Association of the global inflammatory-nutritional index (GINI) with time to next treatment and overall survival in patients with metastatic colorectal cancer receiving third-line therapy: a real-world retrospective study

Introduction

Colorectal cancer (CRC) represents a major global oncologic burden, ranking third in incidence and second in mortality among malignant tumors (1). Approximately 30% of patients present with distant metastases at the time of initial diagnosis, and more than 50% of patients with early-stage disease progress to metastatic colorectal cancer (mCRC) after surgery (2). Despite advances in surgical management and recent progress in targeted therapy and immunotherapy, long-term survival gains remain limited for patients with mCRC whose disease continues to progress after multiple lines of treatment (3,4). Thus, identification of reliable biomarkers is urgently needed in clinical practice to guide treatment decisions, refine risk stratification, and support individualized precision therapy.

Current evidence indicates that systemic inflammatory response, immune function, and nutritional status are major host-related determinants of tumor biology and clinical prognosis (5-7). Although single-parameter hematologic markers, including the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and prognostic nutritional index (PNI), have demonstrated prognostic utility, their capacity to reflect the complex and dynamic interactions within the tumor microenvironment remains limited (8,9). In this context, ratio-based single-dimensional indices proposed in recent years such as cholesterol to lymphocyte ratio (CLR), C-reactive protein to albumin ratio (CAR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), etc. quantify inflammatory, coagulation, or immunological status through simple ratios derived from routine blood parameters, and have demonstrated improved predictive performance across multiple solid tumors, offering an additional approach for prognostic stratification in highly heterogeneous mCRC (10-12).

To overcome the limitations of conventional indices, more sophisticated multidimensional biomarkers have been introduced. Among these, the global inflammatory-nutritional index (GINI), calculated as [(C-reactive protein (CRP) × platelets × monocytes × neutrophils)/(albumin × lymphocytes)), has emerged as a robust predictor in oncology. In glioblastoma patients, GINI demonstrated superior prognostic value compared to established indices such as the systemic immune-inflammation index, with high GINI levels (≥1,350) being independently associated with inferior progression-free survival and overall survival (OS) (13). Similarly, in patients with esophageal squamous cell carcinoma undergoing neoadjuvant immunochemotherapy, GINI was identified as an independent predictor of pathological response and survival outcomes, outperforming traditional ratios including the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in discriminative ability (14). In stage IIIC non-small cell lung cancer, GINI has also been shown to be an independent predictor of progression-free survival and OS, exhibiting greater predictive power than conventional tumor-node-metastasis (TNM) staging (15). Despite its promising performance in other solid tumors, the clinical utility of GINI in the setting of refractory mCRC-particularly in predicting time to next treatment (TTNT), a key real-world surrogate endpoint reflecting treatment resistance and disease progression (16), has not been systematically examined. Addressing this gap is clinically meaningful for informing subsequent treatment selection and optimizing nutritional management. Current evidence predominantly originates from early-stage or first-line treatment populations, creating a gap in the development of composite prognostic tools specifically tailored for patients with refractory mCRC. Within the context of third-line therapy, there remains a marked scarcity of integrated biomarkers capable of capturing multidimensional biological information, which hampers precise risk stratification and individualized management in this highly heterogeneous patient subgroup. Therefore, this study aims to fill this knowledge gap by evaluating the prognostic and predictive value of the GINI index in a cohort of patients with mCRC receiving third-line therapy, with a specific focus on its association with TTNT and OS.

Accordingly, a retrospective analysis was conducted on 320 patients with mCRC who received third-line therapy to evaluate the clinical utility of GINI. The present study further assessed whether GINI could serve as an independent predictor of TTNT and OS, with the goal of providing evidence to support improved clinical management and nutritional strategies in patients with refractory mCRC. We present this article in accordance with the STROBE reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0195/rc).

Methods

Patient sources and ethics

Data were obtained from the Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine and analyzed retrospectively. Medical records from 934 patients admitted to the ward between 2015 and 2020 were reviewed. The study protocol was approved by the Institutional Review Board of Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine (ethics No. 2023SHL-KY-93-01). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. As the study used anonymous and retrospective data, the requirement for the informed consent from patients was waived.

Study patients

Patients were included if they met the following criteria: (I) a diagnosis of mCRC with available survival data; (II) receipt of third-line therapy (refers to treatment administered after failure of first- and second-line therapies) (17); (III) age ≥18 years; and (IV) exclusion of patients with incomplete covariate data.

Study variables

The primary variable of interest was GINI (15), calculated using laboratory results obtained prior to initiation of third-line therapy within 1 week before the initiation of third-line therapy (at baseline).

Demographic variables extracted from the database included age, sex, and body mass index (BMI). Malignancy-related variables included primary tumor site, gene mutation status, metastatic site, number of metastatic lesions, and synchronous and metachronous metastases. Comorbidities included hypertension, diabetes, and cardiac insufficiency.

Outcome measures

The primary outcome was TTNT following third-line therapy (16), and the secondary outcome was OS. TTNT is defined as the duration from the initiation of third‑line therapy to the start of any subsequent treatment or patient death.

Statistical analysis

All calculations and statistical analyses were conducted in R (version 4.3.3). Categorical variables were summarized as frequencies (n) and percentages (%), with group comparisons performed using the Chi-squared test or Fisher’s exact test, as appropriate. Patients were categorized into three groups according to GINI tertiles. Survival differences among groups were assessed using the log-rank test. ROC curve analysis and Kaplan-Meier survival analysis were applied to evaluate the association between GINI and endpoint events. Prognostic factors associated with OS and TTNT were first identified through univariable Cox proportional hazards regression. Variables that reached a significance level of P<0.1 in the univariable analysis were subsequently incorporated into a multivariable Cox proportional hazards model. Additionally, to further evaluate the independent association between the GINI index and both OS and TTNT, indicators that showed P>0.05 in either the univariable Cox analysis, multivariable Cox analysis, or baseline characteristic analysis were also included in the final multivariable regression model. In addition, restricted cubic spline (RCS) regression was used to assess potential nonlinear relationships between GINI and outcome measures. Statistical significance was defined as P<0.05.

The prognostic performance of GINI for predicting 1-, 3-, and 5-year mortality was further evaluated across prespecified subgroups, including age, sex, BMI, primary tumor site, gene mutation status, metastatic site, number of metastatic lesions, synchronous and metachronous metastases, hypertension, diabetes, and cardiac insufficiency.

Results

Patient inclusion flowchart

A total of 934 inpatient records of CRC patients were screened, as shown in Figure 1. Patients without metastasis throughout the study inclusion period, those who did not receive third-line therapy, and those lacking pre-third-line therapy measurements of CRP, platelets, monocytes, neutrophils, albumin, and lymphocytes were excluded. Patients aged <18 years were also excluded. Ultimately, data from 320 patients with mCRC were included in the analysis.

Figure 1 Patient selection process. The H group represents patients in the highest GINI tertile (>485), the M group represents patients in the intermediate tertile [16–485], and the L group represents patients in the lowest tertile (<16). GINI, global inflammatory-nutritional index; mCRC, metastatic colorectal cancer.

Comparisons among different GINI groups

Baseline characteristics across the three GINI tertile groups are summarized in Table 1. No significant between-group differences were observed in age, sex, primary tumor site, gene mutation status, metastatic site, number of metastases, synchronous and metachronous metastases, liver metastases, hypertension, diabetes, or cardiac insufficiency (P>0.05). In contrast, BMI, lung metastases, and peritoneal metastases differed significantly across groups (P<0.05). There was no statistically significant difference in the distribution of third-line treatment regimens (regorafenib, TAS102, fruquintinib, and other regimens) among the groups (P>0.05).

Association between GINI index and mortality risk

Kaplan-Meier survival curves were used to evaluate 1-, 3-, 5-year, and all-cause mortality across GINI tertiles (Figure 2A-2C). Significant differences were observed in 1-, 3-, and 5-year OS among the three groups (P<0.05), with a higher all-cause mortality risk identified in the H group (P<0.05).

Figure 2 Kaplan-Meier survival curves for mortality. (A) Kaplan-Meier curve of GINI and 1-year mortality (P=0.041). (B) Kaplan-Meier curve of GINI and 3-year mortality (P=0.03). (C) Kaplan-Meier curve of GINI and 5-year mortality (P<0.001). L group: low GINI group. H group: high GINI group. M group: moderate GINI group. GINI, global inflammatory-nutritional index; OS, overall survival.

To further assess the association between the GINI index and mortality, we initially performed a univariable Cox proportional hazards regression analysis, which included variables such as the GINI index, sex, BMI, and lung metastasis. Variables that met the screening criteria were subsequently incorporated into a multivariable Cox proportional hazards model. This analysis confirmed that the GINI index is an independent predictor of OS (Table S1). In a further adjusted multivariable Cox model that included sex, BMI, lung metastasis, and peritoneal metastasis, a statistically significant difference in OS was observed between the H (high GINI) and L (low GINI) groups (P<0.05), as detailed in Tables 2-5.

To evaluate the relationship between GINI as a continuous variable and survival outcomes, time-dependent ROC analysis was performed (Figure 3A-3D). Favorable predictive performance was observed at 1, 3, and 4.9 years.

Figure 3 ROC analysis of GINI with mortality. (A-C) Time-dependent ROC analysis evaluating the association between GINI and mortality within 1 year (A), 3 years (B), and 5 years (C); 4.9 years was used for the 5-year analysis due to the limited number of patients in each group. AUC, area under the curve; FPR; GINI, global inflammatory-nutritional index; ROC, receiver operating characteristic; RCS, restricted cubic spline; TPR.

RCS modeling was further applied to assess the association between GINI and mortality (Figure 4A-4C). A significant linear relationship was identified between GINI and OS (overall P<0.01; non-linear P≥0.05).

Figure 4 RCS analysis of GINI with mortality. (A-C) Restricted cubic spline regression analysis evaluating the association between GINI and mortality within 1 year (A), 3 years (B), and 5 years (C). CI, confidence interval; GINI, global inflammatory-nutritional index; HR, hazard ratio; RCS, restricted cubic spline.

Association between GINI index and TTNT following third-line therapy

Kaplan-Meier curves were generated to evaluate the TTNT across GINI tertiles (Figure 5). A significant difference in TTNT was observed among the three groups (P<0.05).

Figure 5 Kaplan-Meier survival curves for TTNT. L group. H group. M group. OS, overall survival; TTNT, time to next treatment.

To further investigate the relationship between the GINI index and TTNT following third-line therapy, a univariable Cox proportional hazards regression model was first constructed. Variables including the GINI index, sex, BMI, and liver metastasis were screened and subsequently incorporated into a multivariable Cox proportional hazards regression model. The results confirmed the GINI index as an independent predictor of TTNT (Table S2). A subsequent multivariable Cox proportional hazards analysis, adjusted for sex, BMI, liver metastasis, lung metastasis, and peritoneal metastasis, demonstrated significant differences in TTNT between the H (high GINI) and M (moderate GINI) groups, as well as between the H and L (low GINI) groups (P<0.05), as shown in Table 5.

To assess the association between GINI and TTNT following third-line therapy, time-dependent ROC analysis was performed (Figure 6A-6C). Favorable predictive performance was observed at 6, 12, and 18 months.

Figure 6 ROC analysis of GINI with third-line TTNT. (A-C) Time-dependent ROC analysis evaluating the association between GINI and mortality within 6 months (A), 12 months (B), and 18 months (C). AUC, area under the curve; FPR, GINI, global inflammatory-nutritional index; ROC, receiver operating characteristic; RCS, restricted cubic spline; TPR, TTNT, time to next treatment.

RCS modeling was further applied to evaluate the relationship between GINI and TTNT following third-line therapy (Figure 7). A nonlinear association was identified between GINI and TTNT following third-line therapy (overall P<0.05; non-linear P<0.05).

Figure 7 RCS analysis of GINI with three-line TTNT. CI, confidence interval; GINI, global inflammatory-nutritional index; HR, hazard ratio; RCS, restricted cubic spline; TTNT, time to next treatment.

Subgroup analysis

To validate the associations of GINI with 1-, 3-, and 5-year inpatient OS and with TTNT following third-line therapy, subgroup analyses were conducted according to age, sex, BMI, primary tumor site, gene mutation status, metastatic site, number of metastatic lesions, synchronous and metachronous metastases, hypertension, diabetes, and cardiac insufficiency (Figure 8A-8D). Multiple metastases [hazard ratio (HR) =0.751; 95% confidence interval (CI): 0.578–0.975] were significantly associated with 3-year mortality (Figure 8B). Regorafenib (HR =1.638; 95% CI: 1.050–2.556) was significantly associated with 5-year mortality (Figure 8C). Non-obese status (HR =0.557; 95% CI: 0.386–0.805), lung metastases (HR =1.301; 95% CI: 1.025–1.651), and bone metastases (HR =0.651; 95% CI: 0.466–0.911) were significantly associated with TTNT following third-line therapy (Figure 8D).

Figure 8 Subgroup analysis for mortality/third-line TTNT. (A-D) Subgroup analyses evaluating the association between GINI and mortality at 1 year (A), 3 years (B), and 5 years (C), as well as TTNT following third-line therapy (D). BMI, body mass index; CI, confidence interval; DM, diabetes mellitus; GINI, global inflammatory-nutritional index; HR, hazard ratio; TTNT, time to next treatment.

Discussion

To our knowledge, this study is the first to systematically evaluate and validate the prognostic utility of the novel composite biomarker GINI in mCRC patients receiving third-line therapy. By integrating systemic inflammatory burden, coagulation status, innate immune activity, and nutritional-metabolic indices, GINI provides a multidimensional measure of the complex interplay between host biology and tumor progression. The primary observation from this retrospective analysis was that elevated GINI scores were independently associated with significantly shorter TTNT and OS. Although prior studies have demonstrated the prognostic relevance of single-parameter biomarkers, such as the NLR and PNI, in earlier-stage disease (18,19), the present results extend these associations to later-line settings characterized by greater therapeutic difficulty. Collectively, these findings support the use of GINI as a risk-stratification tool capable of identifying patients at increased risk of rapid progression and treatment failure during third-line therapy for CRC.

The present results are consistent with, and expand upon, accumulating evidence linking systemic inflammation to unfavorable oncologic outcomes. Prior studies, including the analysis by Rembiałkowska et al., have demonstrated that elevated inflammatory markers such as CRP and neutrophils are associated with resistance to chemotherapy and targeted agents (20). In addition, nutritional status has been shown to influence the feasibility and continuity of subsequent-line treatment (21). However, most established scoring systems, including the Glasgow Outcome Scale and NLR, primarily capture a single dimension of inflammation or nutrition. In patients with mCRC who have received multiple lines of therapy, the tumor microenvironment is often characterized by concerted dysregulation, including persistent inflammation, immune exhaustion, and malnutrition (22). By placing pro-tumor components (CRP, platelets, monocytes, neutrophils) in the numerator and host anti-tumor reserves (albumin, lymphocytes) in the denominator, the GINI index may provide a more integrated biological representation than conventional single-parameter markers. This framework may account for the superior predictive performance of GINI observed in this cohort compared with reports focused solely on NLR (18). Moreover, unlike the study by Fucà et al., which evaluated first-line treatment only (23), the present analysis specifically addresses unmet need for prognostic assessment in the third-line setting, in which physiologic heterogeneity is greater and conventional tools may demonstrate reduced discriminatory capacity.

The prognostic capacity of the GINI index is supported by a clear biological rationale. Its molecular components, including CRP, neutrophils, monocytes, and platelets, collectively indicate a pro-inflammatory and pro-angiogenic milieu. Neutrophils and monocytes may differentiate into tumor-associated phenotypes (TANs and TAMs), suppressing cytotoxic T-cell activity and promoting metastatic dissemination through cytokine release, including VEGF and IL-6 (24,25). Platelets may also enable circulating tumor cells to evade immune surveillance, thereby increasing tumor survival and metastatic potential (26).

In contrast, the denominator reflects host defense capacity and physiologic reserve. Lymphocytes represent central effector cells in antitumor immunity, whereas albumin serves as an indicator of nutritional status and protein stores. Reduced denominator values typically indicate immune impairment and cachexia; conditions associated with increased chemotherapy toxicity and adverse prognosis (27,28). Accordingly, an elevated GINI score may represent a biologically meaningful tipping point at which pro-tumor inflammation predominates over host immune defenses, consistent with the accelerated clinical deterioration observed in the high-GINI group.

A key strength of this study lies in clarifying the association between the GINI index and the clinically meaningful endpoint of TTNT. In the third-line setting, therapeutic objectives often shift from cure to disease control and preservation of quality of life, and TTNT may sensitively reflect treatment failure and tolerability (29). The present data indicated that higher baseline GINI scores were associated with significantly shorter intervals between the current regimen and subsequent intervention. This finding suggests that routine third-line monotherapy may be inadequate for patients classified as high risk by GINI. In such cases, closer surveillance may be warranted (e.g., shortening assessment intervals from every 8–10 weeks to every 6 weeks), along with earlier implementation of optimized supportive care and nutritional interventions to stabilize host microenvironment (30). Moreover, because GINI is derived exclusively from routine, low-cost blood tests, it offers high accessibility and affordability while avoiding the substantial financial burden of genomic assays such as ctDNA, supporting dynamic risk stratification across diverse resource settings.

Although the results are encouraging, several limitations are present. First, as a single-center, real-world retrospective analysis, it is inherently susceptible to potential selection bias. Future validation in prospective cohorts and well-annotated biobanks is warranted to confirm the prognostic value of the GINI index in broader populations. Second, GINI was derived exclusively from baseline measurements and did not capture longitudinal changes during treatment; dynamic assessment may improve prediction of treatment response and warrants further evaluation in future studies (31). Third, although the GINI index integrates multidimensional inflammatory, nutritional, and immune information to provide a more comprehensive prognostic tool, its predictive accuracy relative to conventional biomarkers—such as inflammation-nutrition indices (e.g., NLR, PNI) and tumor markers (CEA, CA19-9)—requires further comparative evaluation. Fourth, TTNT can be influenced by non-biological factors, including treatment decisions, patient preferences, and healthcare accessibility, rather than solely reflecting tumor progression. Therefore, the association between the GINI index and TTNT should be interpreted with caution. Additionally, as a pretreatment biomarker, the GINI index may be confounded by prior therapeutic responses and tumor evolution, which represents another limitation of this retrospective analysis.

To address these limitations and facilitate the translation of our findings into clinical practice, we propose the following directions for future research: (I) external validation in independent, preferably multicenter, cohorts to enhance generalizability; (II) determination of an optimal clinical cutoff for the GINI index via receiver operating characteristic (ROC) analysis in larger samples; (III) exploration of the applicability of the GINI-based model in other treatment-line settings; and (IV) prospective, preferably interventional, studies to evaluate whether strategies aimed at improving inflammatory and nutritional status in patients with high GINI scores can lead to better clinical outcomes. Establishing such a causal relationship would help transform the GINI index from a purely prognostic biomarker into a clinically actionable tool for guiding personalized therapeutic and supportive care in patients with mCRC.

Conclusions

In summary, GINI represents a novel, readily accessible, and biologically grounded composite biomarker that independently predicts shorter TTNT and OS in patients with mCRC receiving third-line therapy. By reflecting the balance between systemic inflammatory burden and host immuno-nutritional status, GINI provides prognostic information beyond conventional staging. Integration of this index into clinical workflows may support more individualized management, including optimized surveillance and supportive care for patients at highest risk. Prospective studies are needed to confirm these findings and to evaluate interventions aimed at modifying GINI to improve clinical outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0195/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0195/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0195/prf

Funding: This work was sponsored by Shanghai Rising-Star Program (No. 23YF1442900), the National Natural Science Foundation of China (Nos. 82305330 and 82205207), the Shanghai Municipal Health Commission Clinical Research Project (No. 20254Y0050), and the Jiading District Natural Science Research Project (No. JDKW-2025-0065).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0195/coif). X.Y. reports funding support from the Jiading District Natural Science Research Project (No. JDKW-2025-0065). T.Z. reports funding support from funding from the Sponsored by Shanghai Rising-Star Program (No. 23YF1442900), National Natural Science Foundation of China (Nos. 82305330 and 82205207) and the Shanghai Municipal Health Commission Clinical Research Project (No. 20254Y0050). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study protocol was approved by the Institutional Review Board of Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine (ethics No. 2023SHL-KY-93-01). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. As the study used anonymous and retrospective data, the requirement for the informed consent from patients was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. The global, regional, and national burden of cancer, 1990-2023, with forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2023. Lancet 2025;406:1565-86.
2. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
3. Ciardiello F, Ciardiello D, Martini G, et al. Clinical management of metastatic colorectal cancer in the era of precision medicine. CA Cancer J Clin 2022;72:372-401. [Crossref] [PubMed]
4. Dasari A, Lonardi S, Garcia-Carbonero RFRESCO-2 Study Investigators, et al. Fruquintinib versus placebo in patients with refractory metastatic colorectal cancer (FRESCO-2): an international, multicentre, randomised, double-blind, phase 3 study. Lancet 2023;402:41-53.
5. Wang Y, Zhang L, Shi G, et al. Effects of Inflammatory Response Genes on the Immune Microenvironment in Colorectal Cancer. Front Genet 2022;13:886949. [Crossref] [PubMed]
6. Imen H, Amira H, Fatma K, et al. Prognostic Value of Immunoscore in Colorectal Carcinomas. Int J Surg Pathol 2024;32:58-65. [Crossref] [PubMed]
7. Vatvani AD, Patel P, Shahannaz DC, et al. Controlling Nutritional Status (CONUT) as a predictor of survival and complications among colorectal cancer patients: A systematic review and meta-analysis. Colorectal Dis 2025;27:e70120. [Crossref] [PubMed]
8. Xu N, Zhang JX, Zhang JJ, et al. The prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in colorectal cancer and colorectal anastomotic leakage patients: a retrospective study. BMC Surg 2025;25:57. [Crossref] [PubMed]
9. Wei H, Wang J, Jiang Z, et al. Preoperative prognostic nutritional index associated with anastomotic leakage in colorectal cancer patients after surgery. Adv Clin Exp Med 2024;33:351-9. [Crossref] [PubMed]
10. Meng Y, Long C, Huang X, et al. Prognostic role and clinical significance of C-reactive protein-lymphocyte ratio in colorectal cancer. Bioengineered 2021;12:5138-48. [Crossref] [PubMed]
11. Ishizuka M, Nagata H, Takagi K, et al. Clinical Significance of the C-Reactive Protein to Albumin Ratio for Survival After Surgery for Colorectal Cancer. Ann Surg Oncol 2016;23:900-7. [Crossref] [PubMed]
12. Chen W, Xin S, Xu B. Value Research of NLR, PLR, and RDW in Prognostic Assessment of Patients with Colorectal Cancer. J Healthc Eng 2022;2022:7971415. [Crossref] [PubMed]
13. Feng Z, Yi H, Xiao J, et al. The global immune-nutrition-inflammation index predicts pathological response and survival in esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy. Front Nutr 2025;12:1717477. [Crossref] [PubMed]
14. Yamamoto S, Aoyama T, Maezawa Y, et al. The Global Immune-Nutrition-Information Index (GINI) Is an Independent Prognostic Factor for Esophageal Cancer Patients Who Receive Curative Treatment. Cancer Diagn Progn 2025;5:115-21. [Crossref] [PubMed]
15. Topkan E, Selek U, Pehlivan B, et al. The Prognostic Value of the Novel Global Immune-Nutrition-Inflammation Index (GINI) in Stage IIIC Non-Small Cell Lung Cancer Patients Treated with Concurrent Chemoradiotherapy. Cancers (Basel) 2023;15:4512. [Crossref] [PubMed]
16. Dong S, Wang Z, Zhang JT, et al. Circulating Tumor DNA-Guided De-Escalation Targeted Therapy for Advanced Non-Small Cell Lung Cancer: A Nonrandomized Controlled Trial. JAMA Oncol 2024;10:932-40. [Crossref] [PubMed]
17. Benson AB, Venook AP, Adam M, et al. Colon Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2024;22:e240029. [Crossref] [PubMed]
18. Naszai M, Kurjan A, Maughan TS. The prognostic utility of pre-treatment neutrophil-to-lymphocyte-ratio (NLR) in colorectal cancer: A systematic review and meta-analysis. Cancer Med 2021;10:5983-97. [Crossref] [PubMed]
19. Keskinkilic M, Semiz HS, Ataca E, et al. The prognostic value of immune-nutritional status in metastatic colorectal cancer: Prognostic Nutritional Index (PNI). Support Care Cancer 2024;32:374. [Crossref] [PubMed]
20. Rembiałkowska N, Kocik Z, Kłosińska A, et al. Inflammation-Driven Genomic Instability: A Pathway to Cancer Development and Therapy Resistance. Pharmaceuticals (Basel) 2025;18:1406. [Crossref] [PubMed]
21. Wang J, Huang Y, Zheng X, et al. Effect of Nutritional Intervention on Chemotherapy Tolerance and Quality of Life in Patients with Colorectal Cancer Undergoing Postoperative Chemotherapy: A Randomized Controlled Study. Nutr Cancer 2025;77:414-23. [Crossref] [PubMed]
22. Mecca M, Picerno S, Cortellino S. The Killer's Web: Interconnection between Inflammation, Epigenetics and Nutrition in Cancer. Int J Mol Sci 2024;25:2750. [Crossref] [PubMed]
23. Fucà G, Guarini V, Antoniotti C, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. Br J Cancer 2020;123:403-9. [Crossref] [PubMed]
24. Mizuno R, Kawada K, Itatani Y, et al. The Role of Tumor-Associated Neutrophils in Colorectal Cancer. Int J Mol Sci 2019;20:529. [Crossref] [PubMed]
25. Jeong J, Suh Y, Jung K. Context Drives Diversification of Monocytes and Neutrophils in Orchestrating the Tumor Microenvironment. Front Immunol 2019;10:1817. [Crossref] [PubMed]
26. Wu Q, You L, Nepovimova E, et al. Hypoxia-inducible factors: master regulators of hypoxic tumor immune escape. J Hematol Oncol 2022;15:77. [Crossref] [PubMed]
27. Ilham S, Willis C, Kim K, et al. Cancer incidence in immunocompromised patients: a single-center cohort study. BMC Cancer 2023;23:33. [Crossref] [PubMed]
28. Compton SLE, Heymsfield SB, Brown JC. Nutritional Mechanisms of Cancer Cachexia. Annu Rev Nutr 2024;44:77-98. [Crossref] [PubMed]
29. Walker MS, Herms L, Miller PJE. Performance of time to discontinuation and time to next treatment as proxy measures of progression-free survival, overall and by treatment group. J Clin Oncol 2020;38:e19135.
30. Ghosh N, Wang R, Qureshi ZP, et al. Impact of ibrutinib dose adjustment on TTNT in first-line CLL/SLL: a real-world analysis using target trial emulation. Blood Neoplasia 2024;1:100022. [Crossref] [PubMed]
31. Kaminsky A, Fahmy LM, Bilgrami Z, et al. Survival and Treatment Response in CTCL Can be Predicted Using Volumetric Lymph Node Size and Dynamic Changes in Lymph Node Size and Skin Score. Blood 2024;144:4446.