Association between lymphopenia and the survival of patients with colorectal cancer: A meta-analysis

Abstract

BACKGROUND

Lymphopenia is a potential prognostic biomarker in colorectal cancer (CRC), but its association with survival remains unclear.

AIM

To evaluate the impact of lymphopenia on progression-free survival (PFS) and overall survival (OS) of CRC in a meta-analysis.

METHODS

We systematically searched PubMed, EMBASE, and Web of Science for cohort studies published up to May 7, 2025, investigating the association between lymphopenia, defined by the absolute lymphocyte count (ALC), and survival in CRC. Hazard ratio (HR) with 95% confidence interval (CI) were pooled using random-effects models by incorporating the possible influence of heterogeneity.

RESULTS

Thirteen retrospective cohort studies were included. Lymphopenia was significantly associated with worse PFS (HR = 2.49, 95%CI: 1.87-3.32, P < 0.001) and OS (HR = 2.10, 95%CI: 1.60-2.76, P < 0.001), with moderate heterogeneity (I² = 43% and 39%, respectively). Subgroup analyses demonstrated that treatment-related lymphopenia had a stronger association with poor PFS than pretreatment lymphopenia (HR = 3.40 vs 1.90; P for subgroup difference = 0.02). PFS was also more adversely affected when ALC was defined using a lower cutoff (< 600/μL vs ≥ 600/μL, HR = 4.43 vs 1.98; P = 0.02). For OS, the associations were consistent across subgroups based on region, age, timing, and ALC cutoffs (all P for subgroup difference > 0.05). According to further meta-regression, the results were not significantly modified by ALC cutoffs.

CONCLUSION

Both pretreatment and treatment-related lymphopenia are potentially associated with worse survival in patients with CRC. These findings support the prognostic value of lymphocyte monitoring in CRC management.

Key Words: Colorectal cancer; Lymphopenia; Absolute lymphocyte count; Survival; Progression; Meta-analysis

Core Tip: This meta-analysis synthesized evidence from 13 retrospective cohort studies involving 3383 patients with colorectal cancer (CRC) to clarify the prognostic significance of lymphopenia. Both pretreatment and treatment-related lymphopenia were significantly associated with poorer progression-free and overall survival. Notably, treatment-related lymphopenia and lower absolute lymphocyte count cutoffs showed stronger associations with disease progression. These findings highlight lymphopenia as a simple, readily available immune biomarker for risk stratification and prognosis assessment in CRC, supporting closer lymphocyte monitoring during clinical management.

INTRODUCTION

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related death globally, accounting for more than 1.9 million new cases and nearly 935000 deaths annually[1,2]. Despite advances in surgical techniques, chemotherapy, and targeted therapies, survival outcomes remain heterogeneous across patients with CRC, particularly those with advanced-stage disease[3]. Prognosis is influenced by various clinicopathological and molecular factors, but considerable variability persists even among patients with similar disease stages and treatment regimens[4]. This underscores the importance of identifying reliable prognostic biomarkers that can refine risk stratification and guide personalized treatment strategies[5].

Lymphopenia, defined as a reduction in the absolute lymphocyte count (ALC), has emerged as a potential prognostic marker in oncology[6]. Lymphocytes play a critical role in antitumor immunity, contributing to immune surveillance, cytotoxic responses, and modulation of the tumor microenvironment[7]. Lymphopenia can arise from the systemic inflammatory response to malignancy, cancer-induced immune suppression, or iatrogenic effects of chemotherapy and radiotherapy[8,9]. These changes can impair the host’s immune competence, leading to reduced treatment efficacy and accelerated tumor progression[9,10]. Several studies explored the prognostic implications of lymphopenia in CRC, but the findings were inconsistent, likely because of variations in the ALC threshold, timing of assessment, and study design[11-23]. Some studies focused on pretreatment lymphopenia as a reflection of the baseline immune status, whereas others investigated treatment-induced lymphopenia as a marker of immune depletion. To address these uncertainties, we conducted a meta-analysis to evaluate the association between lymphopenia and survival outcomes in patients with CRC and examine whether this association is modified by study or patient characteristics.

MATERIALS AND METHODS

This study followed the PRISMA 2020[24,25] and Cochrane Handbook guidelines[24] for conducting systematic reviews and meta-analyses, covering study design, data collection, statistical methods, and interpretation of results. The protocol was also registered in PROSPERO under the ID CRD420251087190.

Database search

To identify studies pertinent to this meta-analysis, we searched PubMed, EMBASE, and Web of Science databases using an extensive array of search terms, which involved the combined terms of: (1) “Lymphopenia” OR “lymphocytopenia”; (2) “Colorectal” OR “colorectum” OR “colon” OR “rectal” OR “rectum”; and (3) “Neoplasms” OR “carcinoma” OR “cancer” OR “tumor” OR “malignancy” OR “adenocarcinoma”. The search strategy was developed according to the PICOS framework and constructed by combining controlled vocabulary terms (e.g., MeSH in PubMed and Emtree in EMBASE) with relevant free-text keywords related to CRC, lymphopenia, and survival outcomes. The strategy was iteratively refined through pilot searches to ensure adequate sensitivity while maintaining specificity. Final search strings were adapted to the syntax requirements of each database. In addition, the reference lists of eligible articles and relevant reviews were manually screened to identify potentially missed studies. The search was restricted to full-length articles published in English in peer-reviewed journals to ensure methodological transparency and data reliability for quantitative synthesis. Grey literature sources (e.g., conference proceedings, clinical trial registries) were not systematically searched because these reports are frequently not peer-reviewed and they often lack sufficient information for extracting adjusted effect estimates. However, we acknowledge that these restrictions can introduce language and publication bias. We also manually checked the references of related original and review articles to find additional relevant studies. The search covered all records from database inception up to May 7, 2025.

Study eligibility criteria

We applied the PICOS framework to define the inclusion criteria.

P (Population): Adults with a histologically confirmed diagnosis of CRC regardless of the histological type, location, stage, or main treatment of the cancer.

I (Exposure): For pretreatment or treatment-related lymphopenia, the cutoff of the ALC for the diagnosis of lymphopenia was consistent with the criteria used in the original studies.

C (Comparison): Patients with and without pretreatment lymphopenia and treatment-related lymphopenia were compared.

O (outcome): Progression-free survival (PFS) and/or overall survival (OS) were compared between patients with and without lymphopenia. PFS was usually defined as the time from treatment start to disease progression or death. OS was defined as the time from treatment start to death from any cause.

S (study design): Observational studies with follow-up, such as cohort studies, nested case-control studies, and post-hoc analyses of clinical trials, were included.

We excluded reviews, editorials, other meta-analyses, preclinical studies, studies that did not include patients with CRC, studies that did not evaluate lymphopenia as exposure, or those that did not report survival outcomes. If studies had overlapping populations, we included the study with the largest sample size in the meta-analysis.

Study quality evaluation

Chen K and Li H independently performed the literature search, study selection, quality assessment, and data extraction. Disagreements were resolved by discussion with the corresponding author. The literature screening process was initiated on May 21, 2025, and data extraction from the included studies began on June 1, 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS)[26], which rates selection, the control of confounders, and outcome evaluation. Scores range from 1 to 9, with scores of 7 or higher considered good quality.

Data collection

The data collected for analysis included the study details (first author, year, study country, and design), patient characteristics (diagnosis, number of patients included in each study, mean age, sex distribution, cancer stages, and main anticancer treatments), details of exposure (such as the timing and cutoffs of ALC for evaluating lymphopenia, and number of patients with lymphopenia), median follow-up durations, outcomes reported, and covariates adjusted in the regression models.

Statistical analysis

We used hazard ratio (HR) and 95% confidence interval (CI) to assess the association between lymphopenia and survival in patients with CRC, including PFS and OS, in patients with and without lymphopenia. HR and SE were directly extracted or calculated from 95%CI or P values and then log-transformed to stabilize variance and normalize the data[24]. If multiple HRs were reported from different models, we used the value with the most complete adjustment. Heterogeneity was assessed using the Cochrane Q test and I² statistic[27], with P < 0.10 suggesting significant heterogeneity and I² of < 25%, 25%-75%, and > 75% indicating low, moderate, and high heterogeneity, respectively. A random-effects model was used to pool the data, accounting for heterogeneity between studies[24]. Sensitivity analyses were conducted by removing one study at a time to validate the robustness of the finding. In addition, sensitivity analysis limited to studies that conducted multivariate analysis was also performed. Predefined subgroup analyses were conducted on the basis of study country (Asian countries vs Western countries), mean patient age, the timing of the lymphopenia evaluation (pretreatment vs treatment-related), the ALC cutoff for the diagnosis of lymphopenia, and follow-up durations. The medians of continuous variables were used to divide subgroups evenly. In addition, univariate meta-regression analysis was performed to evaluate the influence of ALC cutoffs on the results[24]. Publication bias was assessed using funnel plots and visual inspection for asymmetry, along with Egger’s test[28]. All analyses were performed using RevMan (version 5.1; Cochrane Collaboration, London, United Kingdom) and Stata (version 12.0; Stata Corporation, College Station, TX, United States).

RESULTS

Study inclusion

The study selection process is presented in Figure 1. We first identified 1059 records from the three databases. After removing 209 duplicates, 850 articles were screened by title and abstract. Of these, 823 were excluded for not meeting the aims of the meta-analysis. The full texts of the remaining 27 articles were reviewed by two independent authors, and 14 were excluded for various reasons (Figure 1), including non-relevant outcomes, the absence of extractable survival data, non-CRC populations, and overlapping cohorts. Thus, 13 studies were included in the quantitative analysis[11-23].

Figure 1 Flowchart of the database search and study inclusion. Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers). If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools.

Summary of study characteristics

Table 1 presents the key characteristics of the 13 studies included in this meta-analysis, encompassing 3383 patients with CRC. These studies were published between 2011 and 2025 and conducted across various countries, including France, China, Japan, Korea, Israel, Greece, and the United States. All included studies employed retrospective cohort designs and investigated patients with stage I-IV CRC undergoing diverse treatments such as chemotherapy, chemoradiotherapy, and surgical resection. The mean ages of the included patients varied from 56.0 years to 72.0 years, and the proportions of men ranged from 46.1% to 77.0%. The definitions of lymphopenia varied across the studies, with pretreatment lymphopenia analyzed in six studies[11,13-17] and treatment-associated lymphopenia analyzed in the other seven studies[12,18-23]. The ALC thresholds for the diagnosis of lymphopenia ranged 500-1993/μL. The number of patients with lymphopenia per study ranged from 9 to 373. Accordingly, 1088 (32.2%) of the included patients had lymphopenia. The median follow-up duration spanned from 18.5 months to 68.0 months. PFS was reported in 12 studies[11-14,16-23], whereas OS was reported in 10 studies[11-15,17,18,20,22,23]. Adjustment for key prognostic variables such as age, sex, tumor stage, carcinoembryonic antigen (CEA) levels, and treatment modalities was performed in 11 studies, whereas two studies reported unadjusted data only[17,21]. NOS, as a measure of study quality (Table 2), ranged from 6 to 9, indicating moderate to high quality. The score was 7 or higher in 12 studies, reflecting strong methodology in terms of cohort selection, exposure and outcome assessment, and follow-up duration[11-20,22,23]. The score was 6 for one study[21], mainly because of its limited representativeness and lack of adjustment for confounding factors. Overall, the included studies demonstrated robust outcome assessment and adequate follow-up, supporting the reliability of the synthesized survival data.

Table 1 Characteristics of the included studies.

Ref.	Country	Design	Diagnosis	Number of patients	Mean age (years)	Men (%)	Cancer stage	Main treatment	Definition of lymphopenia	Number of patients with lymphopenia	Median follow-up duration (months)	Outcomes reported	Variables adjusted
Cézé et al[11]	France	RC	CRC	260	64.8	60.8	II-IV	Chemotherapy	Pretreatment ALC < 1000/μL	49	18.5	PFS and OS	Age, sex, WHO PS, tumor site, number of metastatic sites, chemotherapy regimen, and ALP level
Chu-Yuan et al[12]	China	RC	CRC	243	59	55.1	II-III	Chemotherapy	Chemotherapy-associated ALC < 660/μL	42	31	PFS and OS	Age, sex, cancer differentiation, stage, location, pretreatment albumin, and pretreatment CEA level
Liang et al[13]	China	RC	CRC	1332	60	61.6	II	Curative tumor resection and adjuvant chemotherapy	Pretreatment ALC < 1300/μL	373	26.8	PFS and OS	Age, T stage, vessel invasion, perineural invasion, LN sampling, and CEA level
Iseki et al[14]	Japan	RC	CRC	362	67	53.9	II-III	Curative tumor resection and neoadjuvant chemotherapyor neoadjuvant CRT	Pretreatment ALC < 1700/μL for PFS and ALC < 1100/μL for OS	205	63.4	PFS and OS	Age, sex, BMI, tumor location, size, T stage, LVI, LN metastasis, preoperative CEA level, adjuvant chemotherapy, and tumor histology
Oh et al[15]	Korea	RC	Rectal adenocarcinoma	139	56	77	I-III	Preoperative CRT and surgical resection	Pretreatment ALC < 1993/μL	69	49.6	PFS and OS	Age, sex, tumor stage, histology grade, and surgery type
Yamamoto et al[17]	Japan	RC	CRC	379	NR (> 70)	55.4	I-IV	Surgical resection	Pretreatment ALC < 1280/μL	145	30	OS	None
Xu et al[16]	China	RC	Locally advanced rectal cancer	34	59	73.5	II-III	Neoadjuvant CRT, surgical resection, and adjuvant chemotherapy	Pretreatment ALC < 500/μL	10	32	PFS	Age, sex, tumor stage, and pretreatment CEA level
Shibutani et al[18]	Japan	RC	CRC	115	65	46.1	II-III	Surgical resection, and adjuvant chemotherapy	Chemotherapy-associated ALC < 1000/μL	17	32.9	PFS and OS	Age, sex, tumor location, depth, histology type, LN metastasis, pretreatment CEA level, CA-199 level, albumin level, and chemotherapy regimen
Li et al[19]	China	RC	Locally advanced rectal cancer	36	60.5	63.9	II-III	Surgical resection and CRT	CRT-associated ALC < 500/μL	20	34	PFS	Age, sex, LN metastasis, pretreatment CEA level, and pretreatment ALC
Baik et al[20]	Korea	RC	Rectal adenocarcinoma	147	65	63.3	I-III	Preoperative CRT and surgical resection	CRT-associated ALC < 500/μL	21	60.7	PFS and OS	Age, BMI, DM, hypertension, tumor stage, LVI, and perineural invasion
Koukourakis et al[21]	Greece	RC	Rectal adenocarcinoma	33	72	69.7	I-III	CRT	CRT associated ALC < 518/μL	18	32	PFS	None
Soyfer et al[22]	Israel	RC	Rectal cancer	110	61	60	I-III	Radiotherapy and surgical resection	Radiotherapy-associated ALC < 500/μL	9	62.5	PFS and OS	Age, sex, tumor stage, pretreatment CEA level, tumor location, and comorbidities
Kim et al[23]	United States	RC	Rectal adenocarcinoma	193	58	62	II-III	Neoadjuvant CRT, surgical resection	CRT-associated ALC < 500/μL	110	68	PFS and OS	Age, sex, race, ECOG PS, smoking, tumor stage, baseline albumin level, TB level, CEA level, and surgery type

NR: Not reported; RC: Retrospective cohort; CRC: Colorectal cancer; ALC: Absolute lymphocyte count; PFS: Progression-free survival; OS: Overall survival; WHO PS: World Health Organization performance status; ALP: Alkaline phosphatase; CEA: Carcinoembryonic antigen; LN: Lymph node; BMI: Body mass index; CRT: Chemoradiotherapy; LVI: Lymphovascular invasion; CA-199: Carbohydrate antigen 19-9; DM: Diabetes mellitus; ECOG PS: Eastern Cooperative Oncology Group performance status; TB: Total bilirubin.

Table 2 Study quality evaluation via the Newcastle-Ottawa Scale.

Ref.	Representativeness of the exposed cohort	Selection of the non- exposed cohort	Ascertainment of exposure	Outcome not present at baseline	Control for age	Control for other confounding factors	Assessment of outcome	Sufficiently long follow-up duration	Adequacy of follow-up of cohorts	Total
Cézé et al[11]	0	1	1	1	1	1	1	0	1	7
Chu-Yuan et al[12]	0	1	1	1	1	1	1	1	1	8
Liang et al[13]	1	1	1	1	1	1	1	1	1	9
Iseki et al[14]	0	1	1	1	1	1	1	1	1	8
Oh et al[15]	0	1	1	1	1	1	1	1	1	8
Yamamoto et al[17]	1	1	1	1	0	0	1	1	1	7
Xu et al[16]	0	1	1	1	1	1	1	1	1	8
Shibutani et al[18]	0	1	1	1	1	1	1	1	1	8
Li et al[19]	0	1	1	1	1	1	1	1	1	8
Baik et al[20]	0	1	1	1	1	1	1	1	1	8
Koukourakis et al[21]	0	1	1	1	0	0	1	1	1	6
Soyfer et al[22]	0	1	1	1	1	1	1	1	1	8
Kim et al[23]	0	1	1	1	1	1	1	1	1	8

Association between lymphopenia and PFS in CRC

Twelve studies[11-14,16-23] reported the association between lymphopenia and PFS in patients with CRC. Moderate heterogeneity was observed (P for Cochrane Q test = 0.05; I² = 43%). Pooled results with a random-effects model revealed that lymphopenia was associated with poor PFS in patients with CRC (HR = 2.49, 95%CI: 1.87-3.32, P < 0.001; Figure 2A). Sensitivity analyses were performed by removing one dataset at a time, and the results remained stable (HR = 2.24-2.69, all P < 0.05). Specifically, sensitivity analysis limited to high-quality studies (NOS ≥ 7) with multivariate analysis only revealed similar results (HR = 2.35, 95%CI: 1.79-3.08, P < 0.001; I² = 38%). Further subgroup analyses identified similar results for studies from Asian and Western countries (HR = 2.22 vs 3.67, P for subgroup difference = 0.26; Figure 2B) and in patients with mean ages of < 61 or ≥ 61 years (HR = 2.50 vs 2.54, P for subgroup difference = 0.96; Figure 2C). Interestingly, worse PFS had a stronger association with treatment-related lymphopenia than with pretreatment lymphopenia (HR = 3.40 vs 1.90, P for subgroup difference = 0.02; Figure 3A), and the association with worse PFS was stronger in studies defining lymphopenia with an ALC cutoff of < 600/μL than in those using a cutoff of ≥ 600/μL (HR = 4.43 vs 1.98, P for subgroup difference = 0.02; Figure 3B). Similar results were also observed between subgroups of studies with follow-up durations of < 33 or ≥ 33 months (HR = 2.51 vs 2.57, P for subgroup difference = 0.94; Figure 3C). Finally, further meta-regression did not indicate that the association between lymphopenia and PFS was significantly modified by ALC cutoffs for defining lymphopenia in each study (P = 0.17; Table 3).

Figure 2 Forest plots for the meta-analysis of the association between lymphopenia and progression-free survival in patients with colorectal cancer. A: Overall meta-analysis; B: Subgroup analysis according to study country; C: Subgroup analysis according to the mean patient age.

Figure 3 Forest plots for the subgroup analyses of the association between lymphopenia and progression-free survival in patients with colorectal cancer. A: Subgroup analysis according to the timing of lymphopenia evaluation; B: Subgroup analysis according to the absolute lymphocyte count cutoff for defining lymphopenia; C: Subgroup analysis according to the follow-up duration.

Table 3 Results of univariate meta-regression analysis of the influence of absolute lymphocyte count cutoffs on the outcomes.

Outcomes	Influence of the ALC cutoffs
	Coefficient	95%CI	P value	Adjusted R2
HR for the association between lymphopenia and PFS	0.00055	-0.00025 to 0.00136	0.17	37.9%
HR for the association between lymphopenia and OS	0.00024	-0.00052 to 0.00099	0.49	7.5%

ALC: Absolute lymphocyte count; PFS: Progression-free survival; OS: Overall survival; HR: Hazard ratio; CI: Confidence interval.

Association between lymphopenia with OS

Further meta-analysis involving 10 cohort studies[11-15,17,18,20,22,23] suggested that lymphopenia was also associated with worse OS in patients with CRC (HR = 2.10, 95%CI: 1.60-2.76, P < 0.001; Figure 4A) with moderate heterogeneity (P for Cochrane Q test = 0.10; I² = 39%). Sensitivity analyses did not significantly change the results (HR = 1.96-2.36, P all < 0.05). Specifically, sensitivity analysis limited to high-quality studies with multivariate analysis also revealed consistent results (HR = 2.16, 95%CI: 1.57-2.98, P < 0.001; I² = 46%). Similar results were recorded in subgroup analyses of studies from Asian and Western countries (HR = 1.90 vs 2.87, P for subgroup difference = 0.14; Figure 4B), patients with mean ages of < 60 or ≥ 64 years (HR = 2.56 vs 1.79, P for subgroup difference = 0.17; Figure 4C), studies evaluating pretreatment lymphopenia and treatment-related lymphopenia (HR = 2.23 vs 2.17, P for subgroup difference = 0.94; Figure 5A), studies defining lymphopenia as ALC cutoffs of < 1000 or ≥ 1000/μL (HR = 2.05 vs 2.27, P for subgroup difference = 0.80; Figure 5B), and studies with follow-up durations of < 33 or ≥ 33 months (HR = 2.23 vs 2.13, P for subgroup difference = 0.90; Figure 5C). Finally, further meta-regression did not indicate that the association between lymphopenia and OS was significantly modified by ALC cutoffs for defining lymphopenia in each study (P = 0.49; Table 3).

Figure 4 Forest plots for the meta-analysis of the association between lymphopenia and overall survival in patients with colorectal cancer. A: Overall meta-analysis; B: Subgroup analysis according to the study country; C: Subgroup analysis according to the mean patient age.

Figure 5 Forest plots for the subgroup analyses of the association between lymphopenia and overall survival in patients with colorectal cancer. A: Subgroup analysis according to the timing of lymphopenia evaluation; B: Subgroup analysis according to the absolute lymphocyte count cutoff for defining lymphopenia; C: Subgroup analysis according to the follow-up duration.

Publication bias

Funnel plots for meta-analyses of the association between lymphopenia and survival outcomes of patients with CRC are presented in Figure 6. The plots appeared symmetrical, suggesting a low risk of publication bias. Egger’s test also uncovered no evidence of publication bias (PFS: P = 0.29; OS: P = 0.41). However, given the relatively limited number of included studies for each outcome, the statistical power of funnel plot asymmetry tests might have been insufficient to reliably exclude small-study effects.

Figure 6 Funnel plots for estimating the potential publication biases underlying the meta-analyses of the association between lymphopenia and survival outcomes of patients with colorectal cancer. A: Funnel plots for the meta-analysis of the association between lymphopenia and progression-free survival; B: Funnel plots for the meta-analysis of the association between lymphopenia and overall survival.

DISCUSSION

This meta-analysis demonstrated a significant and consistent association between lymphopenia and inferior survival outcomes in patients with CRC, emphasizing the potential value of ALC as a prognostic biomarker. Our findings suggest that reduced lymphocyte counts, whether present at baseline or induced during treatment, are predictive of worse PFS and OS. These results remained stable across sensitivity analyses limited to multivariate-adjusted and high-quality studies, reinforcing their robustness. Notably, treatment-related lymphopenia and lymphopenia defined using lower ALC thresholds were more strongly associated with poor PFS in subgroup analyses. However, it should be emphasized that no universally accepted or clinically validated ALC cutoff for defining lymphopenia in CRC currently exists. The variability in thresholds across studies reflects differences in laboratory standards, treatment settings, and clinical contexts. To account for this heterogeneity, we applied random-effects models, conducted predefined subgroup analyses by cutoff category, and performed meta-regression to evaluate potential threshold effects. Neither subgroup interaction tests nor meta-regression analyses demonstrated a statistically significant modifying effect of ALC cutoffs on the pooled estimates. These findings suggest that the adverse prognostic association of lymphopenia is not likely driven by a specific extreme threshold. However, definitional variability might influence the precise magnitude of the effect, and it should be considered when interpreting clinical applicability.

Several biological mechanisms might plausibly explain the observed association between lymphopenia and poor prognosis in CRC. Experimental and translational studies have demonstrated critical roles of cytotoxic T lymphocytes[29] and natural killer cells[30] in antitumor immunity and treatment response. However, it should be emphasized that most evidence directly linking peripheral lymphopenia to survival outcomes in CRC was generated by clinical observational studies rather than mechanistic experiments. The ALC reflects a composite measure of circulating immune cells, and it does not distinguish between biologically distinct subsets, which can have heterogeneous or even opposing effects on tumor progression. Therefore, the proposed mechanisms remain biologically plausible but not definitively established in this specific clinical context. Lymphopenia might reflect a suppressed or exhausted immune system, which impairs the host’s ability to control residual tumor cells or respond effectively to treatment[31]. Moreover, treatment-related lymphopenia might both represent immune depletion caused by cytotoxic regimens and indicate the patient’s sensitivity to treatment-induced immunosuppression[32,33]. This dual role could partly explain its stronger association with disease progression, as observed in our subgroup analyses. Unlike pretreatment lymphopenia, which captures the baseline immune status[34], treatment-induced lymphopenia reflects the cumulative immunosuppressive effects of both cancer and anticancer treatment. Treatment-induced lymphopenia might indicate a critical depletion of immune cells at a time when the immune system is essential for tumor clearance and surveillance, particularly following chemotherapy or chemoradiotherapy[8]. Furthermore, patients who develop severe lymphopenia during treatment might have reduced capacity to recover immune function, contributing to early disease progression[35]. This suggests that monitoring lymphocyte counts during therapy could provide important prognostic information and identify patients at higher risk of recurrence.

Moreover, the subgroup finding that lower ALC thresholds (e.g., < 600/μL) were more predictive of poor PFS also aligns with the notion that severe lymphocyte depletion represents a critical level of immunosuppression beyond which the host’s ability to contain tumor spread is significantly compromised[36]. In the CRC context, particularly in locally advanced rectal cancer, modern neoadjuvant chemoradiation involves pelvic irradiation and cytotoxic chemotherapy, both of which can induce profound and sustained lymphocyte depletion because of the high radiosensitivity of circulating lymphocytes[37]. This treatment-related immune suppression can impair post-therapy tumor immune surveillance during a critical window when residual micrometastatic disease is present, potentially contributing to earlier progression. Although this hypothesis is biologically plausible, direct mechanistic evidence specific to CRC remains limited, and the association observed in this study should be interpreted as reflective of treatment-associated immune dysregulation rather than definitive causation.

In addition, the consistent associations across geographic regions and age groups suggest that the prognostic signal of lymphopenia is not confined to a specific population. However, it should be noted that most included studies were conducted in high-income countries and academic centers, and differences in healthcare resources, treatment accessibility, and supportive care practices might limit direct applicability of the findings to other healthcare settings. Moreover, in subgroup analyses by geographic region, the association between lymphopenia and poor PFS remained directionally consistent. However, heterogeneity was relatively high among Western cohorts (I² = 76%). Several factors might have contributed to this variability. Western studies might have included a higher proportion of patients with rectal cancer undergoing neoadjuvant chemoradiation with pelvic irradiation, which can induce substantial lymphocyte depletion because of the radiosensitivity of circulating immune cells. Differences in treatment protocols, lymphocyte assessment timing, tumor stage distribution, supportive care practices, and laboratory reference standards for ALC measurement can further contribute to between-study variability. Importantly, although the effect magnitude varied, the direction of association remained consistent across regions, supporting the overall robustness of the findings while underscoring the need for cautious interpretation of subgroup estimates.

Conversely, although OS was not significantly influenced by the timing or cutoff of lymphopenia in subgroup analyses, the trends were directionally similar, suggesting that both the pretreatment immune status and treatment-induced lymphocyte depletion contribute to long-term outcomes. The lack of statistical interaction in some subgroup comparisons (e.g., OS by lymphopenia timing) might reflect insufficient power rather than the absence of biological relevance. Nonetheless, the stronger effect sizes observed for treatment-related lymphopenia and lower ALC cutoffs in relation to PFS warrant further exploration, particularly in prospective studies with serial immune profiling.

This study had several strengths. It provided an up-to-date synthesis of the available evidence, incorporating 13 cohort studies with more than 3300 patients. The search strategy was comprehensive, and the rigorous inclusion criteria ensured relevance to CRC survival outcomes. We conducted multiple sensitivity analyses, including restriction to multivariate-adjusted and high-quality studies, which supported the stability of the pooled estimates. In addition, predefined subgroup analyses enabled us to explore the potential influence of study- and patient-level characteristics, offering insight into the contexts in which lymphopenia might have greater prognostic implications. However, several limitations must be acknowledged. First, all included studies had a retrospective design, which might have introduced selection bias, immortal time bias, and residual confounding inherent to observational time-to-event analyses[38]. Although most studies reported multivariate-adjusted HRs and adequate follow-up durations, unmeasured confounders cannot be excluded. Second, substantial heterogeneity existed in the definition and measurement of lymphopenia across the studies, which could have affected comparability and interpretation. Although subgroup analyses and meta-regression did not indicate a statistically significant modifying effect of cutoff thresholds, residual clinical heterogeneity might have influenced the precision and applicability of the pooled estimates. Third, we could not perform subgroup analyses based on individual-level data, such as treatment modality, tumor stage, microsatellite instability status, or baseline inflammatory markers[39,40]. Although these factors can modify the prognostic value of lymphopenia, they could not be adequately explored because of aggregate data reporting. Fourth, the search was limited to English-language peer-reviewed publications, and grey literature was not systematically explored. Although this approach prioritized methodological quality and extractable data consistency, it might have introduced language and publication bias. In addition, although funnel plot inspection and Egger’s test did not indicate significant publication bias, these methods have limited sensitivity when the number of studies is modest. Finally, the observational nature of the included studies precluded causal inference, and it remains unclear whether lymphopenia directly contributes to poor outcomes or serves as a surrogate marker for other underlying processes such as the tumor burden, systemic inflammation, or frailty. Although we did not formally apply the GRADE framework, the overall certainty of evidence should be interpreted as moderate at best given the observational design of the included studies and potential residual biases.

From a clinical perspective, our findings suggest that ALC can serve as a simple and widely available marker associated with prognosis in CRC. However, these observations should be considered hypothesis-generating rather than practice-changing. Prospective studies are needed to validate whether serial ALC monitoring can identify patients at higher risk of recurrence and whether supportive strategies, such as treatment schedule modification, radiotherapy field optimization, or immune-supportive interventions, can mitigate adverse outcomes. In addition, given the growing role of immunotherapy in mismatch-repair deficient CRC, future research should explore whether treatment-related lymphopenia modifies response to immune checkpoint inhibitors. At present, these considerations are hypothesis-generating, and validation in prospective and interventional settings is required. However, before lymphopenia can be incorporated into routine prognostic models or treatment decision-making, prospective validation in well-designed studies is needed. Future research should prioritize prospective study designs incorporating standardized definitions of lymphopenia and harmonized ALC cutoff thresholds to improve comparability across studies. Serial lymphocyte measurements during the treatment course could help clarify timing-dependent effects and distinguish baseline immune status from treatment-induced immune depletion. Furthermore, meta-analyses based on individual patient data could enable more refined modeling of threshold effects, dose–response relationships, and interactions with tumor stage or treatment modality. In particular, CRC-specific prospective studies should evaluate whether lymphocyte depletion during chemoradiation independently predicts recurrence after total neoadjuvant therapy and whether integration of immune biomarkers improves patient stratification.

CONCLUSION

In conclusion, this meta-analysis found that lymphopenia, particularly when treatment-related or defined by lower ALC thresholds, is associated with poorer survival in patients with CRC. These findings highlight the prognostic relevance of lymphocyte monitoring and underscore the importance of the host immune status in shaping cancer progression and treatment efficacy. Further research is warranted to confirm these observations in prospective settings and investigate whether mitigating lymphopenia can positively influence clinical outcomes.