Pan‑immune‑inflammatory values predict survival in patients after radical surgery for non‑metastatic colorectal cancer: A retrospective study

The present study aimed to further investigate the predictive value of pan-immune-inflammatory values (PIVs) in patients with non-metastatic colorectal cancer after radical surgery. Additionally, the study sought to develop a predictive scoring model to predict the survival of patients with colorectal cancer after surgery. A total of 470 non-metastatic patients who attended The Affiliated Cancer Hospital of Xinjiang Medical University (Urumqi, China) for radical colorectal cancer surgery were retrospectively collected based on specific inclusion and exclusion criteria. Patients were categorized into the Low-PIV group and the High-PIV group according to the optimal cut-off value of PIV and a survival analysis was performed. Cox regression was performed for one-way multifactorial analysis. After independent risk factors were screened, a simple score prediction model was constructed and evaluated. The study indicated that PIV was significantly associated with the T stage, Tumor-Node-Metastasis stage, differentiated degree and nerve invasion (all P<0.05). Survival tanalysis showed that patients in the Low-PIV group had a significantly higher 5-year overall survival (OS) rate compared with those in the High-PIV group (88.7 vs. 46.3%; P<0.001). Through multifactorial Cox regression analysis, N stage [hazard ratio (HR), 2.00; 95% confidence interval (CI), 1.04-3.84; P=0.039], differentiated degree (HR, 1.98; 95% CI, 1.16-3.38; P=0.012), neutrophil/lymphocyte ratio (HR, 4.00; 95% CI, 2.19-7.29; P<0.001) and PIV (HR, 4.12; 95% CI, 2.04-8.32; P<0.001) were found to be independent predictors of OS. These variables were included in a column chart to create a scoring system. The concordance index was 0.789 (95% CI, 0.746-0.832). The 1-, 3- and 5-year calibration curves for this column-line diagram demonstrated high confidence. The area under the curve of the predictive model for 1-, 3- and 5-year OS were 0.823, 0.845 and 0.845, respectively. PIV was shown to be a reliable biomarker for the prognosis of patients with non-metastatic colorectal cancer. The current simple predictive scoring model can predict the effective survival of patients following radical surgery for non-metastatic colorectal cancer and could therefore play a substantial role in clinical decision-making.

Authors

Institutions

Publication Details

Journal
Oncology Letters
Published
2025-02-24
DOI
https://doi.org/10.3892/ol.2025.14943
Citations
34
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
35.54

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pan‑immune‑inflammatory values predict survival in patients after radical surgery for non‑metastatic colorectal cancer: A retrospective study

Xiangyue Zeng, Zhimin Wu, Ziyi Zhang, Kuan Wang et al.
34 citations
Oncology Letters
Inflammatory Biomarkers in Disease Prognosis
35.54
article

Pan‑immune‑inflammatory values predict survival in patients after radical surgery for non‑metastatic colorectal cancer: A retrospective study

Xiangyue Zeng, Zhimin Wu, Ziyi Zhang, Kuan Wang, Yipeng Pan, Yi Chen, Zeliang Zhao, Kejin Li
article en
34 citations

Abstract

The present study aimed to further investigate the predictive value of pan-immune-inflammatory values (PIVs) in patients with non-metastatic colorectal cancer after radical surgery. Additionally, the study sought to develop a predictive scoring model to predict the survival of patients with colorectal cancer after surgery. A total of 470 non-metastatic patients who attended The Affiliated Cancer Hospital of Xinjiang Medical University (Urumqi, China) for radical colorectal cancer surgery were retrospectively collected based on specific inclusion and exclusion criteria. Patients were categorized into the Low-PIV group and the High-PIV group according to the optimal cut-off value of PIV and a survival analysis was performed. Cox regression was performed for one-way multifactorial analysis. After independent risk factors were screened, a simple score prediction model was constructed and evaluated. The study indicated that PIV was significantly associated with the T stage, Tumor-Node-Metastasis stage, differentiated degree and nerve invasion (all P<0.05). Survival tanalysis showed that patients in the Low-PIV group had a significantly higher 5-year overall survival (OS) rate compared with those in the High-PIV group (88.7 vs. 46.3%; P<0.001). Through multifactorial Cox regression analysis, N stage [hazard ratio (HR), 2.00; 95% confidence interval (CI), 1.04-3.84; P=0.039], differentiated degree (HR, 1.98; 95% CI, 1.16-3.38; P=0.012), neutrophil/lymphocyte ratio (HR, 4.00; 95% CI, 2.19-7.29; P<0.001) and PIV (HR, 4.12; 95% CI, 2.04-8.32; P<0.001) were found to be independent predictors of OS. These variables were included in a column chart to create a scoring system. The concordance index was 0.789 (95% CI, 0.746-0.832). The 1-, 3- and 5-year calibration curves for this column-line diagram demonstrated high confidence. The area under the curve of the predictive model for 1-, 3- and 5-year OS were 0.823, 0.845 and 0.845, respectively. PIV was shown to be a reliable biomarker for the prognosis of patients with non-metastatic colorectal cancer. The current simple predictive scoring model can predict the effective survival of patients following radical surgery for non-metastatic colorectal cancer and could therefore play a substantial role in clinical decision-making.

Oncology LettersVol. 29(4)
Xinjiang Medical University (CN), Sir Run Run Shaw Hospital (CN), Zhejiang University (CN)
Natural Science Foundation of Xinjiang
Openalex Percentile: Top 0%
Inflammatory Biomarkers in Disease Prognosis
35.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.