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Common nutritional/
inflammatory indicators
are not effective tools in
predicting the overall survival
of patients with small cell
lung cancer undergoing
first-line chemotherapy
Huohuan Tian
1
, Guo Li
1,2
, Wang Hou
1
, Jing Jin
1
,
Chengdi Wang
1
, Pengwei Ren
1
, Haoyu Wang
1
, Jie Wang
2
*,
Weimin Li
1
*and Dan Liu
1
*
1
Department of Respiratory & Critical Care Medicine, West China Hospital, Sichuan University,
Chengdu, Sichuan, China,
2
Chinese Academy of Medical Sciences (CAMS) Key Laboratory of
Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of
Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer
Hospital, Chinese Academy of Medical Sciences Peking Union Medical College, Beijing, China
Objective: Various studies have investigated the predictive significance of
numerous peripheral blood biomarkers in patients with small cell lung cancer
(SCLC). However, their predictive values have not been validated. This study
assessed and evaluated the ability of common nutritional or inflammatory
indicators to predict overall survival (OS) in patients with SCLC who received
first-line chemotherapy.
Methods: Between January 2008 and July 2019, 560 patients with SCLC were
enrolled at the Sichuan University West China Hospital. Eleven nutritional or
inflammatory indices obtained before chemotherapy were evaluated. The cutoff
values of continuous peripheral blood indices were confirmed through
maximally selected rank statistics. The relationship of peripheral blood indices
with OS was investigated through univariate and multivariate Cox regression
analyses. Harrell’s concordance (C-index) and time-dependent receiver
operating characteristic curve were used to evaluate the performance of these
indices.
Results: A total of 560 patients with SCLC were enrolled in the study. All the
patients received first-line chemotherapy. In the univariate Cox analysis, all
indices, except the Naples score, were related to OS. In the multivariate
analysis, albumin–globulin ratio was an independent factor linked with
prognosis. All indices exhibited poor performance in OS prediction, with the
area under the curve ranging from 0.500 to 0.700. The lactic dehydrogenase
(LDH) and prognostic nutritional index (PNI) were comparatively superior
predictors with C-index of 0.568 and 0.550, respectively. The LDH showed
incremental predictive values, whereas the PNI showed diminishing values as
Frontiers in Oncology frontiersin.org01
OPEN ACCESS
EDITED BY
Mohamed Rahouma,
Weill Cornell Medical Center,
NewYork-Presbyterian, United States
REVIEWED BY
Aimin Jiang,
The First Affiliated Hospital of Xi’an
Jiaotong University, China
Runbo Zhong,
Shanghai Jiao Tong University, China
*CORRESPONDENCE
Dan Liu
Liudan10965@wchscu.cn
Weimin Li
weimin003@scu.edu.cn
Jie Wang
zlhuxi@163.com
RECEIVED 25 April 2023
ACCEPTED 29 June 2023
PUBLISHED 27 July 2023
CITATION
Tian H, Li G, Hou W, Jin J, Wang C, Ren P,
Wang H, Wang J, Li W and Liu D (2023)
Common nutritional/inflammatory
indicators are not effective tools in
predicting the overall survival of patients
with small cell lung cancer undergoing
first-line chemotherapy.
Front. Oncol. 13:1211752.
doi: 10.3389/fonc.2023.1211752
COPYRIGHT
©2023Tian,Li,Hou,Jin,Wang,Ren,Wang,
Wang, Li and Liu. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that
the original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
TYPE Original Research
PUBLISHED 27 July 2023
DOI 10.3389/fonc.2023.1211752
survival time prolonged, especially for men or smokers. The LDH with highest
sensitivity (0.646) and advanced lung cancer inflammation index (ALI) with
highest specificity (0.952) were conducive to identifying death and survival at
different time points.
Conclusion: Common inflammatory or nutritional biomarkers are only
marginally useful in predicting outcomes in patients with SCLC receiving first-
line chemotherapy. Among them, LDH, PNI, and ALI are relatively promising
biomarkers for prognosis evaluation.
KEYWORDS
small cell lung cancer, nutrition, inflammation, biomarkers, overall survival
Introduction
Small cell lung cancer (SCLC) accounts for approximately 15%
of all lung cancer cases, which is characterized by a high growth
fraction and widespread metastasis (1). Patients with SCLC have a
poor prognosis, with a 5-year survival rate of less than 7% (2).
The Veterans Administration Lung Study Group (VALSG)
system and the eighth edition of the American Joint Committee
on Cancer TNM classification are widely accepted as the staging
system. The National Comprehensive Cancer Network (NCCN)
guidelines recommend surgery, followed by adjuvant treatment, for
patients with limited-stage cancer at T1-2N0M0 (3). Currently, the
standard of care for patients in the extensive stage is chemotherapy
or chemotherapy combined with immunotherapy (3). Although
cancer stage and treatment strategy are decisive factors for cancer
prognosis, weight loss, levels of lactate dehydrogenase, creatinine,
and serum sodium were also reported to be associated with the
prognosis of chemoradiotherapy-treated locally advanced SCLC (4).
Recent studies have shown that some indices calculated on the
basis of peripheral blood cells and biochemical markers can be used
to tailor the treatment response or prognosis of with lung cancer (5,
6). These indices are mainly associated with inflammatory response,
infection, malnutrition, sarcopenia, or cachexia, which are common
complications observed during lung cancer management. Examples
of these indices are neutrophil–lymphocyte ratio (NLR), platelet–
lymphocyte ratio (PLR), lymphocyte–monocyte ratio (LMR),
prognostic nutritional index (PNI), and geriatric nutritional risk
index (GNRI) (7,8). It was reported that NLR and PLR could
enhance the prediction accuracy and stability for the prognosis of
limited-stage SCLC (9). Furthermore, the high PNI level appears to
be an independent beneficial predictor of patients with
chemotherapy-treated SCLC (10). According to a prospective
analysis, GNRI is linked to treatment response in extensive-stage
SCLC (11). Moreover, the controlling nutritional status score
(CONUT score) has been indicated as a predictor of recurrence
and survival time for patients with SCLC (12). The predictive ability
of these biomarkers, however, has not been examined. Furthermore,
the optimal index has not been identified. Most importantly, these
studies have used a mix of all patients with SCLC who received
distinct treatments, which may lead to a considerable bias.
Therefore, we conducted this retrospective study to determine
and compare the prognostic capability of common inflammatory/
nutritional biomarkers in patients with SCLC who received first-
line chemotherapy.
Methods
Participants
This single-center retrospective study was conducted on patients
diagnosed as having SCLC through biopsy at the West China
Hospital between January 2008 and July 2019. The study included
patients (1) diagnosed as having SCLC through biopsy, (2) whose
basic clinical information and data about peripheral blood tests
before treatment initiation were available, and (3) who received
first-line chemotherapy. We excluded patients (1) whose clinical
information and peripheral blood test details were unavailable, (2)
peripheral blood tests were after initiation of chemotherapy, and (3)
who were lost to follow-up. In total, 560 patients with SCLC who
underwent routine in-hospital laboratory tests were included in the
study. This study protocol was approved by the West China Hospital
Ethics Committee of Sichuan University.
Clinical information collection
and follow-up
To retrieve the fundamental patient data, electronic medical
records, including case notes and pathology reports, were analyzed.
Abbreviations: SCLC, small cell lung cancer; OS, overall survival; NLR,
neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; LMR,
lymphocyte–monocyte ratio; PNI, prognostic nutritional index; ALI, advanced
lung cancer inflammation index; GNRI, geriatric nutritional risk index; ScrCys,
creatinine–cystatin C ratio; CONUT score, controlling nutritional status score;
AGR, albumin–globulin ratio; LDH, lactic dehydrogenase; NSE, neuron-specific
enolase; CEA, carcino embryonic antigen.
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The retrieved data included age, sex, weight, height, VALSG stage,
smoking history, metastasis site, complication, comorbidity, and
therapy strategy. The blood biomarkers evaluated at pathological
diagnosis or before initiating chemotherapy were neutrophil count
(10
9
/L), lymphocyte count (10
9
/L), monocyte count (10
9
/L), platelet
count (10
9
/L), albumin (g/L), albumin–globulin ratio (AGR), lactic
dehydrogenase (LDH) (U/L), creatinine (mmol/L), cholesterol
(mmol/L), neuron-specific enolase (NSE) (U/mL), and carcino
embryonic antigen (CEA) (ng/mL). The survival status was
determined from the date of the last follow-up in July 2019. The
patients were followed up every 3 months by telephone. The
outcome was overall survival (OS) time, which was measured
from the time that SCLC was diagnosed up until the time of
death or the last follow-up.
Definition and cutoff values of biomarkers
NLR, PLR, and LMR represented the neutrophil–lymphocyte
count ratio, the platelet–lymphocyte count ratio, and the
lymphocyte–monocyte count ratio in the whole blood,
respectively. The PNI was defined as the albumin concentration
(g/L) in the whole blood plus five times the total lymphocyte count
(10
9
/L). The advanced lung cancer inflammation index (ALI) was
defined as body mass index × serum albumin (g/L)/NLR. The GNRI
was defined as 1.489 × albumin (g/L) −41.7 × (actual weight/ideal
weight). An ideal weight for men was defined as 0.75 × height (cm)
−62.5, whereas that for women was defined as 0.60 × height (cm) −
40. For patients whose actual weight exceeded the ideal weight, the
actual weight/ideal weight was set to 1. The creatinine–cystatin C
ratio (ScrCys) was the ratio of serum creatinine and cystatin C. The
Naples score was calculated from the NLR, LMR, and albumin and
cholesterol levels. The CONUT score was derived on the basis of the
serum albumin concentration, total blood cholesterol level, and
total peripheral lymphocyte count. The optimal cutoff values of
NLR, PLR, LMR, PNI, GNRI, AGR, ScrCys, LDH, NSE, and CEA
were determined through the maximally selected rank statistics (13,
14). The cutoff point of Naples and CONUT scores was defined as 2.
Statistics analysis
The basic clinical characteristics of the included patients were
summarized using descriptive statistics. The collinearity among
indices was conducted by Pearson correlation analysis. The
optimal cutoff values of NLR, PLR, LMR, ALI, PNI, GNRI, AGR,
ScrCys, LDH, NSE, and CEA were determined using Jamovi 2.2.5.
The prognostic values of all biomarkers and other clinical
characteristics were evaluated through the univariate Cox
regression analysis. The most important and significant
parameters without collinearity in univariate analysis were further
submitted for multivariate Cox regression analysis. The capability
to predict OS of indices was assessed using Harrell’s concordance
index (C-index), time-dependent area under the curve (t-AUC),
sensitivity, specificity, positive predictive value (PPV), and negative
predictive value (NPV). All statistical analyses were performed
using R software version 3.5.1, and all figures were charted using
ggplot2 and GraphPad Prism 8.0.
Results
Basic clinical characteristics of patients
In total, 560 patients with SCLC who met the inclusion and
exclusion criteria were included (Figure 1). Patients’demographic
characteristics are presented in Table 1 and Supplementary Table 1.
The mean age at diagnosis was 57 years (range: 28–79 years), and
the majority of the patients were men and smokers, which was
consistent with the substantial evidence (15). Approximately 67.7%
of the patients had evolved to the extensive stage during enrollment.
Pretreatment NLR, PLR, LMR, PNI, GNRI, AGR, ALI, ScrCys,
LDH, NSE, and CEA were described as continuous variables,
whereas Naples and CONUT scores were described as categorical
variables (Table 1). Liver, bone, and brain metastases were the most
common metastatic sites. A total of 11.6% of patients were
complicated with superior vena cava syndrome and 6.4% of
patients suffered from pleural or pericardial effusion at diagnosis.
Hypertension, diabetes mellitus, and chronic bronchitis with
emphysema were the most common comorbidities. Nearly all the
patients (97.5%) underwent platinum-based chemotherapy
(Figure 2,Supplementary Table S1). A total of 206 patients
received thoracic radiotherapy and 49 patients received
prophylactic cranial irradiation combined with first-line
chemotherapy (Supplementary Table S1). Median OS was 393
days, and median follow-up time was 1941 days.
Determination of the optimal cutoff and
prognostic values of indices
NLR, PLR, LMR, PNI, GNRI, ALI, AGR, ScrCys, LDH, NSE,
and CEA had appropriate cutoff values of 3.56, 143.84, 3.50, 45.15,
98.58, 159.04, 1.34, 62.79, 204.00, 22.35, and 5.36, respectively
(Supplementary Figure S1). These cutoff values were close to
those reported in previous investigations. All biomarkers were
dichotomized into high or low groups according to the
corresponding cutoff values. The univariate analysis was
conducted to reveal the unadjusted relationship between
biomarkers and OS. Except for the Naples score, all other
inflammatory/nutritional biomarkers were associated with OS
(Table 2). Because candidate indices were derived from common
blood parameters, the correlation analysis was performed. ALI, PNI,
GNRI,CONUT,NLR,PLR,andLMRshowedasignificant
correlation reciprocally with r coefficient > 0.40 and P-value <
0.05 (Figure 3;Supplementary Table S2). Therefore, ALI, AGR,
Tian et al. 10.3389/fonc.2023.1211752
Frontiers in Oncology frontiersin.org03
ScrCys, NSE, and CEA were eventually incorporated into
multivariate analysis, which were not correlated with each other.
In the multivariate Cox analysis, low AGR that indicated poor
physical nutrition or immune state remained as an independent risk
factor for survival after adjusted by sex, smoking, stage, metastasis
sites, complications, TRT, PCI, and tumor biomarkers (HR, 1.25;
95% CI, 1.02–1.54; P-value, 0.033).
Prognostic predictive performance of
inflammatory and nutritional indicators
The prognostic predictive performance of indices was assessed
using the C-index and t-AUC. As shown in Table 3 and Figure 4A,
common inflammation- and nutrition-based indices showed
inferior C-index to conventional tumor biomarkers such as NSE
and CEA, which reflected their limited values for prognostic
prediction (Table 3,Figure 4A). Among them, LDH and PNI
were the relatively desirable indices with the highest C-index of
0.568 and 0.550, respectively. Notably, most of the aforementioned
biomarkers exhibited increased predictive capabilities in long-term
survival (Figure 4B). The LDH was the most valuable indices for 3-
year survival with AUC of 0.629. Otherwise, the sensitivity,
specificity, PPV, and NPV of all the biomarkers were analyzed.
Except for NSE, the LDH showed a highest sensitivity (a high true
positive fraction of death) and NPV, whereas ALI showed a highest
specificity (a low false positive fraction of death) and PPV at
different time points (Tables 4,5;Supplementary Tables S3,S4).
Subgroup analysis of the predictive
value of inflammation/nutrition-
based biomarkers
We further assessed the values of the inflammation/nutrition-
based biomarkers for SCLC subgroups. All the biomarkers
remained poor predictive performance in all the subgroups with
C-index in the 0.500–0.600 range. Compared with other indices, the
LDH, PNI, and AGR were the most prominent predictors for men
and smokers. These groups of patients accounted for the majority of
participants. Similarly, the LDH showed increasing predictive
values, whereas the PNI showed diminishing values in this
population as the survival time prolonged. The LDH was
significant predictor for women or non-smokers (Supplementary
Tables S5–S8).
Discussion
Currently, numerous nutrition- and inflammation-based
indices have been shown to serve as prognostic markers for
SCLC. We verified and contrasted the prognostic predictive
efficiency of these indices in a sizable cohort. The AGR was an
independent factor for survival after adjusted for clinical
information. However, all these prognostic biomarkers or scores
had C-index and t-AUC ranging from 0.500 to 0.700, which
indicated that these peripheral blood parameters were not
effective enough in prognosis prediction. The LDH and PNI were
FIGURE 1
Flowchart of the selection of study population and exclusion criteria.
Tian et al. 10.3389/fonc.2023.1211752
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relatively valuable with C-index of 0.568 and 0.550, respectively.
The LDH and ALI showed highest sensitivity (negative predictive
value) and specificity (positive predictive value), respectively,
indicating a promising significance of predicting survival and
death at different time points.
As a canonical tumor and inflammation biomarker, LDH that
had collinearity relationship with NSE showed superior
performance in prognostic prediction in this study, especially for
long-term survival. Previous studies have confirmed that the LDH
level was an independent risk for chemotherapy effect and OS of
SCLC (16,17). Cancer cells metabolizes 10-fold glucose through the
glycolysis pathway rather than through mitochondrial respiration
to produce energy known as the Warburg effect, in which LDH
catalyzes the transformation of pyruvate to lactic acid (18). It is also
associated with tumor burden, tumor immunogenicity, and
activation of oncogenic signaling pathways (19). Although a
partial list of innovative prognostic scores were proposed present
study demonstrated the relative superiority of LDH and NSE for
patients with SCLC undergoing first-line chemotherapy.
Meanwhile, the LDH and NSE with highest sensitivity (highest
negative predictive value) may be conducive to predicting survival
probability at different time points.
Second to LDH and NSE, the PNI was also manifested as a
valuable index for prognostic evaluation, especially for short-term
survival in smokers or men subgroups. A pooled analysis including
4,164 patients with SCLC suggested that low PNI was correlated
with decreased OS in SCLC (20). The study also illustrated that
Eastern Cooperative Oncology Group (ECOG) performance status
(PS) ≥2, extensive stage, and PCI were influencing factors for PNI.
Another study presented the efficacy of the PNI for survival
prognostication in patients with SCLC treated with platinum-
based chemotherapy with an AUC of 0.564, which resembled our
result with a C-index of 0.550 (10). Albumin is involved in the
transportation of fatty acids, cholesterol, metal ions, therapeutic
agents, and antioxidant effects. Reduced albumin level signifies
multiple physiological function impairment, which negatively
affects outcomes. Patients with low albumin levels, a sign of poor
nutritional status, are prone to experiencing cachexia or infection.
Lymphocyte deficiency symbolizes host immunosuppression,
which favors tumor development and pathogen aggression. A
study suggested that patients with SCLC who received
radiotherapy showed aberrant alteration of circulating
lymphocyte subsets (21). It was also demonstrated that precursors
in peripheral blood could contribute to terminal tumor-infiltrating
CD8+ T lymphocytes (22). Because of the correlation between
ECOG-PS and PNI, their efficacy for prognosis assessment should
have been compared. Similar to PNI, the AGR also reflects physical
nutritional and immune status. It could serve as a simple prognostic
marker in patients with SCLC (23), which was proved in our study,
especially for smokers and men subgroups. Albumin synthesis
decreases in response to the production of some inflammatory
factors involved in tumor immunity and the acute phase response,
such as tumor necrosis factor, interleukin-6, and C-reactive protein
(CRP) (24–26). In contrast to the trend in albumin variability,
globulin synthesis increases with the accumulation of CRP and
other acute-phase reactants (27). Consequently, lower AGR is
associated with worse survival in patients with advanced
malignancy and a high risk of tumor recurrence after undergoing
complete resection (23,28,29).
As opposed to LDH, the ALI exhibited outstanding specificity
(positive predictive value) at different time points. That is to say, a
low level of ALI could be used to predict death probability.
Accumulating investigations tend to concentrate on the
importance of neutrophil, lymphocyte, and albumin. The ALI is
TABLE 1 Baseline characteristics of all study participants.
Variables All (n = 560)
Age (year), Mean (SD) 57.04 (9.51)
Sex, n (%)
Male 425 (75.9)
Female 135 (24.1)
Smoking, n (%)
Yes 395 (70.5)
No 165 (29.5)
BMI(Kg/m2), Mean (SD) 23.02 (3.18)
Stage, n (%)
Limited stage 181 (32.3)
Extensive stage 379 (67.7)
ALI, Median (IQR) 307.55 (208.04, 450.10)
PNI, Mean (SD) 48.29 (5.91)
GNRI, Median (IQR) 100.52 (95.27, 105.58)
ScrCys, Median (IQR) 73.66 (65.81, 83.37)
AGR, Median (IQR) 1.48 (1.28, 1.67)
NLR, Median (IQR) 3.01 (2.17, 4.14)
PLR, Median (IQR) 138.93 (98.04, 191.34)
LMR, Median (IQR) 3.45 (2.46, 4.61)
Naples score, n (%)
≤2281 (50.2)
>2 279 (49.8)
CONUT score, n (%)
≤2378 (67.5)
>2 183 (32.7)
LDH (U/L), Median (IQR) 210 (173,275)
NSE (U/mL), Median (IQR) 44.92 (23.25,90.55)
CEA (ng/mL), Median (IQR) 3.62 (1.97,8.79)
NLR, neutrophil–lymphocyte ratio; PLR, plate let–lymphocyte ratio; LMR, lym phocyte–
monocyte ratio; PNI, prognostic nutritional index; ALI, advanced lung cancer
inflammation index; GNRI, geriatric nutritional risk index; ScrCys, creatinine–cystatin C
ratio; CONUT score, controlling nutritional status score; AGR, albumin-globulin ratio; LDH,
lactic dehydrogenase; NSE, neuron-specific enolase; CEA, carcino embryonic antigen. SD,
standard deviation; IQR, interquartile range.
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BC
A
FIGURE 2
Baseline clinical characteristics of the included patients. (A) Metastasis sites, (B) complications or comorbidities, and (C) chemotherapy regimens of
the included patients.
TABLE 2 Univariate and multivariate analyses in relation to the patient’s overall survival.
Variables
Univariate analysis Multivariate analysis
HR 95% CI Pvalue HR 95% CI P-value
Sex (male) 1.44 1.17–1.78 0.001 1.25 0.81–1.92 0.321
Age 1.01 1.00–1.02 0.117
Smoking (yes) 1.43 1.18–1.74 <0.001 1.22 0.82–1.81 0.329
Stage (LS) 0.56 0.46–0.69 <0.001 0.92 0.71–1.19 0.508
BMI 0.97 0.95–1.00 0.076
Pleura or pericardium metastasis (yes) 1.29 0.99–1.69 0.064
Brain metastasis (yes) 1.42 1.13–1.79 0.003 1.04 0.79–1.37 0.762
Bone metastasis (yes) 1.26 1.00–1.60 0.052
Liver metastasis (yes) 1.68 1.34–2.11 <0.001 1.14 0.88–1.49 0.322
Adrenal gland metastasis (yes) 1.26 0.97–1.64 0.081
Superior vena cava syndrome (yes) 1.44 1.10–1.87 0.007 1.38 1.02–1.87 0.035
Pleural or pericardial effusion (yes) 1.50 1.07–2.11 0.019 0.97 0.66–1.43 0.878
CB with emphysema (yes) 1.49 1.17–1.90 0.001 1.12 0.85–1.48 0.401
Chronic viral hepatitis (yes) 0.83 0.60–1.16 0.284
Hypertension (yes) 0.87 0.68–1.10 0.247
Diabetes mellitus (yes) 1.05 0.79–1.40 0.726
Thoracic radiotherapy (TRT) (yes) 0.44 0.36–0.54 <0.001 0.58 0.45–0.75 <0.001
PCI (yes) 0.51 0.36–0.73 <0.001 0.64 0.44–0.94 0.021
EP vs. EC 1.07 0.81–1.42 0.617
LDH (low) 0.63 0.53–0.75 <0.001
NSE (low) 0.56 0.44–0.70 <0.001 0.75 0.58–0.96 0.022
CEA (low) 0.66 0.54–0.79 <0.001 0.72 0.58–0.88 0.002
NLR (low) 0.74 0.62–0.89 0.001
PLR (low) 0.76 0.64–0.91 0.003
LMR (low) 1.35 1.13–1.62 0.001
(Continued)
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TABLE 2 Continued
Variables
Univariate analysis Multivariate analysis
HR 95% CI Pvalue HR 95% CI P-value
Naples score (≤2) 0.92 0.77–1.10 0.345
ALI (low) 1.71 1.34–2.20 <0.001 1.28 0.96–1.72 0.091
PNI (low) 1.50 1.25–1.82 <0.001
GNRI (low) 1.41 1.18–1.69 <0.001
ScrCys (low) 1.32 1.05–1.65 0.018 1.27 0.98-1.65 0.071
CONUT score (≤2) 0.80 0.67–0.97 0.022
AGR (low) 1.44 1.20–1.72 <0.001 1.25 1.02–1.54 0.033
LS, limited stage; BMI, body mass index; CB, chronic bronchitis; EP, etoposide + cisplatin; EC, etoposide + carboplatin; ScrCys, creatinine–cystatin C ratio; PCI, prophylactic cranial irradiation.
FIGURE 3
The correlation analysis among all the indices. P< 0.05 was labeled as *.
TABLE 3 Prognostic predictive performance of all the biomarkers.
Variables C-index 1-year AUC 2-year AUC 3-year AUC
NLR 0.532 0.538 (0.498–0.578) 0.544 (0.500–0.589) 0.610 (0.564–0.657)
PLR 0.540 0.551 (0.498–0.578) 0.558 (0.500–0.589) 0.574 (0.564–0.657)
LMR 0.544 0.57 (0.529–0.611) 0.538 (0.490–0.586) 0.586 (0.529–0.642)
(Continued)
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B
A
FIGURE 4
Prognostic predictive performance of the biomarkers. (A) C-index and time-dependent AUC at 1-, 2-, and 3-year survival of all the indices.
(B) Biomarkers showed better prognostic predictive value for long-term survival.
TABLE 3 Continued
Variables C-index 1-year AUC 2-year AUC 3-year AUC
ALI 0.539 0.553 (0.524–0.582) 0.545 (0.517–0.573) 0.555 (0.527–0.583)
GNRI 0.548 0.561 (0.520–0.602) 0.562 (0.517–0.608) 0.575 (0.522–0.628)
ScrCys 0.523 0.528 (0.500–0.560) 0.536 (0.501–0.570) 0.565 (0.532–0.598)
CONUT 0.532 0.544 (0.505–0.583) 0.521 (0.476–0.565) 0.553 (0.502–0.603)
AGR 0.543 0.557 (0.517–0.597) 0.551 (0.507–0.595) 0.576 (0.526–0.626)
PNI 0.550 0.575 (0.537–0.613) 0.546 (0.504–0.588) 0.572 (0.525–0.620)
LDH 0.568 0.606 (0.565–0.647) 0.609 (0.562–0.656) 0.629 (0.574–0.684)
NSE 0.560 0.584 (0.548–0.620) 0.614 (0.567–0.661) 0.635 (0.577–0.694)
CEA 0.555 0.591 (0.550–0.631) 0.601 (0.559–0.643) 0.597 (0.549–0.646)
t-AUC, the time-dependent area under ROC; ScrCys, creatinine–cystatin C ratio.
TABLE 4 Sensitivity of all the biomarkers to identify mortality risk at different time points.
Indices Sensitivity (1 year) Sensitivity (2 years) Sensitivity (3 years)
NLR 0.401 0.386 0.4
PLR 0.525 0.501 0.493
LMR 0.584 0.532 0.541
ALI 0.195 0.163 0.157
PNI 0.385 0.331 0.335
GNRI 0.463 0.429 0.424
ScrCys 0.214 0.201 0.201
CONUT 0.374 0.338 0.344
AGR 0.409 0.376 0.379
LDH 0.646 0.587 0.579
NSE 0.850 0.817 0.809
CEA 0.450 0.404 0.390
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exactly a composite index combining these parameters. Neutrophil
infiltration and neutrophils extracellular traps in tumor
microenvironment have been demonstrated to facilitate tumor
progression and metastasis (30,31). Tumor cells in return can
produce granulocyte colony-stimulating factor, which skews the
balance of neutrophil retention and release in bone marrow causing
alteration of circulation neutrophils counts (32). A recent research
conceded that ALI was the optimal inflammatory biomarkers of
overall survival in patients with lung cancer (14). However, they did
not report the sensitivity and specificity of ALI. We observed a wide
range of cutoff points among various relevant studies, which will
have profound impact on sensitivity and specificity of the
biomarker (33–35). Thus, the excellent specificity in our study
should be validated in an external cohort.
Regardless, because of the critical role of metabolism and
inflammation in cancer occurrence and management, nutrition-
or inflammation-based biomarkers can be employed as adjuvant
measurements in prognostic estimation. However, their
performance is poor and inferior to conventional tumor
biomarkers. Actually, similar outcomes were also described in
other available studies (14,35). Because the peripheral biomarkers
are not entirely identical to that in tumor microenvironment, and
patients’clinical manifestations are volatile, their application in
clinical practice should be prudent.
Our study has some limitations. First, data of more completed
variables, including serum CRP, and procalcitonin, were not
acquired during baseline data collection and processing, leading
to the absence of some crucial biomarkers such as CRP/albumin.
Moreover, TNM stage, ECOG-PS, response evaluation, and later-
line treatment after chemotherapy resistance should have been
included as clinical variables. In addition, according to the NCCN
guideline, chemoimmunotherapy is preferred as the first-line
systemic therapy for patients with extensive-stage SCLC with an
ECOG performance score of 0–2. Hence, studies involving a
population undergoing novel treatment strategies are warranted.
Finally, this was a retrospective study without independent external
validation and, thus, inevitably involves considerable bias.
Conclusion
Common inflammatory or nutritional indices are only
marginally useful in predicting outcomes in patients with SCLC
receiving first-line chemotherapy. The aforementioned variables
should be prudently used as adjuvant measurements in clinical
practice. Among them, the LDH and PNI are relatively superior.
The LDH and ALI are promising biomarkers to identify death and
live patients at different time points.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Author contributions
Conception and design: HT, GL, WH, and JJ. Administrative
support: DL, WL, and JW. Provision of the study materials
orpatients: CW and PR. Collection and assembly of the data: HT,
GL, and WH. Data analysis and interpretation: HT and JJ. Review of
the manuscript: GL, HW, and JW. Manuscript writing: All authors.
Final approval of manuscript: All authors.
Funding
This work was supported by the National Natural Science
Foundation of China under grant 82173182 and the National Key
TABLE 5 Specificity of all the biomarkers at different time points.
Indices Specificity (1 year) Specificity (2 years) Specificity (3 years)
NLR 0.675 0.703 0.824
PLR 0.576 0.616 0.655
LMR 0.556 0.543 0.631
ALI 0.911 0.928 0.952
PNI 0.765 0.761 0.810
GNRI 0.659 0.696 0.726
ScrCys 0.841 0.870 0.930
CONUT 0.715 0.703 0.762
AGR 0.705 0.725 0.774
LDH 0.566 0.630 0.679
NSE 0.318 0.411 0.462
CEA 0.731 0.797 0.805
Tian et al. 10.3389/fonc.2023.1211752
Frontiers in Oncology frontiersin.org09
Research and Development Program of Science and Technology
Ministry under grant 2017YFC0910004.
Acknowledgments
The authors thank all the patients involved in the study.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1211752/
full#supplementary-material
SUPPLEMENTARY FIGURE 1
Determination cutoff points of all the biomarkers.
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