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Kanser Biyobelirteçlerinin Belirlenmesi için Meta-analizin Uygulanması

Yıl 2024, Cilt: 33 Sayı: 3, 165 - 171, 30.09.2024

Halil İbrahim Pazarbaşı , Athanasia Pavlopoulou

https://doi.org/10.17827/aktd.1508230

Öz

Sağlık uzmanları, kanıta dayalı bir karara varmak için çok sayıda, çoğu zaman birbiriyle çelişen klinik araştırmalardan elde edilen bulguları birleştirme ve tercüme etme zorluğuyla karşı karşıyadır. Meta-analitik yaklaşımların tıp alanında uygulanması, tedavi etkisinin daha doğru tahmin edilmesi veya hastalık risk faktörlerinin belirlenmesi gibi araştırmanın ana kısmı hakkında sonuçlar çıkarmak için çalışmalardaki sonuçların sistematik sentezine ve değerlendirilmesine olanak tanır. Bu çalışmada, güçlü kanser biyobelirteçlerinin tanımlanmasına yönelik meta-analizin avantajlarını ve temel adımlarını derleyeceğiz.

Anahtar Kelimeler

meta-analysis cancer biomarkers

Kaynakça

  • 1. Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. Sensors (Basel). 2023;24.
  • 2. Liu Z, Zhang Y, Niu Y, Li K, Liu X, Chen H et al. A systematic review and meta-analysis of diagnostic and prognostic serum biomarkers of colorectal cancer. PLoS One. 2014;9:e103910.
  • 3. Yu Y, Zeng D, Ou Q, Liu S, Li A, Chen Y et al. Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis. JAMA Netw Open. 2019;2:e196879.
  • 4. Zhang L, Li L, Liu J, Wang J, Fan Y, Dong B et al. Meta-analysis of multiple hematological biomarkers as prognostic predictors of survival in bladder cancer. Medicine (Baltimore). 2020;99:e20920.
  • 5. Fountzilas E, Vo HH, Mueller P, Kurzrock R, Tsimberidou AM. Correlation between biomarkers and treatment outcomes in diverse cancers: a systematic review and meta-analysis of phase I and II immunotherapy clinical trials. Eur J Cancer. 2023;189:112927.
  • 6. Shadish WR, Lecy JD. The meta-analytic big bang. Res Synth Methods. 2015;6:246-64.
  • 7. Papakostidis C, Giannoudis PV. Meta-analysis. What have we learned? Injury. 2023;54 Suppl 3:S30-S34.
  • 8. Forero DA, Lopez-Leon S, Gonzalez-Giraldo Y, Bagos PG. Ten simple rules for carrying out and writing meta-analyses. PLoS Comput Biol. 2019;15:e1006922.
  • 9. Shaheen N, Shaheen A, Ramadan A, Hefnawy MT, Ramadan A, Ibrahim IA et al. Appraising systematic reviews: a comprehensive guide to ensuring validity and reliability. Front Res Metr Anal. 2023;8:1268045.
  • 10. Toy HI, Okmen D, Kontou PI, Georgakilas AG, Pavlopoulou A. HOTAIR as a Prognostic Predictor for Diverse Human Cancers: A Meta- and Bioinformatics Analysis. Cancers (Basel). 2019;11.
  • 11. Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J. 2008;22:338-42.
  • 12. Nikolopoulos GK, Bagos PG, Bonovas S. Developing the evidence base for cancer chemoprevention: use of meta-analysis. Curr Drug Targets. 2011;12:1989-97.
  • 13. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17:1-12.
  • 14. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25:603-5.
  • 15. Moskalewicz A, Oremus M. No clear choice between Newcastle-Ottawa Scale and Appraisal Tool for Cross-Sectional Studies to assess methodological quality in cross-sectional studies of health-related quality of life and breast cancer. J Clin Epidemiol. 2020;120:94-103.
  • 16. Downes MJ, Brennan ML, Williams HC, Dean RS. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open. 2016;6:e011458.
  • 17. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529-36.
  • 18. Greco T, Zangrillo A, Biondi-Zoccai G, Landoni G. Meta-analysis: pitfalls and hints. Heart Lung Vessel. 2013;5:219-25.
  • 19. Clarke M. The QUORUM statement. Lancet. 2000;355:756-7.
  • 20. Brooke BS, Schwartz TA, Pawlik TM. MOOSE Reporting Guidelines for Meta-analyses of Observational Studies. JAMA Surg. 2021;156:787-88.
  • 21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
  • 22. Cuschieri S. The CONSORT statement. Saudi J Anaesth. 2019;13:S27-S30.
  • 23. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344-9.
  • 24. Sagoo GS, Little J, Higgins JP. Systematic reviews of genetic association studies. Human Genome Epidemiology Network. PLoS Med. 2009;6:e28.
  • 25. Evangelou E, Ioannidis JP. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet. 2013;14:379-89.
  • 26. Szumilas M. Explaining odds ratios. J Can Acad Child Adolesc Psychiatry. 2010;19:227-9.
  • 27. Ranganathan P, Aggarwal R, Pramesh CS. Common pitfalls in statistical analysis: Odds versus risk. Perspect Clin Res. 2015;6:222-4.
  • 28. Zhu S, Shuai P, Yang C, Zhang Y, Zhong S, Liu X et al. Prognostic value of long non-coding RNA PVT1 as a novel biomarker in various cancers: a meta-analysis. Oncotarget. 2017;8:113174-84.
  • 29. Wang X, Xie L, Zhu L. Clinicopathological significance of HSP70 expression in gastric cancer: a systematic review and meta-analysis. BMC Gastroenterol. 2021;21:437.
  • 30. Sistrom CL, Garvan CW. Proportions, odds, and risk. Radiology. 2004;230:12-9.
  • 31. Nassour AJ, Jain A, Hui N, Siopis G, Symons J, Woo H. Relative Risk of Bladder and Kidney Cancer in Lynch Syndrome: Systematic Review and Meta-Analysis. Cancers (Basel). 2023;15.
  • 32. Kim HY. Statistical notes for clinical researchers: Risk difference, risk ratio, and odds ratio. Restor Dent Endod. 2017;42:72-76.
  • 33. Nakamura ET, Park A, Pereira MA, Kikawa D, Tustumi F. Prognosis value of heat-shock proteins in esophageal and esophagogastric cancer: A systematic review and meta-analysis. World J Gastrointest Oncol. 2024;16:1578-95.
  • 34. Roberts MR, Ashrafzadeh S, Asgari MM. Research Techniques Made Simple: Interpreting Measures of Association in Clinical Research. J Invest Dermatol. 2019;139:502-11 e1.
  • 35. Tibshirani R. A plain man's guide to the proportional hazards model. Clin Invest Med. 1982;5:63-8.
  • 36. Fang SX, Chen C, Guo Q, Ke XX, Lu HL, Xu G. High lncSNHG15 expression may predict poor cancer prognosis: a meta-analysis based on the PRISMA and the bio-informatics analysis. Biosci Rep. 2020;40.
  • 37. de Moraes FCA, Pereira CRM, Sano VKT, Laia EA, Stecca C, Burbano RMR. Do proton pump inhibitors affect the effectiveness of cyclin-dependent kinase 4/6 inhibitors in advanced HR positive, HER2 negative breast cancer? A meta-analysis. Front Pharmacol. 2024;15:1352224.
  • 38. Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ. 2001;322:1479-80.
  • 39. Dettori JR, Norvell DC, Chapman JR. Fixed-Effect vs Random-Effects Models for Meta-Analysis: 3 Points to Consider. Global Spine J. 2022;12:1624-26.
  • 40. Sedgwick P. Meta-analyses: what is heterogeneity? BMJ. 2015;350:h1435.
  • 41. Cordero CP, Dans AL. Key concepts in clinical epidemiology: detecting and dealing with heterogeneity in meta-analyses. J Clin Epidemiol. 2021;130:149-51.
  • 42. Cochran WG. The comparison of percentages in matched samples. Biometrika. 1950;37:256-66.
  • 43. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557-60.
  • 44. Richardson M, Garner P, Donegan S. Interpretation of subgroup analyses in systematic reviews: A tutorial Clinical Epidemiology and Global Health. 2019;7:192-98.
  • 45. Bonovas S, Filioussi K, Sitaras NM. Statins are not associated with a reduced risk of pancreatic cancer at the population level, when taken at low doses for managing hypercholesterolemia: evidence from a meta-analysis of 12 studies. Am J Gastroenterol. 2008;103:2646-51.
  • 46. Sedgwick P. What is publication bias in a meta-analysis? BMJ. 2015;351:h4419.
  • 47. Lin L, Chu H. Quantifying publication bias in meta-analysis. Biometrics. 2018;74:785-94.
  • 48. Jin ZC, Zhou XH, He J. Statistical methods for dealing with publication bias in meta-analysis. Stat Med. 2015;34:343-60.
  • 49. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629-34.
  • 50. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088-101.

Application of Meta-analysis for Determining Cancer Biomarkers

Yıl 2024, Cilt: 33 Sayı: 3, 165 - 171, 30.09.2024

Halil İbrahim Pazarbaşı , Athanasia Pavlopoulou

https://doi.org/10.17827/aktd.1508230

Öz

The health care professionals are facing the challenge to combine and translate the findings from a plethora of, often conflicting, clinical trials or clinical studies in order to reach an evidence-based decision. The application of a meta-analytical approach in the medical field allows the systematic synthesis and assessment of the results across studies to draw conclusions about the main body of the research, such as a more accurate estimate of treatment effect or determining disease risk factors. Herein, we review the advantages and the basic steps of meta-analysis towards the identification of powerful cancer biomarkers.

Anahtar Kelimeler

meta-analysis cancer biomarkers

Etik Beyan

We declare no conflict of interest.

Kaynakça

  • 1. Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. Sensors (Basel). 2023;24.
  • 2. Liu Z, Zhang Y, Niu Y, Li K, Liu X, Chen H et al. A systematic review and meta-analysis of diagnostic and prognostic serum biomarkers of colorectal cancer. PLoS One. 2014;9:e103910.
  • 3. Yu Y, Zeng D, Ou Q, Liu S, Li A, Chen Y et al. Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis. JAMA Netw Open. 2019;2:e196879.
  • 4. Zhang L, Li L, Liu J, Wang J, Fan Y, Dong B et al. Meta-analysis of multiple hematological biomarkers as prognostic predictors of survival in bladder cancer. Medicine (Baltimore). 2020;99:e20920.
  • 5. Fountzilas E, Vo HH, Mueller P, Kurzrock R, Tsimberidou AM. Correlation between biomarkers and treatment outcomes in diverse cancers: a systematic review and meta-analysis of phase I and II immunotherapy clinical trials. Eur J Cancer. 2023;189:112927.
  • 6. Shadish WR, Lecy JD. The meta-analytic big bang. Res Synth Methods. 2015;6:246-64.
  • 7. Papakostidis C, Giannoudis PV. Meta-analysis. What have we learned? Injury. 2023;54 Suppl 3:S30-S34.
  • 8. Forero DA, Lopez-Leon S, Gonzalez-Giraldo Y, Bagos PG. Ten simple rules for carrying out and writing meta-analyses. PLoS Comput Biol. 2019;15:e1006922.
  • 9. Shaheen N, Shaheen A, Ramadan A, Hefnawy MT, Ramadan A, Ibrahim IA et al. Appraising systematic reviews: a comprehensive guide to ensuring validity and reliability. Front Res Metr Anal. 2023;8:1268045.
  • 10. Toy HI, Okmen D, Kontou PI, Georgakilas AG, Pavlopoulou A. HOTAIR as a Prognostic Predictor for Diverse Human Cancers: A Meta- and Bioinformatics Analysis. Cancers (Basel). 2019;11.
  • 11. Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J. 2008;22:338-42.
  • 12. Nikolopoulos GK, Bagos PG, Bonovas S. Developing the evidence base for cancer chemoprevention: use of meta-analysis. Curr Drug Targets. 2011;12:1989-97.
  • 13. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17:1-12.
  • 14. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25:603-5.
  • 15. Moskalewicz A, Oremus M. No clear choice between Newcastle-Ottawa Scale and Appraisal Tool for Cross-Sectional Studies to assess methodological quality in cross-sectional studies of health-related quality of life and breast cancer. J Clin Epidemiol. 2020;120:94-103.
  • 16. Downes MJ, Brennan ML, Williams HC, Dean RS. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open. 2016;6:e011458.
  • 17. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529-36.
  • 18. Greco T, Zangrillo A, Biondi-Zoccai G, Landoni G. Meta-analysis: pitfalls and hints. Heart Lung Vessel. 2013;5:219-25.
  • 19. Clarke M. The QUORUM statement. Lancet. 2000;355:756-7.
  • 20. Brooke BS, Schwartz TA, Pawlik TM. MOOSE Reporting Guidelines for Meta-analyses of Observational Studies. JAMA Surg. 2021;156:787-88.
  • 21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
  • 22. Cuschieri S. The CONSORT statement. Saudi J Anaesth. 2019;13:S27-S30.
  • 23. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344-9.
  • 24. Sagoo GS, Little J, Higgins JP. Systematic reviews of genetic association studies. Human Genome Epidemiology Network. PLoS Med. 2009;6:e28.
  • 25. Evangelou E, Ioannidis JP. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet. 2013;14:379-89.
  • 26. Szumilas M. Explaining odds ratios. J Can Acad Child Adolesc Psychiatry. 2010;19:227-9.
  • 27. Ranganathan P, Aggarwal R, Pramesh CS. Common pitfalls in statistical analysis: Odds versus risk. Perspect Clin Res. 2015;6:222-4.
  • 28. Zhu S, Shuai P, Yang C, Zhang Y, Zhong S, Liu X et al. Prognostic value of long non-coding RNA PVT1 as a novel biomarker in various cancers: a meta-analysis. Oncotarget. 2017;8:113174-84.
  • 29. Wang X, Xie L, Zhu L. Clinicopathological significance of HSP70 expression in gastric cancer: a systematic review and meta-analysis. BMC Gastroenterol. 2021;21:437.
  • 30. Sistrom CL, Garvan CW. Proportions, odds, and risk. Radiology. 2004;230:12-9.
  • 31. Nassour AJ, Jain A, Hui N, Siopis G, Symons J, Woo H. Relative Risk of Bladder and Kidney Cancer in Lynch Syndrome: Systematic Review and Meta-Analysis. Cancers (Basel). 2023;15.
  • 32. Kim HY. Statistical notes for clinical researchers: Risk difference, risk ratio, and odds ratio. Restor Dent Endod. 2017;42:72-76.
  • 33. Nakamura ET, Park A, Pereira MA, Kikawa D, Tustumi F. Prognosis value of heat-shock proteins in esophageal and esophagogastric cancer: A systematic review and meta-analysis. World J Gastrointest Oncol. 2024;16:1578-95.
  • 34. Roberts MR, Ashrafzadeh S, Asgari MM. Research Techniques Made Simple: Interpreting Measures of Association in Clinical Research. J Invest Dermatol. 2019;139:502-11 e1.
  • 35. Tibshirani R. A plain man's guide to the proportional hazards model. Clin Invest Med. 1982;5:63-8.
  • 36. Fang SX, Chen C, Guo Q, Ke XX, Lu HL, Xu G. High lncSNHG15 expression may predict poor cancer prognosis: a meta-analysis based on the PRISMA and the bio-informatics analysis. Biosci Rep. 2020;40.
  • 37. de Moraes FCA, Pereira CRM, Sano VKT, Laia EA, Stecca C, Burbano RMR. Do proton pump inhibitors affect the effectiveness of cyclin-dependent kinase 4/6 inhibitors in advanced HR positive, HER2 negative breast cancer? A meta-analysis. Front Pharmacol. 2024;15:1352224.
  • 38. Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ. 2001;322:1479-80.
  • 39. Dettori JR, Norvell DC, Chapman JR. Fixed-Effect vs Random-Effects Models for Meta-Analysis: 3 Points to Consider. Global Spine J. 2022;12:1624-26.
  • 40. Sedgwick P. Meta-analyses: what is heterogeneity? BMJ. 2015;350:h1435.
  • 41. Cordero CP, Dans AL. Key concepts in clinical epidemiology: detecting and dealing with heterogeneity in meta-analyses. J Clin Epidemiol. 2021;130:149-51.
  • 42. Cochran WG. The comparison of percentages in matched samples. Biometrika. 1950;37:256-66.
  • 43. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557-60.
  • 44. Richardson M, Garner P, Donegan S. Interpretation of subgroup analyses in systematic reviews: A tutorial Clinical Epidemiology and Global Health. 2019;7:192-98.
  • 45. Bonovas S, Filioussi K, Sitaras NM. Statins are not associated with a reduced risk of pancreatic cancer at the population level, when taken at low doses for managing hypercholesterolemia: evidence from a meta-analysis of 12 studies. Am J Gastroenterol. 2008;103:2646-51.
  • 46. Sedgwick P. What is publication bias in a meta-analysis? BMJ. 2015;351:h4419.
  • 47. Lin L, Chu H. Quantifying publication bias in meta-analysis. Biometrics. 2018;74:785-94.
  • 48. Jin ZC, Zhou XH, He J. Statistical methods for dealing with publication bias in meta-analysis. Stat Med. 2015;34:343-60.
  • 49. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629-34.
  • 50. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088-101.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Hizmetleri ve Sistemleri (Diğer)
Bölüm Derleme
Yazarlar

Halil İbrahim Pazarbaşı 0000-0002-0945-3861

Athanasia Pavlopoulou 0000-0002-0815-3808

Erken Görünüm Tarihi 25 Eylül 2024
Yayımlanma Tarihi 30 Eylül 2024
Gönderilme Tarihi 1 Temmuz 2024
Kabul Tarihi 5 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 33 Sayı: 3

Kaynak Göster

AMA Pazarbaşı Hİ, Pavlopoulou A. Application of Meta-analysis for Determining Cancer Biomarkers. aktd. Eylül 2024;33(3):165-171. doi:10.17827/aktd.1508230
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