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. 2026 Sep 25;105(39):e50902. doi: 10.1097/MD.0000000000050902

Peripheral blood markers predict prognosis in patients with advanced non-small cell lung cancer receiving immunotherapy

Peng Zhang a, Peng Cao a,✉, Shanyue Tan a, Ling Xu a,*
PMCID: PMC13619245  PMID: 42798121

Abstract

In recent years, immunotherapy has significantly reshaped the pattern of non-small cell lung cancer (NSCLC) treatment. However, the issue of how to pinpoint potential responsive populations more accurately warrants immediate and focused attention. This study mainly explored the correlation between baseline peripheral blood biomarkers and prognosis in patients with advanced NSCLC undergoing immunotherapy. A retrospective analysis was performed on 139 patients diagnosed with advanced NSCLC who underwent first-line immunotherapy treatment between 2019 and 2022. Baseline demographic information and peripheral blood parameter data were collected. The Cox proportional hazards regression model was used to estimate hazard ratios and 95% confidence intervals to identify independent prognostic factors, while Kaplan–Meier survival analysis with log-rank tests was applied to compare survival differences between groups. Finally, the predictive efficacy of biomarkers was assessed utilizing the receiver operating characteristic curve. C-reactive protein (CRP) and carcinoembryonic antigen (CEA) were independent predictors for both progression-free survival (PFS) and overall survival (OS). Eosinophils > 0.23 × 109/L, PLT (platelets) > 228 × 109/L, albumin ≤ 37.2g/L, CRP > 6.21mg/L, or CEA > 6.23ng/ml was associated with poorer PFS. Meanwhile, (hemoglobin) ≤ 132g/L, platelets > 228 × 109/L, CRP > 7.59mg/L, or CEA > 7.39ng/ml may imply an inferior OS outcome. And the combination of CRP and CEA yielded areas under the curve with 0.675 and 0.656 for predicting PFS and OS, respectively. CRP and CEA hold the potential to predict the prognosis of advanced NSCLC patients undergoing first-line immunotherapy.

Keywords: biomarker, immunotherapy, non-small cell lung cancer, peripheral blood parameters, Prognosis

1. Introduction

Lung cancer, as its name implies, is a neoplasm formed by abnormal proliferation of lung cells.[1] Over the past 3 decades, the incidence and prevalence of lung cancer have both increased dramatically worldw ide.[2] Given its high mortality, lung cancer occupies the major causes of cancer-related deaths, which imposes a tremendous burden on the global public health system.[3] According to biological characteristics, lung cancer is classified widely as small cell lung cancer and non-small cell lung cancer (NSCLC). And the latter accounts for approximately 85% of all squamous cell carcinoma.[4] In recent years, there have been promising advances and breakthroughs in the treatment of NSCLC. Among which, the most representative are emerging therapies such as immunotherapy and targeted therapy.[5,6]

Immune checkpoints are receptor molecules expressed on immune cells, which can negatively regulate antigen-induced immune activation through binding to their ligands.[7] The two most widely known immune checkpoints are cytotoxic T lymphocyte-associated antigen 4 and programmed cell death protein 1 (PD-1).[8] The pathway composed of PD-1 and its ligand programmed death-ligand 1 (PD-L1) has been most extensively studied in NSCLC. By blocking the interaction of PD-1 and PD-L1, immune checkpoint inhibitors (ICIs) can reactivate the anti-tumor immune activity of T cells.[9] Over the past decade or so, immunotherapy represented by ICIs has revolutionized the paradigm of NSCLC medication.[10,11] ICIs can not only improve survival in severe patients with pre treated NSCLC but also benefit advanced disease, adjuvant, and neoadjuvant regimens as first-line treatment.[12] Of note, although ICIs bring hope for the treatment of NSCLC, merely <20% of patients exhibit a persistent immune response after immunotherapy.[13] Hence, there is an urgent demand to explore more novel biomarkers for screening potential responsive populations for immunotherapy.

Currently, biomarkers approved by the Food and Drug Administration for ICI therapy in solid malignancies mainly include PD-L1, microsatellite instability in combination with mismatch repair, and tumor mutational burden (TMB), which are already broadly applied in clinical practice.[14] Nevertheless, the detection of all these biomarkers is generally based on invasive tissue biopsy. Additionally, the detection is not economical or reproducible in certain scenarios and fails to reflect the overall heterogeneity of the tumor. This introduces certain challenges to the evaluation and scheme adjustment during the course of tumor immunotherapy. In recent years, growing research has focused on the predictive ability of peripheral blood indicators for the prognosis of malignant tumors treated with ICI therapy.[15–17] However, the concrete correlation between peripheral blood indicators and NSCLC prognosis remains unclear. Thus, the objective of this research was to investigate the relationship between pretreatment peripheral blood parameters and prognosis in advanced NSCLC patients receiving first-line ICI treatment based on a prospective cohort study.

2. Materials and methods

2.1. Study population

This retrospective study was approved by the Ethics Committee of Anhui Chest Hospital. All individuals were aware of and consented to participate in the research. The study population consisted of 139 patients with a definitive cytological or histological diagnosis of NSCLC, who were treated with ICIs in the abovementioned hospital from March 2019 to April 2022. Additional inclusion criteria were the following: stage III-IV; and first-line use of ICIs. Patients with failed follow-up or incomplete baseline data were excluded. The last follow-up was completed in April 2024.

2.2. Data collection

Demographic characteristics and clinical data were obtained from the electronic medical record system of our hospital. Follow-up was conducted through telephone inquiries and collection of hospitalization information. Progression -free survival (PFS was defined as the time from initiation of ICI treatment to disease progression, death, or last follow-up. Overall survival (OS) was calculated as the time span from the commencement of immunotherapy to either the final follow-up appointment or the date of death.

Baseline peripheral blood was drawn within 1 week prior to initial immunotherapy. Blood routine and serum biochemical indicators were further collected. Peripheral blood assay data were then utilized to calculate the neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio (PLR), albumin-to-globulin ratio (AGR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI). The SII can be calculated by the following formula: absolute neutrophil count × absolute platelet count / absolute lymphocyte count. The PNI was calculated as serum albumin + 5 × absolute lymphocyte count.

2.3. Statistical analysis

SPSS 26.0 software (SPSS Inc., Chicago) and GraphPad Prism 9.1.0 (GraphPad Software Inc,) were applied for statistical processing of the data in this research. In addition, X-Tile was used to select the cutoff value.[18] Blood test indexes were divided into high and low groups according to the cutoff value. Continuous variables and categorical variables were compared by Student t test and Pearson’s Chi-square test respectively. Predictors of PFS and OS were screened by COX regression analysis and Kaplan–Meier survival analysis. Differences were considered statistically significant when P < .05.

3. Results

3.1. Demographic and clinical parameters

In the present study, a total of 139 advanced NSCLC patients were recruited. As displayed in Table 1, demographic characteristics and clinical information were analyzed. The entire cohort consisted of 127 (91.40%) men and 12 (8.60%) women with a mean age of 66.19 years. Ninety-four (67.60%) cases were confirmed with a history of smoking. The main pathological types of 61 (43.90%) stage III patients and 78 (56.10%) stage IV patients were squamous cell carcinoma (n = 92, 66.19%) and adenocarcinoma (n = 41, 29.50%).

Table 1.

Baseline characteristics of patients.

Characteristics Entire cohort (n = 139) PD + SD (n = 80) PR + CR (n = 59) P value
Age (yr), mean ± SD 66.19 ± 9.91 65.86 ± 10.88 66.64 ± 8.50 .648
Sex, n (%) .954
Male 127 (91.40) 73 (91.25) 54 (91.53)
Female 12 (8.60) 7 (8.75) 5 (8.47)
Smoking, n (%) .971
Ever 94 (67.60) 54 (67.50) 40 (67.80)
Never 45 (32.40) 26 (32.50) 19 (32.20)
Stage, n (%) .282
III 61 (43.90) 32 (40.00) 29 (49.15)
IV 78 (56.10) 48 (60.00) 30 (50.85)
Histology, n (%) .094
Squamous cell carcinoma 92 (66.19) 50 (35.97) 42 (30.22)
Adenocarcinoma 41 (29.50) 24 (17.27) 17 (12.23)
Others 6 (4.32) 6 (4.32) 0 (0.00)
Peripheral blood parameters
WBC (109/L), mean±SD 7.30 ± 2.44 7.56 ± 2.71 6.96 ± 1.98 .152
NEUT (109/L), mean±SD 5.15 ± 2.18 5.43 ± 2.50 4.76 ± 1.59 .055
LYM (109/L), mean±SD 1.39 ± 0.49 1.38 ± 0.50 1.39 ± 0.49 .894
EOS (109/L), mean±SD 0.18 ± 0.24 0.16 ± 0.19 0.21 ± 0.30 .322
MONO%, mean±SD 7.58 ± 2.59 7.45 ± 2.76 7.75 ± 2.36 .501
BASO%, mean±SD 0.35 ± 0.30 0.34 ± 0.31 0.36 ± 0.28 .565
HGB (g/L), mean ± SD 123.64 ± 17.43 123.56 ± 17.85 123.75 ± 17.00 .951
PLT (109/L), mean±SD 238.81 ± 94.12 229.50 ± 89.19 251.42 ± 99.81 .176
TBIL (umol/L), mean±SD 11.69 ± 4.65 12.13 ± 4.86 11.09 ± 4.31 .192
ALP (u/L), mean ±SD 101.96 ± 38.25 103.55 ± 44.09 99.80 ± 28.74 .569
PreALB (mg/L), mean±SD 185.61 ± 62.97 181.82 ± 59.90 190.74 ± 67.08 .411
ALB (g/L), mean ± SD 35.70 ± 4.37 36.30 ± 4.57 34.87 ± 3.98 .056
GLB (g/L), mean±SD 30.28 ± 5.50 29.58 ± 5.70 31.24 ± 5.13 .079
LDL (mmol/L), mean±SD 2.34 ± 0.84 2.26 ± 0.74 2.45 ± 0.96 .178
HDL (mmol/L), mean±SD 1.27 ± 0.35 1.26 ± 0.37 1.27 ± 0.32 .990
CRP (mg/L), mean ± SD 21.65 ± 27.26 20.91 ± 27.01 22.66 ± 27.80 .710
LDH (u/L), mean±SD 202.21 ± 76.51 200.16 ± 69.73 204.98 ± 85.39 .715
CEA (ng/mL), mean±SD 44.80 ± 369.33 17.38 ± 48.91 81.98 ± 564.66 .384
SCCA (ng/mL), mean±SD 8.16 ± 32.32 10.13 ± 41.05 5.50 ± 13.34 .406
CYFRA21–1 (ng/mL), mean±SD 15.14 ± 24.81 11.60 ± 20.03 19.94 ± 29.63 .065
NLR, mean ± SD 4.24 ± 2.81 4.55 ± 3.28 3.81 ± 1.96 .102
LMR, mean ± SD 3.05 ± 2.16 3.15 ± 2.63 2.92 ± 1.29 .538
PLR, mean±SD 192.46 ± 109.92 187.65 ± 114.64 198.98 ± 103.79 .550
AGR, mean±SD 1.23 ± 0.31 1.28 ± 0.35 1.15 ± 0.25 .013
SII, mean±SD 1031.71 ± 873.52 1059.64 ± 989.04 993.85 ± 693.27 .662
PNI, mean±SD 42.64 ± 5.25 43.22 ± 5.57 41.85 ± 4.72 .128
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AGR = albumin-to-globulin ratio, ALB = albumin, ALP = alkaline phosphatase, BASO = basophil granulocytes, CEA = carcinoembryonic antigen, CR = complete response, CRP = C-reactive protein, CYFRA21-1 = cytokeratin fragment 19, EOS = eosinophils, GLB = globulin, HDL = high-density lipoprotein, HGB = hemoglobin, LDH = lactic dehydrogenase, LDL = low-density lipoprotein, LMR = lymphocyte-to-monocyte ratio, LYM = lymphocyte, MONO = monocyte, NEUT = neutrophil, NLR = neutrophil-to-lymphocyte ratio, PD = progressive disease, PLR = platelet-to-lymphocyte ratio, PLT = platelet, PNI = prognostic nutritional index, PR = partial response, PreALB = prealbumin, SCCA = squamous cell carcinoma antigen, SD = stable disease, SD = standard deviation, SII = systemic immune-inflammation index, TBIL = total bilirubin, WBC = white blood cell count.

The mean values of peripheral blood parameters were as follows: white blood cell count, 7.30 × 109/L; neutrophils,5.15 × 109/L; lymphocytes, 1.39 × 109/L; eosinophils (EOS), 0.18 × 109/L; basophil percentage 0.35%; monocyte percentage, 7.58%; hemoglobin (HGB), 123.64 g/L; platelets (PLT), 238.81 × 109/L; total bilirubin,11.69 μmol/L; alkaline phosphatase, 101.96 U/L; prealbumin, 185.61 mg/L; albumin (ALB), 35.70 g/L; globulin, 30.28 g/L; low-density lipoprotein, 2.34 mmol/L; high-density lipoprotein, 1.27 mmol/L; C-reactive protein (CRP), 21.65 mg/L; lactate dehydrogenase, 202.21 U/L; carcinoembryonic antigen (CEA), 44.80 ng/mL; squamous cell carcinoma antigen 8.16 ng/mL; cytokeratin fragment 19, 15.14 ng/mL; neutrophil-to-lymphocyte ratio, 4.24; lymphocyte-to-monocyte ratio, 3.05; platelet-to-lymphocyte ratio, 192.46; AGR, 1.23; SII, 1031.71; and PNI, 42.64. Notably, subgroup analysis revealed elevated AGR levels in patients with progressive disease or stable disease compared to responders (1.28 vs 1.15, P = .013).

3.2. Prognostic factors identified by multivariate analysis

In the multivariate Cox survival analyses (Table 2), stage IV (hazard ratio [HR], 0.504; 95% confidence intervals [CI], 0.338–0.751; P < .001), EOS (HR, 0.615; 95% CI, 0.401–0.943; P = .026), ALB (HR, 1.723; 95% CI,1.130–2.627; P = .011), high-density lipoprotein (HR, 0.515; 95% CI, 0.318–0.835; P = .007), CRP (HR, 0.409; 95% CI,0.266–0.628; P < .001), and CEA (HR, 0.586; 95% CI,0.391–0.877; P = .009) independently predicted PFS. Analogously, HGB (HR, 2.094; 95% CI, 1.167–3.756; P = 0.013), PLT (HR, 0.562; 95% CI, 0.352–0.898; P = .016), CRP (HR, 0.521; 95% CI, 0.316–0.860; P = .011), and CEA (HR, 0.487; 95% CI, 0.296–0.799; P = .004) were independently associated with OS. As depicted in Figure 1, stage III patients demonstrated significantly longer PFS and OS compared with stage IV patients. Nevertheless, factors including age, sex, smoking, and histology did not effectively stratify the prognosis of patients who received immunotherapy.

Table 2.

COX regression analysis of progression-free survival and overall survival.

Variables Progression-free survival Overall survival
Univariable Multivariable Univariable Multivariable
HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value
Age 1.613 (0.929–2.799) .089 1.725 (0.928–3.207) .085
Sex (Male) 1.185 (0.619–2.269) .609 1.869 (0.929–3.762) .080
Smoking (ever) 0.992 (0.668–1.474) .970 0.924 (0.570–1.498) .749
Stage (Ⅳ) 0.511 (0.349–0.749) <.001 0.504(0.338–0.751) <.001 0.517 (0.321–0.831) .006
Histology (SCC) 1.050 (0.714–1.544) .804 0.881 (0.548–1.417) .602
WBC 0.653 (0.451–0.946) .024 0.489 (0.308–0.778) .003
NEUT 0.631 (0.435–0.915) .015 0.540 (0.341–0.855) .009
LYM 0.673 (0.369–1.228) .197 0.518 (0.265–1.013) .054
EOS 0.630 (0.416–0.955) .030 0.615 (0.401–0.943) .026 0.698 (0.384–1.271) .240
MONO% 0.598 (0.360–0.992) .046 1.707 (1.080–2.699) .022
BASO% 0.715 (0.426–1.198) .202 1.461 (0.962–2.291) .099
HGB 1.526 (0.969–2.403) .068 1.824 (1.037–3.208) .037 2.094 (1.167–3.756) .013
PLT 0.555 (0.382–0.806) .002 0.490 (0.310–0.776) .002 0.562 (0.352–0.898) .016
TBIL 1.409 (0.818–2.429) .217 2.020 (1.127–3.621) .018
ALP 0.614 (0.398–0.947) .027 1.711 (0.785–3.726) .177
PreALB 1.906 (1.263–2.876) .002 1.827 (1.137–2.934) .013
ALB 1.618 (1.087–2.409) .018 1.723 (1.130–2.627) .011 1.702 (0.895–3.236) .105
GLB 0.601 (0.409–0.881) .009 0.533 (0.340–0.838) .066
LDL 1.396 (0.767–2.540) .276 1.658 (0.851–3.231) .138
HDL 0.729 (0.461–1.152) .175 0.515 (0.318–0.835) .007 1.405 (0.861–2.292) .174
CRP 0.424 (0.279–0.645) <.001 0.409 (0.266–0.628) <.001 0.463 (0.281–0.760) .002 0.521 (0.316–0.860) .011
LDH 0.559 (0.379–0.826) .003 0.571 (0.353–0.922) .022
CEA 0.488 (0.330–0.722) <.001 0.586 (0.391–0.877) .009 0.507 (0.315–0.815) .005 0.487 (0.296–0.799) .004
SCCA 0.523 (0.292–0.937) .029 0.537 (0.330–0.874) .012
CYFRA21-1 0.639 (0.427–0.957) .030 0.689 (0.419–1.135) .143
NLR 0.539 (0.289–1.004) .052 0.666 (0.403–1.102) .114
LMR 1.458 (1.000–2.125) .050 0.715 (0.406–1.259) .246
PLR 0.622 (0.341–1.134) .121 0.643 (0.409–1.013) .057
AGR 1.587 (1.068–2.359) .022 1.830 (0.911–3.677) .089
SII 0.591 (0.360–0.970) .038 0.529 (0.285–0.980) .043
PNI 1.388 (0.939–2.051) .100 1.466 (0.894–2.403) .129
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AGR = albumin-to-globulin ratio, ALB = albumin, ALP = alkaline phosphatase, BASO = basophil granulocytes, CEA = carcinoembryonic antigen, CRP = C-reactive protein, CYFRA21-1 = cytokeratin fragment 19, EOS = eosinophils, GLB = globulin, HDL = high-density lipoprotein, HGB = hemoglobin, LDH = lactic dehydrogenase, LDL = low-density lipoprotein, LMR = lymphocyte-to-monocyte ratio, LYM = lymphocyte, MONO = monocyte, NEUT = neutrophil, NLR = neutrophil-to-lymphocyte ratio, PLR = platelet-to-lymphocyte ratio, PLT = platelet, PNI = prognostic nutritional index, PreALB = prealbumin, SCCA = squamous cell carcinoma antigen, SII = systemic immune-inflammation index, TBIL = total bilirubin, WBC = white blood cell count.

Figure 1.

Figure 1.

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Survival analyses for patients with different tumor stages (A–B).

3.3. Prognostic stratification using optimal cutoff values

As we can see in Figure 2, the indicators were divided into high and low groups by the cutoff values obtained using X-tile. Apparently, significantly worse PFS was observed in patients with EOS ≤ 0.23 × 109/L (vs > 0.23 × 10109/L), PLT > 228 × 109/L (vs ≤ 228 × 109/L), ALB ≤ 37.2g/L (vs > 37.2g/L), CRP > 6.21mg/L (vs ≤ 6.21mg/L), or CEA > 6.23ng/mL (vs ≤6.23ng/mL). Similarly, HGB ≤132g/L, PLT > 228 × 109/L, CRP > 7.59mg/L, or CEA > 7.39ng/mL was markedly associated with poorer OS than HGB > 132g/L, PLT ≤ 228 × 109/L, CRP ≤ 7.59mg/L, or CEA ≤ 7.39ng/mL. Conversely, other analyzed blood parameters did not demonstrate the similar potential for stratifying the prognosis.

Figure 2.

Figure 2.

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Survival analyses for patients with different EOS (A–B), HGB (C–D), PLT (E–F), ALB (G–H), HDL (I–J), CRP (K–L), CEA (M–N). ALB = albumin, CEA = carcino-embryonic antigen, CRP = c-reactive protein, EOS = eosinophils, HDL = high-density lipoprotein, HGB = hemoglobin, PLT = platelet.

3.4. Predictive efficacy of key biomarkers

Given that CRP and CEA emerged as independent predictors for both PFS and OS, their combined predictive performance was evaluated using receiver operating characteristic analysis (Fig. 3) The combined biomarkers yielded AUCs of 0.682 (95%CI, 0.563–0.801) for PFS and 0.664 (95%CI, 0.572–0.756) for OS.

Figure 3.

Figure 3.

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The ROC curves of CRP and CEA for predicting progression-free survival (A) and overall survival (B). AUC = area under the curve of the receiver operating characteristic, CEA = carcinoembryonic antigen, CRP = C-reactive protein, ROC = receiver operating characteristic.

4. Discussion

In recent years, the therapeutic pattern for lung cancer has experienced a profound transformation due to the advent of ICIs. However, the issue of low response rate and adverse effects of immunotherapy warrants careful consideration.[19] Hence, identifying readily available and cost-effective biomarkers for the screening of patients who are likely to respond favorably to immunotherapy is deemed highly essential. In this retrospective study, our findings indicated that higher CRP and CEA levels in peripheral blood may be correlated with poorer prognosis in NSCLC patients treated with ICIs.

Previously, a multitude of studies have uncovered the substantial link between biomarkers identified from biopsy samples and the efficacy of immunotherapy. For instance, Luan et al observed superior survival benefit in the subgroup of NSCLC with higher PD-L1 expression.[20] According to Checkmate 017, patients with PD-L1-negative squamous-cell lung cancer also achieved a favorable survival benefit after immunotherapy, which indicated that PD-L1 is not the only decisive predictive factor of immunotherapy efficacy.[21] In a separate randomized controlled trial, the efficacy of immunotherapy in NSCLC patients characterized by a high TMB markedly outperformed chemotherapy, irrespective of the expression level of PD-L1. Conversely, among patients with low TMB, the survival outcomes did not exhibit a significant disparity between immunotherapy and chemotherapy.[22] Notwithstanding, owing to the inherent heterogeneity of human malignancies, biomarkers discerned solely through histopathological analysis of biopsy specimens derived from a single tumor site often exhibit inadequate robustness and a lack of comprehensiveness. On the other hand, biopsy is prone to causing related complications and may potentially amplify the risk of tumor metastasis. Although subsequent research has discovered that these biomarkers in the blood circulation also possess predictive capabilities for the prognosis of immunotherapy, the cost and time involved in the detection are worth considering.[15,23,24] On the contrary, the value of conventional peripheral blood indicators with advantages of economy, rapidness, and convenience as prognostic biomarkers of immunotherapy deserves in-depth investigation.

The findings of the current study were corroborated by prior research. First of all, Trontzas et al identified a significant role of CRP in prognosticating the outcomes of immunotherapy, where a higher CRP level was linked to a less favorable prognosis.[25] Furthermore, a rise in serum CEA level has been observed to be a potential indicator of tumor progression in NSCLC patients undergoing PD-L1 inhibitor therapy.[26] More importantly, we first pointed out that the integrated assessment of CRP and CEA offered enhanced predictive accuracy for both the PFS and OS outcomes of NSCLC patients receiving ICIs treatment. The conclusion may imply a further understanding of predicting responses to immunotherapy.

Elucidating the relationship between the aforementioned 2 biomarkers and tumors may be more conducive to our exploration of their predictive value and is expected to become the direction of future research. To start with, inflammation plays a critical role in the tumor microenvironment and is intricately linked to both the occurrence and progression of cancer.[27] It is reported that chronic inflammation may precipitate the transformation of tumors, with this factor accounting for approximately 15% of cancer incidences worldwide.[27,28] On the flip side, the presence of tumors can also trigger an excessive release of inflammatory cytokines and chemokines, resulting in the generation of tumor-related inflammation.[28–30] As a quintessential marker of chronic inflammation, CRP is produced primarily by hepatocytes in response to inflammatory stimuli.[31] It exhibits dual functions: one subtype demonstrates anti-inflammatory effects, whereas the other promotes chemotaxis.[27] Furthermore, evidence suggests that CRP promotes the formation of immunosuppressive immune cells.[31] Hence, a potential connection between CRP and tumors could be inferred. With respect to CEA, as a crucial cell adhesion molecule, it exerts a significant influence by impeding cellular differentiation and apoptosis, dismantling cell architecture, and facilitating metastasis.[32] Moreover, CEA also functions as a negative regulator of anti-tumor immune responses primarily by dampening the killing capabilities of natural killer (NK) cells.[33] The immunopredictive mechanism of CEA might be fundamentally linked to its role in fostering an immunosuppressive microenvironment.

Admittedly, there were several limitations in the current research. To begin with, constrained by its single-center and retrospective design, this study was subject to potential confounding factors and selection bias. Thus, it is imperative to conduct multicenter and prospective studies to substantiate our findings in the future. Secondly, PD-L1 and TMB were not included in this study due to data deficiency, resulting in the impact of these widely utilized biomarkers being unable to be assessed. Subsequent studies should incorporate these markers into analysis. Thirdly, the limited sample size somewhat undermines the validity of the findings. Last but not least, the approach to determining the cutoff point may impact the results.

5. Conclusion

Our research suggested that baseline peripheral blood CRP and CEA can predict the prognosis of advanced NSCLC treated with immunotherapy. The combination of the 2 indices exerts a certain predictive efficacy for PFS and OS. In a nutshell, CRP and CEA possess the potential to be prognostic biomarkers for immunotherapy in NSCLC patients. These initial findings warrant prospective validation in larger cohorts.

Acknowledgments

We extend our heartfelt gratitude to all participants whose invaluable contributions made this study feasible.

Author contributions

Conceptualization: Peng Zhang, Ling Xu, Peng Cao.

Data curation: Peng Zhang, Ling Xu, Shanyue Tan.

Formal analysis: Peng Zhang, Ling Xu, Shanyue Tan.

Software: Peng Zhang, Shanyue Tan.

Methodology: Shanyue Tan, Peng Cao.

Supervision: Peng Cao.

Writing – review & editing: Peng Cao.

Writing – original draft: Peng Zhang, Ling Xu.

Abbreviations:

AGR
albumin-to-globulin ratio
ALB
albumin
ALP
alkaline phosphatase
CEA
carcinoembryonic antigen
CI
confidence intervals
CRP
C-reactive protein
EOS
eosinophils
HGB
hemoglobin
HR
hazard ratio
ICIs
immune checkpoint inhibitors
LDH
lactate dehydrogenase
NSCLC
non-small cell lung cancer
OS
overall survival
PD-1
programmed cell death protein 1
PD-L1
programmed death-ligand 1
PFS
progression-free survival
PLT
platelets
PNI
prognostic nutritional index
SII
systemic immune-inflammation index
TMB
tumor mutational burden

PZ and PC contributed to this article equally.

The authors have no funding and conflicts of interest to declares.

The researches encompassing human participants were reviewed and approved by the institutional review boards of the Hospital. The ethics committee has granted a waiver for the necessity of written informed consent for participation.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Zhang P, Cao P, Tan S, Xu L. Peripheral blood markers predict prognosis in patients with advanced non-small cell lung cancer receiving immunotherapy. Medicine 2026;105:39(e50902).

Contributor Information

Peng Zhang, Email: caopeng199577@163.com.

Shanyue Tan, Email: tan1122336@163.com.

Ling Xu, Email: xuling810628@126.com.

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