Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Development and validation of a novel strong prognostic index for colon cancer through a robust combination of laboratory features for systemic inflammation: a prognostic immune nutritional index

British Journal of Cancer volume 126, pages 1539–1547 (2022)Cite this article

Subjects

Abstract

Background

Systemic inflammation is associated with survival outcomes in colon cancer. However, it is not well-known which systemic inflammatory marker is a powerful prognostic marker in patients with colon cancer.

Methods

A total of 4535 colon cancer patients were included in this study. We developed a novel prognostic index using a robust combination of seven systemic inflammation-associated blood features of the discovery set. The predictability and generality of the novel prognostic index were evaluated in the discovery, validation and replication sets.

Results

Among all combinations, the combination of albumin and monocyte count was the best candidate expression. The final formula of the proposed novel index is named the Prognostic Immune and Nutritional Index (PINI). The concordance index of PINI for overall and progression-free survival was the highest in the discovery, validation and replication sets compared to existing prognostic inflammatory markers. PINI was found to be a significant independent prognostic factor for both overall and progression-free survival.

Conclusions

PINI is a novel prognostic index that has improved discriminatory power in colon cancer patients and appears to be superior to existing prognostic inflammatory markers. PINI can be utilised for decision-making regarding personalised treatment as the complement of the TNM staging system.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The workflow of developing a novel prognostic inflammatory index in colon cancer.
Fig. 2: The average predictive performance of the top 20 candidate expressions in the distinctive prognosis group over 10-fold CV with 100 repeats.
Fig. 3: Kaplan–Meier curves of the high and low PINI groups for OS and PFS.

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

References

  1. Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493–e503.

    Article  Google Scholar 

  2. Park JW, Chang HJ, Yeo HY, Han N, Kim BC, Kong S-Y, et al. The relationships between systemic cytokine profiles and inflammatory markers in colorectal cancer and the prognostic significance of these parameters. Br J Cancer. 2020;123:610–8.

    Article  CAS  Google Scholar 

  3. Sylman JL, Mitrugno A, Atallah M, Tormoen GW, Shatzel JJ, Tassi Yunga S, et al. The predictive value of inflammation-related peripheral blood measurements in cancer staging and prognosis. Front Oncol. 2018;8:78.

    Article  Google Scholar 

  4. Tanio A, Saito H, Uejima C, Takaya S, Yamamoto M, Tokuyasu N, et al. A prognostic index for colorectal cancer based on preoperative absolute lymphocyte, monocyte, and neutrophil counts. Surg Today. 2019;49:245–53.

    Article  CAS  Google Scholar 

  5. Heys S, Walker L, Deehan D, Eremin O. Serum albumin: a prognostic indicator in patients with colorectal cancer. J R Coll Surg Edinb. 1998;43:163–8.

    CAS  PubMed  Google Scholar 

  6. Li MX, Liu XM, Zhang XF, Zhang JF, Wang WL, Zhu Y, et al. Prognostic role of neutrophil‐to‐lymphocyte ratio in colorectal cancer: a systematic review and meta‐analysis. Int J Cancer. 2014;134:2403–13.

    Article  CAS  Google Scholar 

  7. Lu C, Gao P, Yang Y, Chen X, Wang L, Yu D, et al. Prognostic evaluation of platelet to lymphocyte ratio in patients with colorectal cancer. Oncotarget. 2017;8:86287.

    Article  Google Scholar 

  8. Song W, Wang K, Zhang R-J, Zou S-B. Prognostic value of the lymphocyte monocyte ratio in patients with colorectal cancer: a meta-analysis. Medicine. 2016;95:49.

    Google Scholar 

  9. Sun G, Li Y, Peng Y, Lu D, Zhang F, Cui X, et al. Impact of the preoperative prognostic nutritional index on postoperative and survival outcomes in colorectal cancer patients who underwent primary tumor resection: a systematic review and meta-analysis. Int J Colorectal Dis. 2019;34:681–9.

    Article  Google Scholar 

  10. Hu X, Li Y-Q, Li Q-G, Ma Y-L, Peng J-J, Cai S-J. Baseline peripheral blood leukocytosis is negatively correlated with T-cell infiltration predicting worse outcome in colorectal cancers. Front Immunol. 2018;9:2354.

    Article  Google Scholar 

  11. Son H-J, Park JW, Chang HJ, Kim DY, Kim BC, Kim SY, et al. Preoperative plasma hyperfibrinogenemia is predictive of poor prognosis in patients with nonmetastatic colon cancer. Ann Surg Oncol. 2013;20:2908–13.

    Article  Google Scholar 

  12. Proctor M, Morrison D, Talwar D, Balmer S, O’reilly D, Foulis A, et al. An inflammation-based prognostic score (mGPS) predicts cancer survival independent of tumour site: a Glasgow Inflammation Outcome Study. Br J Cancer. 2011;104:726–34.

    Article  CAS  Google Scholar 

  13. Skrede O-J, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020;395:350–60.

    Article  CAS  Google Scholar 

  14. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.

    Google Scholar 

  15. Yang Y, Gao P, Chen X, Song Y, Shi J, Zhao J, et al. Prognostic significance of preoperative prognostic nutritional index in colorectal cancer: results from a retrospective cohort study and a meta-analysis. Oncotarget. 2016;7:58543.

    Article  Google Scholar 

  16. Wu Q, Hu T, Zheng E, Deng X, Wang Z. Prognostic role of the lymphocyte-to-monocyte ratio in colorectal cancer: an up-to-date meta-analysis. Medicine. 2017;96:20.

    Google Scholar 

  17. Sauerbrei W, Taube SE, McShane LM, Cavenagh MM, Altman DG. Reporting recommendations for tumor marker prognostic studies (REMARK): an abridged explanation and elaboration. J Natl Cancer Inst. 2018;110:803–11.

    Article  Google Scholar 

  18. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48:1503–10.

    Article  CAS  Google Scholar 

  19. McMillan DC, Watson WS, O’Gorman P, Preston T, Scott HR, McArdle CS. Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss. Nutr Cancer. 2001;39:210–3.

    Article  CAS  Google Scholar 

  20. Cengiz O, Kocer B, Sürmeli S, Santicky M-J, Soran A. Are pretreatment serum albumin and cholesterol levels prognostic tools in patients with colorectal carcinoma? Med Sci Monit. 2006;12:CR240–CR247.

    CAS  PubMed  Google Scholar 

  21. Almasaudi AS, Dolan RD, Edwards CA, McMillan DC. Hypoalbuminemia reflects nutritional risk, body composition and systemic inflammation and is independently associated with survival in patients with colorectal cancer. Cancers. 2020;12:1986.

    Article  CAS  Google Scholar 

  22. Feliciano EMC, Kroenke CH, Meyerhardt JA, Prado CM, Bradshaw PT, Kwan ML, et al. Association of systemic inflammation and sarcopenia with survival in nonmetastatic colorectal cancer: results from the C SCANS study. JAMA Oncol. 2017;3:e172319–e172319.

    Article  Google Scholar 

  23. Okugawa Y, Toiyama Y, Yamamoto A, Shigemori T, Kitamura A, Ichikawa T, et al. Close relationship between immunological/inflammatory markers and myopenia and myosteatosis in patients with colorectal cancer: a propensity score matching analysis. J Parenter Enter Nutr. 2019;43:508–15.

    Article  CAS  Google Scholar 

  24. Boonpipattanapong T, Chewatanakornkul S. Preoperative carcinoembryonic antigen and albumin in predicting survival in patients with colon and rectal carcinomas. J Clin Gastroenterol. 2006;40:592–5.

    Article  CAS  Google Scholar 

  25. Chittezhath M, Dhillon MK, Lim JY, Laoui D, Shalova IN, Teo YL, et al. Molecular profiling reveals a tumor-promoting phenotype of monocytes and macrophages in human cancer progression. Immunity. 2014;41:815–29.

    Article  CAS  Google Scholar 

  26. Qian B-Z, Pollard JW. Macrophage diversity enhances tumor progression and metastasis. Cell. 2010;141:39–51.

    Article  CAS  Google Scholar 

  27. Chan JC, Chan DL, Diakos CI, Engel A, Pavlakis N, Gill A, et al. The lymphocyte-to-monocyte ratio is a superior predictor of overall survival in comparison to established biomarkers of resectable colorectal cancer. Ann Surg. 2017;265:539.

    Article  Google Scholar 

  28. Grothey A, Sobrero AF, Shields AF, Yoshino T, Paul J, Taieb J, et al. Duration of adjuvant chemotherapy for stage III colon cancer. N Engl J Med. 2018;378:1177–88.

    Article  CAS  Google Scholar 

  29. Danielsen H, Hveem T, Domingo E, Pradhan M, Kleppe A, Syvertsen R, et al. Prognostic markers for colorectal cancer: estimating ploidy and stroma. Ann Oncol. 2018;29:616–23.

    Article  CAS  Google Scholar 

  30. Suzuki S, Akiyoshi T, Oba K, Otsuka F, Tominaga T, Nagasaki T, et al. Comprehensive comparative analysis of prognostic value of systemic inflammatory biomarkers for patients with stage II/III colon cancer. Ann Surg Oncol. 2020;27:844–52.

    Article  Google Scholar 

Download references

Acknowledgements

We appreciate Mi Ae Lee, R.N. for assisting the collection of clinical data.

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1F1A1063000).

Author information

Author notes

  1. These authors contributed equally: Sang-Hyuk Jung, Jie Hao.

Authors and Affiliations

  1. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sang-Hyuk Jung, Jie Hao, Manu Shivakumar, Yonghyun Nam, Eun Kyung Choe, Dokyoon Kim & Ji Won Park

  2. Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea

    Sang-Hyuk Jung & Hong-Hee Won

  3. Department of Computer Engineering, Ajou University, Suwon, Republic of Korea

    Jaesik Kim

  4. Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea

    Min Jung Kim, Seung-Bum Ryoo, Eun Kyung Choe, Seung-Yong Jeong, Kyu Joo Park & Ji Won Park

  5. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea

    Min Jung Kim, Seung-Bum Ryoo, Seung-Yong Jeong & Ji Won Park

  6. Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea

    Eun Kyung Choe

  7. Center for Colorectal Cancer, National Cancer Center, Goyang, Republic of Korea

    Sung Chan Park, Dae Kyung Sohn & Jae Hwan Oh

  8. Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea

    Hong-Hee Won

  9. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Dokyoon Kim

Authors
  1. Sang-Hyuk Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manu Shivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yonghyun Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jaesik Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min Jung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seung-Bum Ryoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Eun Kyung Choe
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Seung-Yong Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kyu Joo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sung Chan Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dae Kyung Sohn
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jae Hwan Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Hong-Hee Won
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Dokyoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ji Won Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S-HJ, DK and JWP conceived and designed the study. MJK, S-BR, S-YJ, KJP, SCP, DKS, JHO and JWP collected the data. S-HJ, JH, DK and JWP analysed and interpreted the data. S-HJ did the statistical analysis. S-HJ, JH and JWP wrote the manuscript. MS, YN, JK, EKC, MJK, S-BR, S-YJ, KJP, SCP, DKS, JHO, H-HW and DK read and provided critical revision of the manuscript for intellectual contents; and all authors read and approved the final manuscript.

Corresponding authors

Correspondence to Dokyoon Kim or Ji Won Park.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board (no. 2101-042-1187, NCC2021-0057).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, SH., Hao, J., Shivakumar, M. et al. Development and validation of a novel strong prognostic index for colon cancer through a robust combination of laboratory features for systemic inflammation: a prognostic immune nutritional index. Br J Cancer 126, 1539–1547 (2022). https://doi.org/10.1038/s41416-022-01767-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41416-022-01767-w

This article is cited by

Search

Advanced search

Quick links