environmental determinants of cancer mortality.Annu. Rev. Public Health201233113715610.1146/annurev‑publhealth‑031811‑124627 22224878
    [Google Scholar]
  9. BakerM.J. HussainS.R. LovergneL. UntereinerV. HughesC. LukaszewskiR.A. ThiéfinG. SockalingumG.D. Developing and understanding biofluid vibrational spectroscopy: A critical review.Chem. Soc. Rev.20164571803181810.1039/C5CS00585J 26612430
    [Google Scholar]
  10. ThakkarV. PatelP. PrajapatiN. KaurR. NandaveM. Serum levels of glycoproteins are elevated in patients with ovarian cancer.Indian J. Clin. Biochem.201429334535010.1007/s12291‑013‑0380‑6 24966484
    [Google Scholar]
  11. WuL. QuX. Cancer biomarker detection: Recent achievements and challenges.Chem. Soc. Rev.201544102963299710.1039/C4CS00370E 25739971
    [Google Scholar]
  12. WHO Cancer.2025Available from https://www.who.int/news-room/fact-sheets/detail/cancer
    [Google Scholar]
  13. DregerM. Proteome analysis at the level of subcellular structures.Eur. J. Biochem.2003270458959910.1046/j.1432‑1033.2003.03426.x 12581199
    [Google Scholar]
  14. MannM. JensenO.N. Proteomic analysis of post-translational modifications.Nat. Biotechnol.200321325526110.1038/nbt0303‑255 12610572
    [Google Scholar]
  15. WilkinsM.R. SanchezJ.C. GooleyA.A. AppelR.D. Humphery-SmithI. HochstrasserD.F. WilliamsK.L. Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it.Biotechnol. Genet. Eng. Rev.1996131195010.1080/02648725.1996.10647923 8948108
    [Google Scholar]
  16. SallamR.M. Proteomics in cancer biomarkers discovery: Challenges and applications.Dis. Markers2015201532137010.1155/2015/321370 25999657
    [Google Scholar]
  17. WilhelmM. SchleglJ. HahneH. GholamiA.M. LieberenzM. SavitskiM.M. ZieglerE. ButzmannL. GessulatS. MarxH. MathiesonT. LemeerS. SchnatbaumK. ReimerU. WenschuhH. MollenhauerM. Slotta-HuspeninaJ. BoeseJ.H. BantscheffM. GerstmairA. FaerberF. KusterB. Mass-spectrometry-based draft of the human proteome.Nature2014509750258258710.1038/nature13319 24870543
    [Google Scholar]
  18. BrunetS. ThibaultP. GagnonE. KearneyP. BergeronJ.J. DesjardinsM. Organelle proteomics: Looking at less to see more.Trends Cell Biol.2003131262963810.1016/j.tcb.2003.10.006 14624841
    [Google Scholar]
  19. NicholsonJ.K. LindonJ.C. HolmesE. ‘Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data.Xenobiotica199929111181118910.1080/004982599238047 10598751
    [Google Scholar]
  20. AslamB. BasitM. NisarM.A. KhurshidM. RasoolM.H. Proteomics: Technologies and their applications.J. Chromatogr. Sci.2017552182196 28087761
    [Google Scholar]
  21. WaitR. MillerI. EberiniI. CairoliF. VeronesiC. BattocchioM. GemeinerM. GianazzaE. Strategies for proteomics with incompletely characterized genomes: The proteome of Bos taurus serum.Electrophoresis200223193418342710.1002/1522‑2683(200210)23:19<3418::AID‑ELPS3418>3.0.CO;2‑7 12373772
    [Google Scholar]
  22. TwymanR. CfeP.D. GeorgeA. Principles of proteomics.New YorkGarland Science2013
    [Google Scholar]
  23. AltelaarA.F.M. MunozJ. HeckA.J.R. Next-generation proteomics: Towards an integrative view of proteome dynamics.Nat. Rev. Genet.2013141354810.1038/nrg3356 23207911
    [Google Scholar]
  24. JoshiS. WangT. AraujoT.L.S. SharmaS. BrodskyJ.L. ChiosisG. Adapting to stress — Chaperome networks in cancer.Nat. Rev. Cancer201818956257510.1038/s41568‑018‑0020‑9 29795326
    [Google Scholar]
  25. AlessandroR. FontanaS. KohnE. De LeoG. Proteomic strategies and their application in cancer research.Tumori200591644745510.1177/030089160509100601 16457140
    [Google Scholar]
  26. AlfaroJ.A. SinhaA. KislingerT. BoutrosP.C. Onco-proteogenomics: Cancer proteomics joins forces with genomics.Nat. Methods201411111107111310.1038/nmeth.3138 25357240
    [Google Scholar]
  27. KumarS. MohanA. GuleriaR. Biomarkers in cancer screening, research and detection: Present and future: A review.Biomarkers200611538540510.1080/13547500600775011 16966157
    [Google Scholar]
  28. SrinivasP.R. SrivastavaS. HanashS. WrightG.L. Proteomics in early detection of cancer.Clin. Chem.200147101901191110.1093/clinchem/47.10.1901 11568117
    [Google Scholar]
  29. HanashS.M. BobekM.P. RickmanD.S. WilliamsT. RouillardJ.M. KuickR. Integrating cancer genomics and proteomics in the post-genome era.Proteomics200226975 11788993
    [Google Scholar]
  30. JungE. HellerM. SanchezJ.C. HochstrasserD.F. Proteomics meets cell biology: The establishment of subcellular proteomes.Electrophoresis200021163369337710.1002/1522‑2683(20001001)21:16<3369::AID‑ELPS3369>3.0.CO;2‑7 11079557
    [Google Scholar]
  31. AdonnaT.A. AbbatielloS.E. SchillingB. SkatesS.J. ManiD.R. BunkD.M. SpiegelmanC.H. ZimmermanL.J. HamA.J.L. KeshishianH. HallS.C. AllenS. BlackmanR.K. BorchersC.H. BuckC. CardasisH.L. CusackM.P. DodderN.G. BigxonB.W. HeldJ.M. HiltkeT. JacksonA. JohansenE.B. KinsingerC.R. LiJ. MesriM. NeubertT.A. NilesR.K. PulsipherT.C. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.Nat. Biotechnol.200927763364110.1038/nbt.1546 19561596
    [Google Scholar]
  32. AngererJ. EwersU. WilhelmM. Human biomonitoring: State of the art.Int. J. Hyg. Environ. Health20072103-420122810.1016/j.ijheh.2007.01.024 17376741
    [Google Scholar]
  33. MishraA. VermaM. Cancer biomarkers: Are we ready for the prime time?Cancers20102119020810.3390/cancers2010190 24281040
    [Google Scholar]
  34. OdegaardJ.I. VincentJ.J. MortimerS. VowlesJ.V. UlrichB.C. BanksK.C. Validation of a plasma-based comprehensive cancer genotyping assay utilizing orthogonal tissue-and plasma-based methodologiesvalidation of a comprehensive cancer liquid biopsy test.Clin. Cancer Res.201824153539354910.1158/1078‑0432.CCR‑17‑3831 29691297
    [Google Scholar]
  35. TakabeK. BeneschM.G.K. Biomarker research in world journal of oncology.World J. Oncol.20231411310.14740/wjon1577 36895990
    [Google Scholar]
  36. ShawA. BradleyM.D. ElyanS. KurianK.M. Tumour biomarkers: Diagnostic, prognostic, and predictive.BMJ2015351h344910.1136/bmj.h3449 26141725
    [Google Scholar]
  37. ZhuC.Q. da CunhaG. DingK. SakuradaA. CutzJ.C. LiuN. ZhangT. MarranoP. WhiteheadM. SquireJ.A. Kamel-ReidS. SeymourL. ShepherdF.A. TsaoM.S. Role of KRAS and EGFR as biomarkers of response to erlotinib in national cancer institute of canada clinical trials group study BR.21.J. Clin. Oncol.200826264268427510.1200/JCO.2007.14.8924 18626007
    [Google Scholar]
  38. RayS. ReddyP.J. ChoudharyS. RaghuD. SrivastavaS. Emerging nanoproteomics approaches for disease biomarker detection: A current perspective.J. Proteomics201174122660268110.1016/j.jprot.2011.04.027 21596164
    [Google Scholar]
  39. HenryN.L. HayesD.F. Cancer biomarkers.Mol. Oncol.20126214014610.1016/j.molonc.2012.01.010 22356776
    [Google Scholar]
  40. MnatsakanyanR. ShemaG. BasikM. BatistG. BorchersC.H. SickmannA. ZahediR.P. Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry.Expert Rev. Proteomics201815651553510.1080/14789450.2018.1483340 29893147
    [Google Scholar]
  41. GstaigerM. AebersoldR. Applying mass spectrometry-based proteomics to genetics, genomics and network biology.Nat. Rev. Genet.200910961762710.1038/nrg2633 19687803
    [Google Scholar]
  42. The role of proteomics in oncology biomarker. 2020. Available from: https://blog.crownbio.com/role-of-proteomics-in-oncology-biomarker-discovery
  43. SheuB.C. ChangW.C. LinH.H. ChowS.N. HuangS.C. Immune concept of human papillomaviruses and related antigens in local cancer milieu of human cervical neoplasia.J. Obstet. Gynaecol. Res.200733210311310.1111/j.1447‑0756.2007.00492.x 17441881
    [Google Scholar]
  44. ChoW.C.S. Contribution of oncoproteomics to cancer biomarker discovery.Mol. Cancer2007612510.1186/1476‑4598‑6‑25 17407558
    [Google Scholar]
  45. HicksD.G. TubbsR.R. Assessment of the HER2 status in breast cancer by fluorescence in situ hybridization: A technical review with interpretive guidelines.Hum. Pathol.200536325026110.1016/j.humpath.2004.11.010 15791569
    [Google Scholar]
  46. VeenstraT.D. PrietoD.A. ConradsT.P. Proteomic patterns for early cancer detection.Drug Discov. Today200492088989710.1016/S1359‑6446(04)03246‑5 15475322
    [Google Scholar]
  47. LabibM. KelleyS.O. Single-cell analysis targeting the proteome.Nat. Rev. Chem.20204314315810.1038/s41570‑020‑0162‑7 37128021
    [Google Scholar]
  48. PinhoS.S. ReisC.A. Glycosylation in cancer: Mechanisms and clinical implications.Nat. Rev. Cancer201515954055510.1038/nrc3982 26289314
    [Google Scholar]
  49. DaiL. ChenweiL. SheddenK.A. LeeC.J. ChenweiL. QuocH. SimeoneD.M. LubmanD.M. Quantitative proteomic profiling studies of pancreatic cancer stem cells.J. Proteome Res.2010973394340210.1021/pr100231m 20486718
    [Google Scholar]
  50. AndersonN.L. AndersonN.G. The human plasma proteome: History, character, and diagnostic prospects.Mol. Cell. Proteomics200211184586710.1074/mcp.R200007‑MCP200 12488461
    [Google Scholar]
  51. HondaK. OnoM. ShitashigeM. MasudaM. KamitaM. MiuraN. YamadaT. Proteomic approaches to the discovery of cancer biomarkers for early detection and personalized medicine.Jpn. J. Clin. Oncol.201343210310910.1093/jjco/hys200 23248327
    [Google Scholar]
  52. LiH. LiG. ZhaoX. WuY. MaW. LiuY. GongF. LiangS. Complementary serum proteomic analysis of autoimmune hepatitis in mice and patients.J. Transl. Med.201311114610.1186/1479‑5876‑11‑146 23763817
    [Google Scholar]
  53. BatisN. BrooksJ.M. PayneK. SharmaN. NankivellP. MehannaH. Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation.Adv. Drug Deliv. Rev.202117611385410.1016/j.addr.2021.113854 34192550
    [Google Scholar]
  54. TatischeffI. Current search through liquid biopsy of effective biomarkers for early cancer diagnosis into the rich cargoes of extracellular vesicles.Int. J. Mol. Sci.20212211567410.3390/ijms22115674 34073560
    [Google Scholar]
  55. DuffyM.J. EvoyD. McDermottE.W. CA 15-3: Uses and limitation as a biomarker for breast cancer.Clin. Chim. Acta201041123-241869187410.1016/j.cca.2010.08.039 20816948
    [Google Scholar]
  56. TorenP. ZoubeidiA. Targeting the PI3K/Akt pathway in prostate cancer: Challenges and opportunities (Review).Int. J. Oncol.20144551793180110.3892/ijo.2014.2601 25120209
    [Google Scholar]
  57. SoppimathK.S. AminabhaviT.M. KulkarniA.R. RudzinskiW.E. Biodegradable polymeric nanoparticles as drug delivery devices.J. Control. Release2001701-212010.1016/S0168‑3659(00)00339‑4 11166403
    [Google Scholar]
  58. ChakrabortyS. RahmanT. The difficulties in cancer treatment.Ecancermedicalscience20126ed16 24883085
    [Google Scholar]
  59. CoplandM. JørgensenH.G. HolyoakeT.L. Evolving molecular therapy for chronic myeloid leukaemia—Are we on target?Hematology200510534935910.1080/10245330500234195 16203604
    [Google Scholar]
  60. OttoT. SicinskiP. Cell cycle proteins as promising targets in cancer therapy.Nat. Rev. Cancer20171729311510.1038/nrc.2016.138 28127048
    [Google Scholar]
  61. ChangL. RuizP. ItoT. SellersW.R. Targeting pan-essential genes in cancer: Challenges and opportunities.Cancer Cell202139446647910.1016/j.ccell.2020.12.008 33450197
    [Google Scholar]
  62. IrobiE.O. Time to Diagnosis of Second Primary Cancers among Patients with Breast Cancer.Walden University2016
    [Google Scholar]
  63. AglianoA. CalvoA. BoxC., Eds The challenge of targeting cancer stem cells to halt metastasis Seminars in Cancer Biology.Elsevier2017
    [Google Scholar]
  64. NagamuneT. Biomolecular engineering for nanobio/bionanotechnology.Nano Converg.201741910.1186/s40580‑017‑0103‑4 28491487
    [Google Scholar]
  65. ElbashirS.M. LendeckelW. TuschlT. RNA interference is mediated by 21- and 22-nucleotide RNAs.Genes Dev.200115218820010.1101/gad.862301 11157775
    [Google Scholar]
  66. LinL.L. HuangH.C. JuanH.F. Discovery of biomarkers for gastric cancer: A proteomics approach.J. Proteomics201275113081309710.1016/j.jprot.2012.03.046 22498886
    [Google Scholar]
  67. GrønborgM. KristiansenT.Z. IwahoriA. ChangR. ReddyR. SatoN. MolinaH. JensenO.N. HrubanR.H. GogginsM.G. MaitraA. PandeyA. Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach.Mol. Cell. Proteomics20065115717110.1074/mcp.M500178‑MCP200 16215274
    [Google Scholar]
  68. MauryaP. MeleadyP. DowlingP. ClynesM. Proteomic approaches for serum biomarker discovery in cancer.Anticancer Res.2007273A12471255 17593616
    [Google Scholar]
  69. ChengA.L. HuangW.G. ChenZ.C. PengF. ZhangP.F. LiM.Y. LiF. LiJ.L. LiC. YiH. YiB. XiaoZ.Q. Identification of novel nasopharyngeal carcinoma biomarkers by laser capture microdissection and proteomic analysis.Clin. Cancer Res.200814243544510.1158/1078‑0432.CCR‑07‑1215 18223218
    [Google Scholar]
  70. Álvarez-ChaverP. Otero-EstévezO. Páez de la CadenaM. Rodríguez-BerrocalF.J. Martínez-ZorzanoV.S. Proteomics for discovery of candidate colorectal cancer biomarkers.World J. Gastroenterol.201420143804382410.3748/wjg.v20.i14.3804 24744574
    [Google Scholar]
  71. DrakeR.R. CazaresL.H. SemmesO.J. WadsworthJ.T. Serum, salivary and tissue proteomics for discovery of biomarkers for head and neck cancers.Expert Rev. Mol. Diagn.2005519310010.1586/14737159.5.1.93 15723595
    [Google Scholar]
  72. DoS.K. ChoiS.H. LeeS.Y. ChoiJ.E. KangH.G. HongM.J. KimJ.H. BaekS.A. LeeJ.H. LeeW.K. DoY.W. LeeE.B. ShinK.M. JeongJ.Y. LeeY.H. SeoH. YooS.S. LeeJ. ChaS.I. KimC.H. SeokY. ChoS. JheonS. ParkJ.Y. Genetic variants in one-carbon metabolism pathway predict survival outcomes of early-stage non-small cell lung cancer.Oncology2020981289790410.1159/000509658 32791502
    [Google Scholar]
  73. ZhouM. KongY. WangX. LiW. ChenS. WangL. LC-MS/MS-based quantitative proteomics analysis of different stages of non-small-cell lung cancer.BioMed Res. Int.20212021556156910.1155/2021/5561569 33728331
    [Google Scholar]
  74. JaysonG.C. KohnE.C. KitchenerH.C. LedermannJ.A. Ovarian cancer.Lancet201438499511376138810.1016/S0140‑6736(13)62146‑7 24767708
    [Google Scholar]
  75. StaicuC.E. PredescuD.V. RusuC.M. RaduB.M. CretoiuD. SuciuN. CrețoiuS.M. VoineaS.C. Role of microRNAs as clinical cancer biomarkers for ovarian cancer: A short overview.Cells20209116910.3390/cells9010169 31936634
    [Google Scholar]
  76. BriggsM.T. CondinaM.R. Klingler-HoffmannM. ArentzG. Everest-DassA.V. KaurG. OehlerM.K. PackerN.H. HoffmannP. Translating N‐glycan analytical applications into clinical strategies for ovarian cancer.Proteomics Clin. Appl.2019133180009910.1002/prca.201800099 30367710
    [Google Scholar]
  77. JungJ.H. KimH.J. YeomJ. YooC. ShinJ. YooJ. KangC.S. LeeC. Lowered expression of galectin-2 is associated with lymph node metastasis in gastric cancer.J. Gastroenterol.2012471374810.1007/s00535‑011‑0463‑1 22015694
    [Google Scholar]
  78. JiaS.Q. NiuZ.J. ZhangL.H. ZhongX.Y. ShiT. DuH. ZhangG.G. HuY. SuX.L. JiJ.F. Identification of prognosis-related proteins in advanced gastric cancer by mass spectrometry-based comparative proteomics.J. Cancer Res. Clin. Oncol.2009135340341110.1007/s00432‑008‑0474‑3 18830628
    [Google Scholar]
  79. KonO.L. YipT.T. HoM.F. ChanW.H. WongW.K. TanS.Y. NgW.H. KamS.Y. EngA.K.H. HoP. VinerR. OngH.S. KumarasingheM.P. The distinctive gastric fluid proteome in gastric cancer reveals a multi-biomarker diagnostic profile.BMC Med. Genomics2008115410.1186/1755‑8794‑1‑54 18950519
    [Google Scholar]
  80. KoopmannJ. ThuluvathP.J. ZahurakM.L. KristiansenT.Z. PandeyA. SchulickR. ArganiP. HidalgoM. IacobelliS. GogginsM. MaitraA. Mac‐2‐binding protein is a diagnostic marker for biliary tract carcinoma.Cancer200410171609161510.1002/cncr.20469 15378479
    [Google Scholar]
  81. KashyapM.K. HarshaH.C. RenuseS. PawarH. SahasrabuddheN.A. KimM.S. MarimuthuA. KeerthikumarS. MuthusamyB. KandasamyK. SubbannayyaY. PrasadT.S.K. MahmoodR. ChaerkadyR. MeltzerS.J. KumarR.V. RustgiA.K. PandeyA. SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome.Cancer Biol. Ther.201010879681010.4161/cbt.10.8.12914 20686364
    [Google Scholar]
  82. GirardS. ShalhoubP. LescureP. SabileA. MisekD.E. HanashS. BréchotC. BerettaL. An altered cellular response to interferon and up-regulation of interleukin-8 induced by the hepatitis C viral protein NS5A uncovered by microarray analysis.Virology2002295227228310.1006/viro.2002.1373 12033786
    [Google Scholar]
  83. HuangL.J. ChenS.X. HuangY. LuoW.J. JiangH.H. HuQ.H. ZhangP.F. YiH. Proteomics-based identification of secreted protein dihydrodiol dehydrogenase as a novel serum markers of non-small cell lung cancer.Lung Cancer2006541879410.1016/j.lungcan.2006.06.011 16876904
    [Google Scholar]
  84. RuiZ. Jian-GuoJ. Yuan-PengT. HaiP. Bing-GenR. Use of serological proteomic methods to find biomarkers associated with breast cancer.Proteomics20033443343910.1002/pmic.200390058 12687611
    [Google Scholar]
  85. PedreroJ.M. FernandezM.P. MorganR.O. HerreroZ. A.; Gonzalez, M.V.; Suarez, N.C.; Rodrigo, J.P. Annexin A1 down-regulation in head and neck cancer is associated with epithelial differentiation status.Am. J. Pathol.20041641737910.1016/S0002‑9440(10)63098‑2 14695321
    [Google Scholar]
  86. RodrigoJ.P. Garcia-PedreroJ.M. FernandezM.P. MorganR.O. SuárezC. HerreroA. Annexin A1 expression in nasopharyngeal carcinoma correlates with squamous differentiation.Am. J. Rhinol.200519548348710.1177/194589240501900511 16270603
    [Google Scholar]
  87. StaackA. TolicD. KristiansenG. SchnorrD. LoeningS.A. JungK. Expression of cathepsins B, H, and L and their inhibitors as markers of transitional cell carcinoma of the bladder.Urology20046361089109410.1016/j.urology.2004.01.018 15183956
    [Google Scholar]
  88. SongD. ChaerkadyR. TanA.C. García-GarcíaE. NalliA. Suárez-GauthierA. López-RíosF. ZhangX.F. SolomonA. TongJ. ReadM. FritzC. JimenoA. PandeyA. HidalgoM. Antitumor activity and molecular effects of the novel heat shock protein 90 inhibitor, IPI-504, in pancreatic cancer.Mol. Cancer Ther.20087103275328410.1158/1535‑7163.MCT‑08‑0508 18852131
    [Google Scholar]
  89. WongC. WongV. ChanC. MaB. HuiE. WongM. LamM. AuT. ChanW.H. CheukW. ChanA. Identification of 5-fluorouracil response proteins in colorectal carcinoma cell line SW480 by two-dimensional electrophoresis and MALDI-TOF mass spectrometry.Oncol. Rep.2008201899810.3892/or.20.1.89 18575723
    [Google Scholar]
  90. MlynarekM.A. Proteomics and the identification of serum biomarkers in a mouse model of oral squamous cell carcinoma.Head Neck200830328729510.1002/hed.20684
    [Google Scholar]
  91. MelleC. ErnstG. SchimmelB. BleulA. KoscielnyS. WiesnerA. BogumilR. MöllerU. OsterlohD. HalbhuberK.J. von EggelingF. A technical triade for proteomic identification and characterization of cancer biomarkers.Cancer Res.200464124099410410.1158/0008‑5472.CAN‑03‑3807 15205318
    [Google Scholar]
  92. MakridakisM. VlahouA. Secretome proteomics for discovery of cancer biomarkers.J. Proteomics201073122291230510.1016/j.jprot.2010.07.001 20637910
    [Google Scholar]
  93. BanduR. MokH.J. KimK.P. Phospholipids as cancer biomarkers: Mass spectrometry‐based analysis.Mass Spectrom. Rev.201837210713810.1002/mas.21510 27276657
    [Google Scholar]
  94. FliserD. NovakJ. ThongboonkerdV. ArgilésA. JankowskiV. GirolamiM.A. JankowskiJ. MischakH. Advances in urinary proteome analysis and biomarker discovery.J. Am. Soc. Nephrol.20071841057107110.1681/ASN.2006090956 17329573
    [Google Scholar]
  95. XiaoG.G. ReckerR.R. DengH-W. Recent advances in proteomics and cancer biomarker discovery.Clin. Med. Oncol.20082637210.4137/cmo.s539 21892267
    [Google Scholar]
  96. KikuchiT. CarboneD.P. Proteomics analysis in lung cancer: Challenges and opportunities.Respirology2007121222810.1111/j.1440‑1843.2006.00957.x 17207021
    [Google Scholar]
  97. WulfkuhleJ.D. LiottaL.A. PetricoinE.F. Proteomic applications for the early detection of cancer.Nat. Rev. Cancer20033426727510.1038/nrc1043 12671665
    [Google Scholar]
  98. SchneiderR. KotseridisY. RayJ.L. AugierC. BaumesR. Quantitative determination of sulfur-containing wine odorants at sub parts per billion levels. 2. Development and application of a stable isotope dilution assay.J. Agric. Food Chem.200351113243324810.1021/jf0211128 12744649
    [Google Scholar]
  99. KasparS. PeukertM. SvatosA. MatrosA. MockH.P. MALDI‐imaging mass spectrometry – An emerging technique in plant biology.Proteomics20111191840185010.1002/pmic.201000756 21462348
    [Google Scholar]
  100. McArthurS.L. VendettuoliM.C. RatnerB.D. CastnerD.G. Methods for generating protein molecular ions in ToF-SIMS.Langmuir20042093704370910.1021/la0358419 15875403
    [Google Scholar]
  101. ZhaoS.S. ZhongX. TieC. ChenD.D.Y. Capillary electrophoresis‐mass spectrometry for analysis of complex samples.Proteomics20121219-202991301210.1002/pmic.201200221 22888086
    [Google Scholar]
  102. PontilloC. FilipS. BorràsD.M. MullenW. VlahouA. MischakH. CE‐MS‐based proteomics in biomarker discovery and clinical application.Proteomics Clin. Appl.201593-432233410.1002/prca.201400115 25641774
    [Google Scholar]
  103. IssaqH.J. VeenstraT.D. ConradsT.P. FelschowD. The SELDI-TOF MS approach to proteomics: Protein profiling and biomarker identification.Biochem. Biophys. Res. Commun.2002292358759210.1006/bbrc.2002.6678 11922607
    [Google Scholar]
  104. BitarteN. BandrésE. ZárateR. RamirezN. Garcia-FoncillasJ. Moving forward in colorectal cancer research, what proteomics has to tell.World J. Gastroenterol.200713445813582110.3748/wjg.v13.i44.5813 17990347
    [Google Scholar]
  105. LiangS. XuZ. XuX. ZhaoX. HuangC. WeiY. Quantitative proteomics for cancer biomarker discovery.Comb. Chem. High Throughput Screen.201215322123110.2174/138620712799218635 22221055
    [Google Scholar]
  106. RenX.Y. YangW.B. TianY. Overexpression of long noncoding RNA PTPRG-AS1 is associated with poor prognosis in epithelial ovarian cancer.Rev. Assoc. Med. Bras.202066794895310.1590/1806‑9282.66.7.948 32844927
    [Google Scholar]
  107. BastR.C. KlugT.L. JohnE.S. JenisonE. NiloffJ.M. LazarusH. BerkowitzR.S. LeavittT. GriffithsC.T. ParkerL. ZurawskiV.R. KnappR.C. A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer.N. Engl. J. Med.19833091588388710.1056/NEJM198310133091503 6310399
    [Google Scholar]
  108. BarakatR.R. MarkmanM. RandallM. Principles and practice of gynecologic oncology.5th edPhiladelphiaLippincott Williams & Wilkins2009
    [Google Scholar]
  109. MenonU. JacobsI.J. Recent developments in ovarian cancer screening.Curr. Opin. Obstet. Gynecol.2000121394210.1097/00001703‑200002000‑00007 10752515
    [Google Scholar]
  110. JacobsI.J. SkatesS.J. MacDonaldN. MenonU. RosenthalA.N. DaviesA.P. WoolasR. JeyarajahA.R. SibleyK. LoweD.G. OramD.H. Screening for ovarian cancer: A pilot randomised controlled trial.Lancet199935391601207121010.1016/S0140‑6736(98)10261‑1 10217079
    [Google Scholar]
  111. CohenL.S. EscobarP.F. ScharmC. GlimcoB. FishmanD.A. Three-dimensional power Doppler ultrasound improves the diagnostic accuracy for ovarian cancer prediction.Gynecol. Oncol.2001821404810.1006/gyno.2001.6253 11426960
    [Google Scholar]
  112. HerbertB.R. SanchezJ-C. BiniL. Two-dimensional electrophoresis: The state of the art and future directions.Proteome Research: New Frontiers in Functional Genomics Principles and Practice.Berlin, HeidelbergSpringer1997133410.1007/978‑3‑662‑03493‑4_2
    [Google Scholar]
  113. RichterR. Schulz-KnappeP. SchraderM. StändkerL. JürgensM. TammenH. ForssmannW.G. Composition of the peptide fraction in human blood plasma: Database of circulating human peptides.J. Chromatogr., Biomed. Appl.19997261-2253510.1016/S0378‑4347(99)00012‑2 10348167
    [Google Scholar]
  114. PaweletzC.P. GillespieJ.W. OrnsteinD.K. SimoneN.L. BrownM.R. ColeK.A. WangQ-H. HuangJ. HuN. YipT-T. RichW.E. KohnE.C. LinehanW.M. WeberT. TaylorP. Emmert-BuckM.R. LiottaL.A. PetricoinE.F. Rapid protein display profiling of cancer progression directly from human tissue using a protein biochip.Drug Dev. Res.2000491344210.1002/(SICI)1098‑2299(200001)49:1<34::AID‑DDR6>3.0.CO;2‑W
    [Google Scholar]
  115. AlizadehA.A. EisenM.B. DavisR.E. MaC. LossosI.S. RosenwaldA. BoldrickJ.C. SabetH. TranT. YuX. PowellJ.I. YangL. MartiG.E. MooreT. HudsonJ. LuL. LewisD.B. TibshiraniR. SherlockG. ChanW.C. GreinerT.C. WeisenburgerD.D. ArmitageJ.O. WarnkeR. LevyR. WilsonW. GreverM.R. ByrdJ.C. BotsteinD. BrownP.O. StaudtL.M. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.Nature2000403676950351110.1038/35000501 10676951
    [Google Scholar]
  116. GolubT.R. SlonimD.K. TamayoP. HuardC. GaasenbeekM. MesirovJ.P. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring.Science1999286543953153710.1126/science.286.5439.531 10521349
    [Google Scholar]
  117. LindahlD. PalmerJ. EdenbrandtL. Myocardial SPET: Artificial neural networks describe extent and severity of perfusion defects.Clin. Physiol.199919649750310.1046/j.1365‑2281.1999.00203.x 10583343
    [Google Scholar]
  118. LapuertaP. L’ItalienG.J. PaulS. HendelR.C. LeppoJ.A. FleisherL.A. CohenM.C. EagleK.A. GiuglianoR.P. Neural network assessment of perioperative cardiac risk in vascular surgery patients.Med. Decis. Making1998181707510.1177/0272989X9801800114 9456211
    [Google Scholar]
  119. MardamshinaM. GeigerT. Next-generation proteomics and its application to clinical breast cancer research.Am. J. Pathol.2017187102175218410.1016/j.ajpath.2017.07.003 28736317
    [Google Scholar]
  120. MeggerD.A. NaboulsiW. MeyerH.E. SitekB. Proteome analyses of hepatocellular carcinoma.J. Clin. Transl. Hepatol.2014212330 26357614
    [Google Scholar]
  121. TobyT.K. FornelliL. KelleherN.L. Progress in top-down proteomics and the analysis of proteoforms.Annu. Rev. Anal. Chem.20169149951910.1146/annurev‑anchem‑071015‑041550 27306313
    [Google Scholar]
  122. ZhangY. FonslowB.R. ShanB. BaekM.C. YatesJ.R. Protein analysis by shotgun/bottom-up proteomics.Chem. Rev.201311342343239410.1021/cr3003533 23438204
    [Google Scholar]
  123. HuangH. ShuklaH. WuC. SaxenaS. Challenges and solutions in proteomics.Curr. Genomics200781212810.2174/138920207780076910 18645629
    [Google Scholar]
  124. SchubertO.T. RöstH.L. CollinsB.C. RosenbergerG. AebersoldR. Quantitative proteomics: Challenges and opportunities in basic and applied research.Nat. Protoc.20171271289129410.1038/nprot.2017.040 28569762
    [Google Scholar]
  125. Amiri-DashatanN. KoushkiM. AbbaszadehH-A. Rostami-NejadM. Rezaei-TaviraniM. Proteomics applications in health: Biomarker and drug discovery and food industry.Iran. J. Pharm. Res.201817415231536 30568709
    [Google Scholar]
  126. GeyerP.E. HoldtL.M. TeupserD. MannM. Revisiting biomarker discovery by plasma proteomics.Mol. Syst. Biol.201713994210.15252/msb.20156297 28951502
    [Google Scholar]
  127. IgnjatovicV. GeyerP.E. PalaniappanK.K. ChaabanJ.E. OmennG.S. BakerM.S. DeutschE.W. SchwenkJ.M. Mass spectrometry-based plasma proteomics: Considerations from sample collection to achieving translational data.J. Proteome Res.201918124085409710.1021/acs.jproteome.9b00503 31573204
    [Google Scholar]
  128. GeyerP.E. VoytikE. TreitP.V. DollS. KleinhempelA. NiuL. MüllerJ.B. BuchholtzM.L. BaderJ.M. TeupserD. HoldtL.M. MannM. Plasma proteome profiling to detect and avoid sample‐related biases in biomarker studies.EMBO Mol. Med.20191111e1042710.15252/emmm.201910427 31566909
    [Google Scholar]
  129. AllenD. McWhinneyB. Quadrupole time-of-flight mass spectrometry: A paradigm shift in toxicology screening applications.Clin. Biochem. Rev.201940313514610.33176/AACB‑19‑00023 31530964
    [Google Scholar]
  130. LinH. ZhangF. GengQ. YuT. CuiY. LiuX. LiJ. YanM. LiuL. HeX. LiJ. YaoM. Quantitative proteomic analysis identifies CPNE3 as a novel metastasis-promoting gene in NSCLC.J. Proteome Res.20131273423343310.1021/pr400273z 23713811
    [Google Scholar]
  131. IwamotoN. ShimadaT. Recent advances in mass spectrometry-based approaches for proteomics and biologics: Great contribution for developing therapeutic antibodies.Pharmacol. Ther.201818514715410.1016/j.pharmthera.2017.12.007 29274706
    [Google Scholar]
  132. ChakrabortyP. PradeepT. The emerging interface of mass spectrometry with materials.NPG Asia Mater.20191114810.1038/s41427‑019‑0149‑3
    [Google Scholar]
  133. AlharbiR.A. Proteomics approach and techniques in identification of reliable biomarkers for diseases.Saudi J. Biol. Sci.202027396897410.1016/j.sjbs.2020.01.020 32127776
    [Google Scholar]
  134. GebreyesusS.T. SiyalA.A. KitataR.B. ChenE.S.W. EnkhbayarB. AngataT. LinK.I. ChenY.J. TuH.L. Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry.Nat. Commun.20221313710.1038/s41467‑021‑27778‑4 35013269
    [Google Scholar]
  135. Burnum-JohnsonK.E. NieS. CaseyC.P. MonroeM.E. OrtonD.J. IbrahimY.M. GritsenkoM.A. ClaussT.R.W. ShuklaA.K. MooreR.J. PurvineS.O. ShiT. QianW. LiuT. BakerE.S. SmithR.D. Simultaneous proteomic discovery and targeted monitoring using liquid chromatography, ion mobility spectrometry, and mass spectrometry.Mol. Cell. Proteomics201615123694370510.1074/mcp.M116.061143 27670688
    [Google Scholar]
  136. ZhangZ. BastR.C. YuY. LiJ. SokollL.J. RaiA.J. RosenzweigJ.M. CameronB. WangY.Y. MengX.Y. BerchuckA. van Haaften-DayC. HackerN.F. de BruijnH.W.A. van der ZeeA.G.J. JacobsI.J. FungE.T. ChanD.W. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer.Cancer Res.200464165882589010.1158/0008‑5472.CAN‑04‑0746 15313933
    [Google Scholar]
  137. KirwanA. UtratnaM. O’DwyerM.E. JoshiL. KilcoyneM. Glycosylation-based serum biomarkers for cancer diagnostics and prognostics.BioMed Res. Int.2015201549053110.1155/2015/490531 26509158
    [Google Scholar]
  138. KailemiaM.J. ParkD. LebrillaC.B. Glycans and glycoproteins as specific biomarkers for cancer.Anal. Bioanal. Chem.2017409239541010.1007/s00216‑016‑9880‑6 27590322
    [Google Scholar]
  139. Islam KhanM.Z. TamS.Y. LawH.K.W. Advances in high throughput proteomics profiling in establishing potential biomarkers for gastrointestinal cancer.Cells202211697310.3390/cells11060973 35326424
    [Google Scholar]
  140. MenyhártO. GyőrffyB. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.Comput. Struct. Biotechnol. J.20211994996010.1016/j.csbj.2021.01.009 33613862
    [Google Scholar]
  141. ManiD.R. KrugK. ZhangB. SatpathyS. ClauserK.R. DingL. EllisM. GilletteM.A. CarrS.A. Cancer proteogenomics: Current impact and future prospects.Nat. Rev. Cancer202222529831310.1038/s41568‑022‑00446‑5 35236940
    [Google Scholar]
  142. RöstH.L. MalmströmL. AebersoldR. Reproducible quantitative proteotype data matrices for systems biology.Mol. Biol. Cell201526223926393110.1091/mbc.E15‑07‑0507 26543201
    [Google Scholar]
  143. GuW. WangY. Gene discovery for disease models. 2011 Hoboken (NJ): John Wiley & Sons.10.1002/9780470933947
    [Google Scholar]
  144. RussellM.R. WalkerM.J. WilliamsonA.J.K. Gentry-MaharajA. RyanA. KalsiJ. SkatesS. D’AmatoA. DiveC. PernemalmM. HumphryesP.C. FourkalaE.O. WhettonA.D. MenonU. JacobsI. GrahamR.L.J. ProteinZ. A putative novel biomarker for early detection of ovarian cancer.Int. J. Cancer2016138122984299210.1002/ijc.30020 26815306
    [Google Scholar]
  145. JefferyD.A. BogyoM. Chemical proteomics and its application to drug discovery.Curr. Opin. Biotechnol.2003141879510.1016/S0958‑1669(02)00010‑1 12566007
    [Google Scholar]
  146. AebersoldR. MannM. Mass spectrometry-based proteomics.Nature2003422692819820710.1038/nature01511 12634793
    [Google Scholar]
  147. López-OtínC. OverallC.M. Protease degradomics: A new challenge for proteomics.Nat. Rev. Mol. Cell Biol.20023750951910.1038/nrm858 12094217
    [Google Scholar]
  148. GravesP.R. HaysteadT.A.J. Molecular biologist’s guide to proteomics.Microbiol. Mol. Biol. Rev.2002661396310.1128/MMBR.66.1.39‑63.2002 11875127
    [Google Scholar]
  149. KimP.S. BaldwinR.L. Intermediates in the folding reactions of small proteins.Annu. Rev. Biochem.199059163166010.1146/annurev.bi.59.070190.003215 2197986
    [Google Scholar]
  150. ChoW.C.S. Proteomics technologies and challenges.Genomics Proteomics Bioinformatics200752778510.1016/S1672‑0229(07)60018‑7 17893073
    [Google Scholar]
  151. KolchW. MischakH. PittA.R. The molecular make-up of a tumour: Proteomics in cancer research.Clin. Sci.2005108536938310.1042/CS20050006 15831087
    [Google Scholar]
  152. SuhreK. McCarthyM.I. SchwenkJ.M. Genetics meets proteomics: Perspectives for large population-based studies.Nat. Rev. Genet.2021221193710.1038/s41576‑020‑0268‑2 32860016
    [Google Scholar]
  153. ThulP.J. ÅkessonL. WikingM. MahdessianD. GeladakiA. Ait BlalH. AlmT. AsplundA. BjörkL. BreckelsL.M. BäckströmA. DanielssonF. FagerbergL. FallJ. GattoL. GnannC. HoberS. HjelmareM. JohanssonF. LeeS. LindskogC. MulderJ. MulveyC.M. NilssonP. OksvoldP. RockbergJ. SchuttenR. SchwenkJ.M. SivertssonÅ. SjöstedtE. SkogsM. StadlerC. SullivanD.P. TegelH. WinsnesC. ZhangC. ZwahlenM. MardinogluA. PonténF. von FeilitzenK. LilleyK.S. UhlénM. LundbergE. A subcellular map of the human proteome.Science20173566340eaal332110.1126/science.aal3321 28495876
    [Google Scholar]
  154. HristovaV.A. ChanD.W. Cancer biomarker discovery and translation: Proteomics and beyond.Expert Rev. Proteomics20191629310310.1080/14789450.2019.1559062 30556752
    [Google Scholar]
  155. Esteve-PastorM.A. RoldánV. Rivera-CaravacaJ.M. Ramírez-MacíasI. LipG.Y.H. MarínF. The use of biomarkers in clinical management guidelines: A critical appraisal.Thromb. Haemost.2019119121901191910.1055/s‑0039‑1696955 31499565
    [Google Scholar]
  156. ZhangM. WangB. XuJ. WangX. XieL. ZhangB. LiY. LiJ. CanProVar 2.0: An updated database of human cancer proteome variation.J. Proteome Res.201716242143210.1021/acs.jproteome.6b00505 27977206
    [Google Scholar]
  157. PatelS.K. GeorgeB. RaiV. Artificial intelligence to decode cancer mechanism: Beyond patient stratification for precision oncology.Front. Pharmacol.202011117710.3389/fphar.2020.01177 32903628
    [Google Scholar]
  158. XuY. SuG.H. MaD. XiaoY. ShaoZ.M. JiangY.Z. Technological advances in cancer immunity: From immunogenomics to single-cell analysis and artificial intelligence.Signal Transduct. Target. Ther.20216131210.1038/s41392‑021‑00729‑7 34417437
    [Google Scholar]
  159. YangQ. ZhangY. CuiH. ChenL. ZhaoY. Lin, Y dbDEPC 3.0: The database of differentially expressed proteins in human cancer with multi-level annotation and drug indication.Database20182018bay01510.1093/database/bay015 29688359
    [Google Scholar]
  160. ShaoX. TahaI.N. ClauserK.R. GaoY.T. NabaA. MatrisomeD.B. The ECM-protein knowledge database.Nucleic Acids Res.202048D1D1136D114410.1093/nar/gkz849 31586405
    [Google Scholar]
  161. JonesP. C^ote, R. The PRIDE proteomics identifications database: Data submission, query, and dataset comparison.Methods Mol. Biol.200848428730310.1007/978‑1‑59745‑398‑1_19 18592187
    [Google Scholar]
  162. WuP. HeinsZ.J. MullerJ.T. KatsnelsonL. de BruijnI. AbeshouseA.A. SchultzN. FenyöD. GaoJ. Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal.Mol. Cell. Proteomics20191891893189810.1074/mcp.TIR119.001673 31308250
    [Google Scholar]
  163. AggarwalS. RajA. KumarD. DashD. YadavA.K. False discovery rate: The Achilles’ heel of proteogenomics.Brief. Bioinform.2022235bbac16310.1093/bib/bbac163 35534181
    [Google Scholar]
  164. MuellerL.N. BrusniakM.Y. ManiD.R. AebersoldR. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data.J. Proteome Res.200871516110.1021/pr700758r 18173218
    [Google Scholar]
  165. ZhangG.L. DeLucaD.S. BrusicV. Database resources for proteomics-based analysis of cancer.Methods Mol. Biol.201172334936410.1007/978‑1‑61779‑043‑0_22 21370076
    [Google Scholar]
  166. MattesW.B. PettitS.D. SansoneS.A. BushelP.R. WatersM.D. Database development in toxicogenomics: Issues and efforts.Environ. Health Perspect.2004112449550510.1289/ehp.6697 15033600
    [Google Scholar]
  167. ATLAS, A.B. Quantification of gene expression.Mol. Syst. Biol.201612862
    [Google Scholar]
  168. KatoK. YamashitaR. MatobaR. MondenM. NoguchiS. TakagiT. NakaiK. Cancer gene expression database (CGED): A database for gene expression profiling with accompanying clinical information of human cancer tissues.Nucleic Acids Res.200433Database issueD533D53610.1093/nar/gki117 15608255
    [Google Scholar]
  169. HeidaryM. AuerM. UlzP. HeitzerE. PetruE. GaschC. RiethdorfS. MauermannO. LaferI. PristauzG. LaxS. PantelK. GeiglJ.B. SpeicherM.R. The dynamic range of circulating tumor DNA in metastatic breast cancer.Breast Cancer Res.201416442110.1186/s13058‑014‑0421‑y 25107527
    [Google Scholar]
  170. RheeH. LeeJ.S. MedRefSNP: A database of medically investigated SNPs.Hum. Mutat.2009303E460E46610.1002/humu.20914 19105187
    [Google Scholar]
/content/journals/acamc/10.2174/0118715206377391250526054417
Loading
/content/journals/acamc/10.2174/0118715206377391250526054417
Loading

Data & Media loading...


  • Article Type:
    Review Article
Keyword(s): analytical techniques; biomarkers; Cancer; database; mass spectrometry; proteomics
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test