Thyroid Dysfunction as a Prognostic Indicator for Overall Survival in Advanced Lung Cancer Patients Treated with PD-1/PD-L1 Inhibitors

Thyroid Dysfunction as a Prognostic Indicator for Overall Survival in Advanced Lung Cancer Patients Treated with PD-1/PD-L1 Inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Thyroid Dysfunction as a Prognostic Indicator for Overall Survival in Advanced Lung Cancer Patients Treated with PD-1/PD-L1 Inhibitors Xiaoping Ma, Yanling Wang, Jing Li, Zhiyi Lin, Ping Gong, Jing Fei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8395613/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective This study aims to explore the correlation between the occurrence of thyroid dysfunction (TD) and the overall survival (OS) of advanced lung cancer patients treated with PD-1/PD-L1 inhibitors, providing a basis for personalized diagnosis and treatment. Method The data of 60 patients who were initially diagnosed with advanced lung cancer at two hospitals in Shihezi, Xinjiang, China from January 2019 to August 2024 were retrospectively collected. The baseline thyroid function was normal. The patients were divided into TD and non-TD groups based on TD occurrence to analyze the correlation between TD occurrence and prognosis. Results The median OS was longer in the TD group than in the non-TD group (19.77 vs. 15.9 months), accompanied by a numerically lower risk of mortality (HR: 0.751, 95% CI (0.414–1.362), p = 0.345). The 1-year OS rate of the TD group was numerically lower than that of the non-TD group (66% vs. 68%, RD (risk difference) = 0.02, NNT (number needed to treat) = 50). However, the 2- (45% vs. 24%, RD = -0.21, NNT = 5), 3- (30% vs. 12%, RD = -0.18, NNT = 6), and 4-year OS rates (30% vs. 12%, RD = -0.18, NNT = 6) were significantly higher than those of the non-TD group. There were differences in OS between different types of TD ( p 1, p < 0.05). Compared with patients with other types of TD, patients with overt hypothyroidism and low-T3 syndrome had significantly shorter OS (11.92 vs. 28.13 months, 5.4 vs. 20.85 months), and they were also risk factors for mortality (HR > 1, p < 0.05). Conclusion The occurrence of TD is positively correlated with the good prognosis of advanced lung cancer patients treated with PD-1/PD-L1 inhibitors, and the correlation becomes increasingly significant over time. There are also some differences in prognosis between different types of TD. Thyroid dysfunction PD-1/PD-L1 inhibitors lung cancer overall survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction According to the latest data released by the International Agency for Research on Cancer (IARC) [ 1 ], lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death in most countries [ 2 ]. More than half of lung cancer patients are already in the intermediate and advanced stages when diagnosed, losing the chance of radical surgery [ 3 ]. The 5-year overall survival (OS) rate is extremely low (less than 5%) [ 4 ]. The emergence of immune checkpoint inhibitors (ICIs) has brought new hope to advanced lung cancer patients [ 5 ]. Programmed Cell Death Protein 1 (PD-1)/Programmed Death Ligand 1 (PD-L1) inhibitors are the most common ICIs and have been widely used in the treatment of advanced lung cancer [ 6 – 8 ]. In recent years, many studies have found that PD-1/PD-L1 inhibitor treatment can lead to unique immune-related adverse events (irAEs) in multiple systems [ 9 ]. Thyroid dysfunction (TD) is one of the common irAEs, accounting for approximately 6–8% of all irAEs [ 10 ]. According to relevant research [ 10 , 11 ], about 20% of advanced lung cancer patients developed TD when treated with PD-1/PD-L1 inhibitors. However, in fact, the incidence of TD can reach up to 40%-50%. Interestingly, many studies have found that the occurrence of TD seems to be associated with better efficacy [ 12 , 13 ]. This was also confirmed in our previous study [ 14 ]. This may be because patients who develop TD have better activation of immune responses [ 15 ]. Our previous study only observed PFS (progression-free survival). Although in most cases the longer the PFS, the longer the OS, there are also unrelated or even opposite cases. Therefore, PFS can only be used as a surrogate endpoint for OS. Pseudo progression (psPD) is a rare phenomenon that occurs when ICIs are used for the therapy of melanoma [ 16 ] and non-small cell lung cancer [ 17 ], in which the tumor first increases and then shrinks. It suggests the delayed anti-tumor effect of immunotherapy. However, it is generally difficult to identify true progressive disease (TPD) in clinical practice. This leads to a certain difference between PFS and OS, failing to fully capture the clinical benefits of PD-1/PD-L1 inhibitors [ 18 ]. Therefore, it is very necessary to further study OS when the results of PFS have been obtained. This study continues to follow up the patients in the previous study until the patient dies, to more intuitively evaluate whether there is a correlation between TD occurrence and OS. This study puts forward the hypothesis that the occurrence of TD is related to the prolongation of OS. The study results will provide certain guidance for clinicians to make decisions. This is an exploratory study. The sample size is based on our previous study and case availability. Notably, the prognosis may also be different among different types of TD, but no conclusion has yet been reached on this matter. Muir et al. [ 19 ] proposed that overt thyrotoxicosis might be a surrogate marker of a robust immune response to ICI treatment. No improvement in survival was observed for other thyroid irAE subtypes. However, Beak et al. [ 20 ] found that patients with new-onset overt hypothyroidism had a significantly lower hazard ratio for mortality than the thyrotoxicosis group. Patients who had already taken thyroid hormones to treat hypothyroidism had a better prognosis. Therefore, they believed that the pathogenesis of different types of TD was consistent, and thyrotoxicosis was transient and eventually transformed into hypothyroidism [ 21 ]. The stimulating effect of TSH (thyroid-stimulating hormone) may be related to the improvement of prognosis, but the mechanism needs to be further explored. Therefore, whether there are differences in prognosis between different types of TD is still controversial. More clinical data is needed to clarify it. 2. Methods 2.1 Research subjects and inclusion and exclusion criteria This study is based on an extended follow-up of our previous research cohort [ 14 ]. This design reduces the influence of confounding factors and ensures the consistency of data. The research subjects were 60 advanced lung cancer patients who received initial treatment at the First Affiliated Hospital of Shihezi University and the People's Hospital of Shihezi City from January 2019 to August 2024. All patients received at least 2 cycles of PD-1/PD-L1 inhibitor treatment combined with chemotherapy (some patients also received bevacizumab treatment), including 21 cases of squamous-cell carcinoma, 17 cases of adenocarcinoma, and 22 cases of small-cell carcinoma. All patients were treated in accordance with the Clinical Practice Guidelines for Malignant Lung Tumors of the Chinese Society of Clinical Oncology (CSCO). Among them, 42 patients were treated with PD-1 inhibitors and 18 patients were treated with PD-L1 inhibitors. This study has been approved by the Ethics Committee of the First Affiliated Hospital of Shihezi University, with ethics number KJX2022-081-01. All patients or their families (when applicable) were informed and signed informed consent forms, agreeing to use their clinical data for this study. This study complies with the Declaration of Helsinki. Inclusion criteria were as follows: (1) The patient was over 18 years old; (2) The patient was confirmed to have advanced lung cancer by pathological tissue, cytology, and imaging (tumor node metastasis (TNM) staging: stage 3 (non-surgical) or 4); (3) The patient was newly diagnosed and had not undergone any anti-tumor treatment; (4) The patient had received standardized PD-1/PD-L1 inhibitor-based combination therapy or other regimens for at least 2 cycles; (5) The patient with a physical status (PS) score greater than or equal to 2; (6) The patient's survival period exceeds 3 months. Exclusion criteria were as follows: (1) Before treatment, there was TD, concomitant thyroid-related diseases, thyroid surgery, radiotherapy for thyroid, or autoimmune disease; (2) The patient had toxic goiter-induced TD; (3) The patient had concomitant malignant tumors or had previously suffered from other malignant tumors and undergone anti-tumor treatment; (4) The patient also had severe heart failure, liver and kidney dysfunction, diseases of hematopoietic system, etc.; (5) Clinical evidence of chronic inflammatory disease, severe acute infection or inflammation before treatment and/or during efficacy evaluation; (6) Patients who were lost to follow-up or who’s complete clinical data was unavailable. 2.2 Clinical observation indicators The information of the patient's thyroid function (thyroid-stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4)) was collected 1–2 days before treatment. Subsequent follow-up was performed every 2 cycles of treatment. Data were collected until the patient died, lost to follow-up, or the cut-off date (February 2025). 2.3 Thyroid function evaluation Thyroid function was evaluated as follows: TD was defined by one or more consecutive abnormal TSH values, regardless of fT4 and fT3 levels (those with reduced fT3 only were also recorded as TD in this study). Hypothyroidism: elevated TSH and reduced fT4 and/or fT3 levels. Subclinical hypothyroidism: only elevated TSH. Hyperthyroidism: reduced TSH and elevated fT4 and/or fT3 levels. Subclinical hyperthyroidism: Only reduced TSH. Low-T3 syndrome: only reduced fT3 levels, with normal TSH and fT4 levels. The indicator ranges of normal thyroid function in this study were as follows: TSH: 0.27–4.2 mIU/L; fT3: 3.1–6.8 pmol/L; fT4: 12.0–22.0 pmol/L. Patients received replacement therapy with levothyroxine when the TSH value was larger than 10 mIU/L. The TD was graded with reference to the Common Terminology Criteria for Adverse Events (CTCAE5.0). 2.4 Outcome indicators Primary outcome indicator: OS, i.e., the time from the start of treatment to death. Secondary outcome indicators were as follows: 1-, 2-, 3-, and 4-year OS rate, i.e., the ratio of the number of patients who survived 1, 2, 3, and 4 years of follow-up to the total number of cases, respectively. 2.5 Grouping and analysis The cases were divided into TD and non-TD groups based on whether TD occurred. The differences in OS between the two groups were analyzed. According to the level of thyroid function, TD was divided into subclinical hypothyroidism, hypothyroidism, subclinical hyperthyroidism, hyperthyroidism, and low-T3 syndrome. The differences in OS between different types of TD were analyzed. The cases with TD were divided into a treatment subgroup and a non-treatment subgroup based on whether the patient received relevant treatment. The differences in OS between the two subgroups were analyzed. 2.6 Statistical analysis SPSS 26.0 software was used for statistical processing. Normally distributed variable data were represented by mean ± standard deviation (χ ± s). The t-test was conducted for comparison between the two groups. ANOVA was conducted for multiple group comparisons. Median and interquartile range (M, IQR) were used for non-normally distributed continuous variables. The nonparametric test was conducted for comparison between groups. Counting data was presented in terms of the number of cases and the rate (%). The Pearson X 2 test was used for inter-group comparison. The Wilcoxon rank sum test was used for comparison of rank data. The Kaplan-Meier method was used for survival analysis. The Log-rank test was used for inter-group comparison of survival. Graphpad Prism (10.1.2) was used to plot survival curves. Multivariate Cox regression analysis was performed on factors with statistical significance in univariate Cox regression analysis to evaluate the hazard ratio (HR) and 95% confidence interval (CI). R software was used for landmark analysis to avoid false-positive associations caused by incomplete follow-up. Differences were considered statistically significant at p < 0.05. 3. Results 3.1 Comparison of general conditions between the TD and non-TD groups Thyroid dysfunction occurred in 29 of 60 advanced lung cancer patients treated with PD-1/PD-L1 inhibitors. There were no significant differences between the TD and non-TD groups in terms of gender, age, ‌body mass index‌ (BMI), smoking history, clinical stage, pathological type, tumor type, PS score, distant metastasis, combination treatment regimen, and PD-1/PD-L1 inhibitor type. Thus, they were comparable (Table 1 ). Table 1 Comparison of general conditions between the TD and non-TD groups. General information TD group Non-TD group p n = 29 n = 31 Sex (n (%)) 22 (75.9%) 27 (87.1%) 0.261 Age (x ± s) 63.03 ± 10.46 66.35 ± 9.63 0.206 < 65 (n (%)) 17 (58.6%) 14 (45.2%) 0.297 BMI (x ± s) 23.27 ± 3.97% 23.41 ± 2.84% 0.873 < 18.5 (n (%)) 4 (13.8%) 1 (3.2%) 0.805 18.5 ≤ X ≤ 23.9 (n (%)) 10 (34.5%) 16 (51.6%) 24.0 ≤ X ≤ 27.9 (n (%)) 11 (37.9%) 12 (38.7%) ≥ 28.0 (n (%)) 4 (13.8%) 2 (6.5%) Smoking history (n (%)) Never 11 (37.9%) 10 (32.3%) 0.532 Currently 17 (58.6%) 17 (54.8%) Quit 1 (3.4%) 4 (12.9%) Clinical Stage (n (%)) Stage 3 4 (13.8%) 6 (19.4%) 0.817 Stage 4 25 (86.2%) 25 (80.6%) Pathological type (n (%)) Squamous carcinoma 10 (34.5%) 11 (35.5%) 0.980 Adenocarcinoma 8 (27.6%) 9 (29.0%) Small cell carcinoma 11 (37.9%) 11 (35.5%) Tumor type (n (%)) NSCLC 18 (62.1%) 20 (64.5%) 0.844 SCLC 11 (37.9%) 11 (35.5%) PS score (n (%)) 1 score 18 (62.1%) 20 (64.5%) 0.844 2 score 11 (37.9%) 11 (35.5%) Metastasis (n (%)) Bone 6 (20.7%) 6 (19.4%) 0.897 Brain 3 (10.3%) 2 (6.5%) 0.938 Liver 5 (17.2%) 2 (6.5%) 0.369 Combination therapy (n (%)) Chemotherapy 24 (82.8%) 25 (80.6%) 0.833 Chemotherapy + bevacizumab 5 (17.2%) 6 (19.4%) Types of PD-1/PD-L1 inhibitors (n (%)) PD-1 19 (65.5%) 23 (74.2%) 0.464 PD-L1 10 (34.5%) 8 (25.8%) 3.2 Comparison of OS between the TD and non-TD groups The median follow-up time in this study was 17.65 months, and the longest follow-up time was 57.43 months (survivors, TD group). During the follow-up, 20 and 24 cases in the TD (20/29, 69%) and non-TD (24/31, 77%) groups died, respectively. The median OS (mOS) of the TD group was longer than that of the non-TD group (19.77 vs. 15.9 months), accompanied by a lower risk of mortality (HR: 0.751, 95% CI (0.414–1.362), p = 0.345) (Fig. 1 ). The 1-year OS rate of the TD group was numerically lower than that of the non-TD group (66% vs. 68%, RD (risk difference) = 0.02, NNT (number need to treat) = 50). However, the 2- (45% vs. 24%, RD = -0.21, NNT = 5), 3- (30% vs. 12%, RD = -0.18, NNT = 6), and 4-year (30% vs. 12%, RD = -0.18, NNT = 6) OS rates were significantly higher than those of the non-TD group (Fig. 2 ). Landmark analysis for avoiding false-positive associations caused by incomplete follow-up showed that with 12 months as the cutoff point, the difference in OS between the TD and non-TD groups increased after 12 months ( HR : 0.567 (< 0.751), 95% CI [0.25–1.29]) , p = 0.169 (< 0.345)) (Fig. 3 ). 3.3 Analysis of confounding factors affecting overall survival The results of univariate COX regression analysis showed that age, PS score, and liver metastasis were possible influencing factors of OS ( p ≤ 0.1) (Fig. 4 ). After incorporating the above single factors into the multivariate COX regression model, it was found that PS score was an independent risk factor for mortality in advanced lung cancer patients ( HR : 2.453, 95% CI [1.228–4.899] , p = 0.011). In addition, liver metastasis was also an independent risk factor for mortality ( HR : 2.256, 95% CI [1.038–6.314] , p = 0.041) (Fig. 5 ). 3.4 Subgroup analysis 3.4.1 Comparison of overall survival between the treated and non-treated patients with thyroid dysfunction During the follow-up process, 5 patients developed grade 2 TD (all developed hypothyroid) and underwent replacement therapy with levothyroxine sodium tablets. The comparison of OS between the treatment and non-treatment subgroups showed that the mOS of the treatment subgroup was shorter than that of the non-treatment subgroup (16.27 vs. 20.85 months), accompanied by a numerically higher risk of mortality ( HR : 1.679, 95% CI (0.468–6.013) , p = 0.426) (Fig. 6 ). 3.4.2 Comparison of overall survival among different types of TD When PD-1/PD-L1 inhibitors were used to treat advanced lung cancer patients, there was a difference in OS between different types of TD and the non-TD group ( p = 0.0007) (Fig. 7 ) . The mOS of patients with subclinical hypothyroidism (42.78 months), subclinical hyperthyroidism (not reached), and hyperthyroidism (16.27 months) was longer than that of patients who did not experience TD (15.9 months). The three were protective factors for mortality ( HR : 0.440, p = 0.074; HR : 0.227, p = 0.148, HR : 0.972, p = 0.959), but the differences were not statistically significant. Patients with low-T3 syndrome had significantly shorter OS than those without TD (5.4 vs. 15.9 months). Thus, low-T3 syndrome was a risk factor for mortality (HR > 1, p 1), the difference was not statistically significant ( p > 0.05) (Table 2 ). Table 2 Comparison of overall survival (OS) between different types of thyroid dysfunction (TD) and the non-TD group. Type of TD n (29) mOS (month) p HR for OS (95% CI ) Non-TD group 31 15.90 / / Subclinical hypothyroidism 12 42.78 0.074 0.440 (0.178–1.084) Hypothyroidism 8 11.92 0.235 1.629 (0.729–3.640) Subclinical hyperthyroidism 3 N 0.148 0.227 (0.030–1.690) Hyperthyroidism 5 16.27 0.959 0.972 (0.335–2.820) Low-T3 syndrome 1 5.40 0.014 17.617(1.805-171.958 The results of the multivariate Cox regression analysis showed that after excluding confounding factors (TD type as well as the influencing factors for OS obtained above (age, PS score, and liver metastasis) (Fig. 4 )), patients with low-T3 syndrome had a significantly higher risk of mortality than the non-TD group ( HR = 11.632, 95% CI (1.161-116.594) , p = 0.037). In addition, PS score was an independent risk factor for mortality ( HR = 2.460, 95% CI (1.180–5.129) , p = 0.016) (Fig. 8 ). There were differences in OS among different types of TD ( p = 0.0003) (Fig. 9 , Table 3 ). The mOS of patients with overt hypothyroidism and low-T3 syndrome was significantly shorter than that of patients with other types of TD (11.92 (overt hypothyroidism) vs. 28.13 (other types of TD except for overt hypothyroidism) months; 5.4 (low-T3 syndrome) vs. 20.85 (other types of TD except for low-T3 syndrome) months). Thus, hypothyroidism and low-T3 syndrome were risk factors for mortality (HR > 1, p 1), the difference was not statistically significant ( p > 0.05). The mOS of patients with subclinical hypothyroidism and subclinical hyperthyroidism was longer than that of patients with other types of TD (42.78 (subclinical hypothyroidism) vs. 16.27 months (other types of TD except for subclinical hypothyroidism); not reached (subclinical hyperthyroidism) vs. 17.04 (other types of TD except for subclinical hyperthyroidism). Thus, subclinical hypothyroidism and subclinical hyperthyroidism were protective factors for mortality (HR: 0.474, p = 0.097; HR: 0.426, p = 0.159), but the difference was not statistically significant ( p > 0.05). Table 3 Comparison of the overall survival (OS) of patients with different types of thyroid dysfunction (TD). Type of TD n (29) mOS (month) p HR for OS (95% CI ) Subclinical hypothyroidism 12 42.78 0.097 0.474 (0.196–1.145) Hypothyroidism 8 11.92 0.022 3.672 (1.208–11.16) Subclinical hyperthyroidism 3 N 0.159 0.426 (0.130–1.398) Hyperthyroidism 5 16.27 0.513 1.515 (0.437–5.253) Low-T3 syndrome 1 5.40 0.0004 NA Note: NA, not applicable. COX regression analysis was only conducted on patients with TD to avoid the influence of confounding factors. According to univariate analysis (Fig. 10 ), TD type, PS score, and liver metastasis were influencing factors for OS ( p ≤ 0.1). Then, these factors were included in the multifactorial Cox regression model. The results showed that after excluding confounding factors, the risk of mortality in patients with subclinical hypothyroidism and subclinical hyperthyroidism was significantly lower than that of patients with low-T3 syndrome ( HR = 0.039, 95% CI (0.220–0690) , p = 0.027); HR = 0.014, 95% CI (0.000-0.432) , p = 0.015). In addition, PS score was an independent risk factor for mortality (HR = 4.521, 95% CI (1.537–13.302), p = 0.006) (Fig. 11 ). 4. Discussion The relationship between the occurrence of TD and patient prognosis is currently a hot and controversial research topic. Zhou et al. [ 22 ] and Chmielewska et al. [ 23 ] found that patients with TD had significantly longer OS and PFS (HR < 0, p < 0.05) than patients without TD. However, Xie et al. [ 24 ] found that patients with TD had a significantly higher ORR (objective response rate) (44.4% vs. 16.4%), but there was no difference in PFS and OS ( p > 0.05). Against this backdrop, this study was conducted. It was found that there was a complex relationship between the occurrence of TD and patient prognosis. In our previous research [ 14 ], the PFS of the TD group was significantly longer than that of the non-TD group (mPFS: 8.83 vs. 6.50 months, p = 0.041). However, in this study, although there was a difference in mOS between the TD and non-TD groups, i.e., the TD group had a longer OS (19.77 vs. 15.9 months), this difference was not statistically significant ( p = 0.345). The longer the PFS, the longer the OS, but there are also inconsistencies, i.e., in some large-scale studies. The survival follow-up results of the study JO25567 [ 25 ] showed that the combination of bevacizumab with erlotinib was significantly associated with an enhancement in PFS (mPFS: 16.4 vs. 9.8 months, p = 0.0005), but there was no significant enhancement in OS (mOS: 47.0 vs. 47.4 months). This inconsistency may be related to multiple factors, including but not limited to disease staging, patient physical condition, comorbidities, and subsequent treatment. Although the influences of these factors were adjusted in this study, there may still be unidentified confounding factors. Secondly, this may be related to the small sample size and the resulting insufficient statistical power in this study. In future research, the sample size will be further expanded to more accurately evaluate the impact of TD on overall survival in advanced lung cancer patients. However, upon further analysis of the annual OS rate, it was found that the downward trend in OS rate for patients with TD over time was significantly slower than that of the non-TD group. The 2-, 3-, and 4-year OS rates were significantly higher than those of the non-TD group (NNT < 10). As no cases were followed up for more than 5 years, the 5-year OS rate could not be calculated. The follow-up will continue in the future. This result suggests that compared with patients without TD, patients with TD have a significantly lower risk of mortality, and this protective mechanism becomes more apparent over time. One year may be an important turning point. If the patient's survival period exceeds one year, the correlation between TD occurrence and good prognosis will become more apparent. Low-T3 syndrome is an abnormality of thyroxine metabolism that involves a variety of complex physiological and pathological processes and is related to acute critical illness and chronic diseases [ 26 ]. However, its pathogenesis is still unclear. It may be related to mechanisms such as inflammation-induced energy and nutrient consumption and metabolic failure. Study has found that a variety of cytokines are involved in the occurrence of low-T3 syndrome [ 27 ], especially in critically ill patients with hematological tumors who are often complicated by infection. The expression of a series of cytokines increases, such as interleukin-6 (IL-6), interferon gamma (IFNγ), and tumor necrosis factor α (TNFα), which inhibits the synthesis of thyroid hormone (T3), leading to low-T3 syndrome. Studies have pointed out that patients with long-term malignant tumors have a negative nitrogen balance, reduced ALB (albumin) and thyroglobulin levels, and accelerated T4 clearance, which exacerbates the decline in T3 and T4 levels, manifesting as low-T3 syndrome [ 28 ]. Recent studies have found that low-T3 syndrome is associated with poor prognosis of cancer [ 29 ]. Low-T3 syndrome is also considered to be a predictive factor for poor prognosis of malignant tumors such as brain tumors [ 29 ], Hodgkin lymphoma [ 30 ], chronic lymphocytic leukemia [ 31 ], diffuse large B-cell lymphoma [ 32 ], and follicular lymphoma [ 33 ]. However, the specific mechanism is not yet clear. In this study, it was found that low-T3 syndrome was associated with poor prognosis (HR > 0, p < 0.05). To avoid the influence of confounding factors, a multivariate COX regression analysis was conducted. It was shown that after correcting for confounding factors, patients with low-T3 syndrome still had a poor prognosis. Therefore, this study speculates that the occurrence of low-T3 syndrome may be an adverse prognostic factor for patients with advanced lung cancer. However, the specific mechanism still needs to be further explored. There are still limitations to this study. Firstly, this is a retrospective study. Although potential confounding factors were corrected through multivariate Cox regression analysis, there may still be unidentified biases, such as recall bias and loss-to-follow-up-induced bias. These unidentified biases may affect the accuracy of the study's results. Secondly, the sample size is small, which may lead to insufficient statistical power and some subgroup analysis results lacking statistical significance, especially for some types of TD with lower incidence. In addition, due to the retrospective nature of the study, some patients were lost to follow-up. This may have resulted in the loss of some meaningful research findings. In addition, as the patients included in this study include patients using various types of PD-1/PD-L1 inhibitors, the heterogeneity of drugs may have a certain impact on the research results. Finally, although this study has identified some interesting trends and associations, the specific biological mechanisms behind TD and its role in improving the prognosis of advanced lung cancer patients treated with PD-1/PD-L1 inhibitors are still unclear. This requires further basic and clinical research to elucidate. Considering these limitations, a multicenter prospective study will be conducted in the future. The sample size will be expanded, more detailed and complete clinical data will be collected, and external validation cohorts and immune-inflammatory markers will be integrated to establish a prognostic prediction model for advanced lung cancer patients treated with PD-1/PD-L1 inhibitors. The innovation of this study lies not only in studying the correlation between the occurrence of TD and prognosis, but also in further analyzing the correlation between different types of TD and prognosis. In most previous studies, low-T3 syndrome was excluded from TD [ 34 ]. However, low-T3 syndrome may be a risk marker for immunotherapy failure. This is one of the key findings of this study. Conclusion When advanced lung cancer patients are treated with PD-1/PD-L1 inhibitors, the occurrence of TD may be an important predictor of good prognosis, and this positive effect becomes more significant as time goes by. Subgroup analysis further revealed the complexity of TD treatment. The treatment subgroup showed an insignificantly shorter OS compared with the non-treatment subgroup. In addition, there were differences in OS between different types of TD. Patients with overt hypothyroidism and low-T3 syndrome had a worse prognosis, while patients with subclinical hypothyroidism and subclinical hyperthyroidism had a longer OS, compared with patients with other types of TD. Therefore, TD type is important in evaluating prognosis. Abbreviations ALB Albumin ANOVA Analysis of variance CI Confidence interval CSCO Chinese Society of Clinical Oncology CTCAE5.0 Common Terminology Criteria for Adverse Events Version 5.0 fT3 Free triiodothyronine fT4 Free thyroxine HR Hazard ratio IARC International Agency for Research on Cancer ICIs Immune checkpoint inhibitors irAEs Immune-related adverse events IFNγ Interferon gamma IL-6 Interleukin-6 IQR Interquartile range mOS Median overall survival M Median NNT Number Need to Treat NSCLC Non-small-cell lung cancer ORR Objective response rate OS Overall Survival PD-1 Programmed Cell Death Protein 1 PD-L1 Programmed Death Ligand 1 PS score Physical Status score PFS Progression-free survival psPD Pseudo progression RECIST 1.1 Response Evaluation Criteria in Solid Tumors Version 1.1 RD Risk Difference SCLC Small-cell lung cancer SPSS 22.0 Statistical Product and Service Solutions Version 22.0 s Standard deviation TD Thyroid Dysfunction TPD True Progressive Disease TSH Thyroid-Stimulating Hormone TNM Tumor Node Metastasis TNFα Tumor Necrosis Factor α χ Mean Declarations Ethical approval and consent to participate This study was approved by the Ethics Committee of the First Affiliated Hospital of Shihezi University under the ethical number KJX 2022-081-01. All participants have given informed consent to participate in this study, and all participants were aware of the study's purpose, risks, and benefits. Consent for publication Not Applicable. Availability of data and materials The data and materials for this study are available from the corresponding author on reasonable request. Competing Interests The authors report no conflict of interest regarding this work. Funding statement: This study was supported by both Shihezi University Young Talents Innovation Program, CXPY202319 and Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Talent Introduction and Scientific Research Startup Project, SYKYQD10101. Authors' contributions Xiaoping Ma : Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Data curation, Validation, Writing-original draft. Yanling Wang : Methodology, Data curation, Funding Acquisition, Writing-original draft. Jing Li : Software, Methodology. Zhiyi Lin: Investigation. Ping Gong : Investigation. Jing Fei : Investigation. Min Shu : Conceptualization, Resources. Quan Tao : Conceptualization, Supervision, Resources, Project administration, Validation. Ping Dai : Supervision, Project administration, Funding Acquisition, Methodology, Validation, Writing-review & editing. All authors read and approved the final version of the manuscript. Acknowledgements We thank the First Affiliated Hospital of Shihezi University in Xinjiang for providing data support and financial support, and especially thank the scientific team from Shanghai Fourth People’s Hospital Affiliated to Tongji University for the financial support and scientific guidance. We also thank all the authors for their selfless contribution to this article. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Jan, 2026 Editor invited by journal 19 Dec, 2025 Editor assigned by journal 19 Dec, 2025 Submission checks completed at journal 19 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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13:47:19","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133581,"visible":true,"origin":"","legend":"","description":"","filename":"f18650f6c1f549d3a3b00cbb7bdd5b1e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/4b90b41fc7d0544e9f62344e.xml"},{"id":100560986,"identity":"053de45d-c3f1-420c-930a-c2d407a6d1de","added_by":"auto","created_at":"2026-01-19 08:43:54","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144838,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/c7228730615100a57893b997.html"},{"id":100595251,"identity":"5c3caab3-b522-4b63-9674-d5a1901ce4c3","added_by":"auto","created_at":"2026-01-19 13:48:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167390,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of overall survival (OS) between the TD and non-TD groups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/5778fbb45b732547d5c8218a.png"},{"id":100560912,"identity":"caed1ade-01fe-42ab-983f-efcf8bdeffff","added_by":"auto","created_at":"2026-01-19 08:43:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120648,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival (OS). RD is the risk difference, i.e., the difference in mortality rates between the TD and non-TD groups at a specific time point (If RD \u0026gt; 0, the occurrence of TD is harmful; If RD \u0026lt; 0, the occurrence of TD is beneficial). NNT is the number need to treat, i.e., the number of patients that need to be treated to prevent an additional patient from dying within a specific period of time (NNT = 1/|RD|). The smaller the NNT, the more significant the effect. If NNT = 1, there is a rare ideal effect; If NNT = 2-5, the effect is significant; If NNT = 10-20, there is a moderate effect; If NNT \u0026gt; 50, the effect is weak.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/ec0239ee7766db21e10f0638.png"},{"id":100560951,"identity":"df446626-fd71-49cb-a9f3-60a251530589","added_by":"auto","created_at":"2026-01-19 08:43:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226083,"visible":true,"origin":"","legend":"\u003cp\u003eLandmark analysis for comparing overall survival (OS) between the TD and non-TD groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/c35b01058ac8a2ad516c28cc.png"},{"id":100595230,"identity":"50c5237a-2d7b-43f7-86c2-090fc0bf725d","added_by":"auto","created_at":"2026-01-19 13:47:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":301725,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate COX regression analysis of overall survival (OS). Smoking history (1): smokers \u003cem\u003evs.\u003c/em\u003e non-smokers; Smoking history (2): smokers \u003cem\u003evs.\u003c/em\u003e non-smokers; Pathological type (1): adenocarcinoma \u003cem\u003evs.\u003c/em\u003e squamous cell carcinoma; Pathological type (2): small cell carcinoma \u003cem\u003evs.\u003c/em\u003e squamous cell carcinoma.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/8be02fabbd7e2c899e39a18c.png"},{"id":100594775,"identity":"3899ad9b-b331-499c-815e-b96e41974e10","added_by":"auto","created_at":"2026-01-19 13:44:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103173,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate COX regression analysis of overall survival (OS).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/998dc2b180aac6bd5d53767f.png"},{"id":100560904,"identity":"ec0cfdfc-c8fc-4899-91c4-33c70aa2dcb8","added_by":"auto","created_at":"2026-01-19 08:43:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135515,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of overall survival (OS) between the treatment and non-treatment subgroups.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/ab7c0627705389e5cd8bdd12.png"},{"id":100560874,"identity":"0d467ee1-946e-48f0-90af-a74fef64ce8d","added_by":"auto","created_at":"2026-01-19 08:43:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":185544,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of overall survival (OS) between different types of thyroid dysfunction (TD) and the non-TD group.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/69f79906433312aa13999bce.png"},{"id":100561132,"identity":"68ca2d0e-e727-4e53-b4c9-4912db52cb0b","added_by":"auto","created_at":"2026-01-19 08:43:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":184290,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Cox regression analysis of overall survival (OS) of patients with different types of thyroid dysfunction (TD) (compared with the non-TD group).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/3848a84382a0d98dd72d819a.png"},{"id":100595309,"identity":"95a2f5c4-65fa-4e6d-b188-269b6e9c8f62","added_by":"auto","created_at":"2026-01-19 13:48:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":179821,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the overall survival (OS) of patients with different types of thyroid dysfunction (TD).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/7d363d75a350a0548e4692f5.png"},{"id":100560852,"identity":"5236f8b9-cecf-46ad-89c7-b1e067b7603d","added_by":"auto","created_at":"2026-01-19 08:43:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":313683,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate COX regression analysis of the overall survival (OS) of patients with different types of thyroid dysfunction (TD). TD type (1): subclinical hypothyroidism \u003cem\u003evs.\u003c/em\u003elow-T3 syndrome; TD type (2): hypothyroidism \u003cem\u003evs.\u003c/em\u003e low-T3 syndrome; TD type (3): subclinical hyperthyroidism \u003cem\u003evs.\u003c/em\u003e low-T3 syndrome; TD type (4): hyperthyroidism \u003cem\u003evs.\u003c/em\u003e low-T3 syndrome; Smoking history (1): smokers \u003cem\u003evs.\u003c/em\u003enon-smokers; Smoking history (2): smokers \u003cem\u003evs.\u003c/em\u003e non-smokers; Pathological type (1): adenocarcinoma \u003cem\u003evs.\u003c/em\u003e squamous cell carcinoma; Pathological type (2): small cell carcinoma \u003cem\u003evs.\u003c/em\u003e squamous cell carcinoma.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/ea1766781321fe8f09901c2e.png"},{"id":100594730,"identity":"2070cd9d-7eaa-4b7e-ab2e-13711d6fcfb9","added_by":"auto","created_at":"2026-01-19 13:44:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":127029,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate COX regression analysis of the overall survival (OS) of patients with different types of thyroid dysfunction (TD). TD type (1): subclinical hypothyroidism \u003cem\u003evs.\u003c/em\u003e low-T3 syndrome; TD type (2): hypothyroidism \u003cem\u003evs.\u003c/em\u003e low-T3 syndrome; TD type (3): subclinical hyperthyroidism \u003cem\u003evs.\u003c/em\u003e low-T3 syndrome; TD type (4): hyperthyroidism \u003cem\u003evs.\u003c/em\u003elow-T3 syndrome.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/3370c526af185a4b8d23b7cf.png"},{"id":100949274,"identity":"29fc919a-eeb8-4e8f-92d8-0d31aad92620","added_by":"auto","created_at":"2026-01-23 06:56:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3130290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8395613/v1/5cb946bc-0730-4770-a945-bcb712575293.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Thyroid Dysfunction as a Prognostic Indicator for Overall Survival in Advanced Lung Cancer Patients Treated with PD-1/PD-L1 Inhibitors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the latest data released by the International Agency for Research on Cancer (IARC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death in most countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More than half of lung cancer patients are already in the intermediate and advanced stages when diagnosed, losing the chance of radical surgery [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The 5-year overall survival (OS) rate is extremely low (less than 5%) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The emergence of immune checkpoint inhibitors (ICIs) has brought new hope to advanced lung cancer patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProgrammed Cell Death Protein 1 (PD-1)/Programmed Death Ligand 1 (PD-L1) inhibitors are the most common ICIs and have been widely used in the treatment of advanced lung cancer [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, many studies have found that PD-1/PD-L1 inhibitor treatment can lead to unique immune-related adverse events (irAEs) in multiple systems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thyroid dysfunction (TD) is one of the common irAEs, accounting for approximately 6\u0026ndash;8% of all irAEs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to relevant research [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], about 20% of advanced lung cancer patients developed TD when treated with PD-1/PD-L1 inhibitors. However, in fact, the incidence of TD can reach up to 40%-50%.\u003c/p\u003e \u003cp\u003eInterestingly, many studies have found that the occurrence of TD seems to be associated with better efficacy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This was also confirmed in our previous study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This may be because patients who develop TD have better activation of immune responses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our previous study only observed PFS (progression-free survival). Although in most cases the longer the PFS, the longer the OS, there are also unrelated or even opposite cases. Therefore, PFS can only be used as a surrogate endpoint for OS. Pseudo progression (psPD) is a rare phenomenon that occurs when ICIs are used for the therapy of melanoma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and non-small cell lung cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], in which the tumor first increases and then shrinks. It suggests the delayed anti-tumor effect of immunotherapy. However, it is generally difficult to identify true progressive disease (TPD) in clinical practice. This leads to a certain difference between PFS and OS, failing to fully capture the clinical benefits of PD-1/PD-L1 inhibitors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, it is very necessary to further study OS when the results of PFS have been obtained. This study continues to follow up the patients in the previous study until the patient dies, to more intuitively evaluate whether there is a correlation between TD occurrence and OS. This study puts forward the hypothesis that the occurrence of TD is related to the prolongation of OS. The study results will provide certain guidance for clinicians to make decisions. This is an exploratory study. The sample size is based on our previous study and case availability.\u003c/p\u003e \u003cp\u003eNotably, the prognosis may also be different among different types of TD, but no conclusion has yet been reached on this matter. Muir et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] proposed that overt thyrotoxicosis might be a surrogate marker of a robust immune response to ICI treatment. No improvement in survival was observed for other thyroid irAE subtypes. However, Beak et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] found that patients with new-onset overt hypothyroidism had a significantly lower hazard ratio for mortality than the thyrotoxicosis group. Patients who had already taken thyroid hormones to treat hypothyroidism had a better prognosis. Therefore, they believed that the pathogenesis of different types of TD was consistent, and thyrotoxicosis was transient and eventually transformed into hypothyroidism [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The stimulating effect of TSH (thyroid-stimulating hormone) may be related to the improvement of prognosis, but the mechanism needs to be further explored. Therefore, whether there are differences in prognosis between different types of TD is still controversial. More clinical data is needed to clarify it.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research subjects and inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThis study is based on an extended follow-up of our previous research cohort [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This design reduces the influence of confounding factors and ensures the consistency of data. The research subjects were 60 advanced lung cancer patients who received initial treatment at the First Affiliated Hospital of Shihezi University and the People's Hospital of Shihezi City from January 2019 to August 2024. All patients received at least 2 cycles of PD-1/PD-L1 inhibitor treatment combined with chemotherapy (some patients also received bevacizumab treatment), including 21 cases of squamous-cell carcinoma, 17 cases of adenocarcinoma, and 22 cases of small-cell carcinoma. All patients were treated in accordance with the Clinical Practice Guidelines for Malignant Lung Tumors of the Chinese Society of Clinical Oncology (CSCO). Among them, 42 patients were treated with PD-1 inhibitors and 18 patients were treated with PD-L1 inhibitors. This study has been approved by the Ethics Committee of the First Affiliated Hospital of Shihezi University, with ethics number KJX2022-081-01. All patients or their families (when applicable) were informed and signed informed consent forms, agreeing to use their clinical data for this study. This study complies with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: (1) The patient was over 18 years old; (2) The patient was confirmed to have advanced lung cancer by pathological tissue, cytology, and imaging (tumor node metastasis (TNM) staging: stage 3 (non-surgical) or 4); (3) The patient was newly diagnosed and had not undergone any anti-tumor treatment; (4) The patient had received standardized PD-1/PD-L1 inhibitor-based combination therapy or other regimens for at least 2 cycles; (5) The patient with a physical status (PS) score greater than or equal to 2; (6) The patient's survival period exceeds 3 months.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: (1) Before treatment, there was TD, concomitant thyroid-related diseases, thyroid surgery, radiotherapy for thyroid, or autoimmune disease; (2) The patient had toxic goiter-induced TD; (3) The patient had concomitant malignant tumors or had previously suffered from other malignant tumors and undergone anti-tumor treatment; (4) The patient also had severe heart failure, liver and kidney dysfunction, diseases of hematopoietic system, etc.; (5) Clinical evidence of chronic inflammatory disease, severe acute infection or inflammation before treatment and/or during efficacy evaluation; (6) Patients who were lost to follow-up or who\u0026rsquo;s complete clinical data was unavailable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical observation indicators\u003c/h2\u003e \u003cp\u003eThe information of the patient's thyroid function (thyroid-stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4)) was collected 1\u0026ndash;2 days before treatment. Subsequent follow-up was performed every 2 cycles of treatment. Data were collected until the patient died, lost to follow-up, or the cut-off date (February 2025).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Thyroid function evaluation\u003c/h2\u003e \u003cp\u003eThyroid function was evaluated as follows: TD was defined by one or more consecutive abnormal TSH values, regardless of fT4 and fT3 levels (those with reduced fT3 only were also recorded as TD in this study). Hypothyroidism: elevated TSH and reduced fT4 and/or fT3 levels. Subclinical hypothyroidism: only elevated TSH. Hyperthyroidism: reduced TSH and elevated fT4 and/or fT3 levels. Subclinical hyperthyroidism: Only reduced TSH. Low-T3 syndrome: only reduced fT3 levels, with normal TSH and fT4 levels.\u003c/p\u003e \u003cp\u003eThe indicator ranges of normal thyroid function in this study were as follows: TSH: 0.27\u0026ndash;4.2 mIU/L; fT3: 3.1\u0026ndash;6.8 pmol/L; fT4: 12.0\u0026ndash;22.0 pmol/L. Patients received replacement therapy with levothyroxine when the TSH value was larger than 10 mIU/L. The TD was graded with reference to the Common Terminology Criteria for Adverse Events (CTCAE5.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcome indicators\u003c/h2\u003e \u003cp\u003ePrimary outcome indicator: OS, i.e., the time from the start of treatment to death.\u003c/p\u003e \u003cp\u003eSecondary outcome indicators were as follows: 1-, 2-, 3-, and 4-year OS rate, i.e., the ratio of the number of patients who survived 1, 2, 3, and 4 years of follow-up to the total number of cases, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Grouping and analysis\u003c/h2\u003e \u003cp\u003eThe cases were divided into TD and non-TD groups based on whether TD occurred. The differences in OS between the two groups were analyzed.\u003c/p\u003e \u003cp\u003eAccording to the level of thyroid function, TD was divided into subclinical hypothyroidism, hypothyroidism, subclinical hyperthyroidism, hyperthyroidism, and low-T3 syndrome. The differences in OS between different types of TD were analyzed. The cases with TD were divided into a treatment subgroup and a non-treatment subgroup based on whether the patient received relevant treatment. The differences in OS between the two subgroups were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 26.0 software was used for statistical processing. Normally distributed variable data were represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s). The t-test was conducted for comparison between the two groups. ANOVA was conducted for multiple group comparisons. Median and interquartile range (M, IQR) were used for non-normally distributed continuous variables. The nonparametric test was conducted for comparison between groups. Counting data was presented in terms of the number of cases and the rate (%). The Pearson \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test was used for inter-group comparison. The Wilcoxon rank sum test was used for comparison of rank data. The Kaplan-Meier method was used for survival analysis. The Log-rank test was used for inter-group comparison of survival. Graphpad Prism (10.1.2) was used to plot survival curves. Multivariate Cox regression analysis was performed on factors with statistical significance in univariate Cox regression analysis to evaluate the hazard ratio (HR) and 95% confidence interval (CI). R software was used for landmark analysis to avoid false-positive associations caused by incomplete follow-up. Differences were considered statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparison of general conditions between the TD and non-TD groups\u003c/h2\u003e \u003cp\u003eThyroid dysfunction occurred in 29 of 60 advanced lung cancer patients treated with PD-1/PD-L1 inhibitors.\u003c/p\u003e \u003cp\u003eThere were no significant differences between the TD and non-TD groups in terms of gender, age, \u0026zwnj;body mass index\u0026zwnj; (BMI), smoking history, clinical stage, pathological type, tumor type, PS score, distant metastasis, combination treatment regimen, and PD-1/PD-L1 inhibitor type. Thus, they were comparable (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of general conditions between the TD and non-TD groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeneral information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTD group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-TD group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;31\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (87.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.35\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5 (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;X\u0026thinsp;\u0026le;\u0026thinsp;23.9 (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026le;\u0026thinsp;X\u0026thinsp;\u0026le;\u0026thinsp;27.9 (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28.0 (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor type (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS score (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination therapy (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u0026thinsp;+\u0026thinsp;bevacizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of PD-1/PD-L1 inhibitors (n (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of OS between the TD and non-TD groups\u003c/h2\u003e \u003cp\u003eThe median follow-up time in this study was 17.65 months, and the longest follow-up time was 57.43 months (survivors, TD group). During the follow-up, 20 and 24 cases in the TD (20/29, 69%) and non-TD (24/31, 77%) groups died, respectively. The median OS (mOS) of the TD group was longer than that of the non-TD group (19.77 \u003cem\u003evs.\u003c/em\u003e 15.9 months), accompanied by a lower risk of mortality (HR: 0.751, 95% CI (0.414\u0026ndash;1.362), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.345) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 1-year OS rate of the TD group was numerically lower than that of the non-TD group (66% \u003cem\u003evs.\u003c/em\u003e 68%, RD (risk difference)\u0026thinsp;=\u0026thinsp;0.02, NNT (number need to treat)\u0026thinsp;=\u0026thinsp;50). However, the 2- (45% \u003cem\u003evs.\u003c/em\u003e 24%, RD = -0.21, NNT\u0026thinsp;=\u0026thinsp;5), 3- (30% \u003cem\u003evs.\u003c/em\u003e12%, RD = -0.18, NNT\u0026thinsp;=\u0026thinsp;6), and 4-year (30% \u003cem\u003evs.\u003c/em\u003e 12%, RD = -0.18, NNT\u0026thinsp;=\u0026thinsp;6) OS rates were significantly higher than those of the non-TD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLandmark analysis for avoiding false-positive associations caused by incomplete follow-up showed that with 12 months as the cutoff point, the difference in OS between the TD and non-TD groups increased after 12 months (\u003cem\u003eHR\u003c/em\u003e: 0.567 (\u0026lt;\u0026thinsp;0.751), 95% \u003cem\u003eCI [0.25\u0026ndash;1.29])\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.169 (\u0026lt;\u0026thinsp;0.345)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of confounding factors affecting overall survival\u003c/h2\u003e \u003cp\u003eThe results of univariate COX regression analysis showed that age, PS score, and liver metastasis were possible influencing factors of OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). After incorporating the above single factors into the multivariate COX regression model, it was found that PS score was an independent risk factor for mortality in advanced lung cancer patients (\u003cem\u003eHR\u003c/em\u003e: 2.453, 95% \u003cem\u003eCI [1.228\u0026ndash;4.899]\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). In addition, liver metastasis was also an independent risk factor for mortality (\u003cem\u003eHR\u003c/em\u003e: 2.256, 95% \u003cem\u003eCI [1.038\u0026ndash;6.314]\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Comparison of overall survival between the treated and non-treated patients with thyroid dysfunction\u003c/h2\u003e \u003cp\u003eDuring the follow-up process, 5 patients developed grade 2 TD (all developed hypothyroid) and underwent replacement therapy with levothyroxine sodium tablets. The comparison of OS between the treatment and non-treatment subgroups showed that the mOS of the treatment subgroup was shorter than that of the non-treatment subgroup (16.27 \u003cem\u003evs.\u003c/em\u003e 20.85 months), accompanied by a numerically higher risk of mortality (\u003cem\u003eHR\u003c/em\u003e: 1.679, 95% \u003cem\u003eCI (0.468\u0026ndash;6.013)\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.426) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Comparison of overall survival among different types of TD\u003c/h2\u003e \u003cp\u003eWhen PD-1/PD-L1 inhibitors were used to treat advanced lung cancer patients, there was a difference in OS between different types of TD and the non-TD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The mOS of patients with subclinical hypothyroidism (42.78 months), subclinical hyperthyroidism (not reached), and hyperthyroidism (16.27 months) was longer than that of patients who did not experience TD (15.9 months). The three were protective factors for mortality (\u003cem\u003eHR\u003c/em\u003e: 0.440, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.074; \u003cem\u003eHR\u003c/em\u003e: 0.227, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148, \u003cem\u003eHR\u003c/em\u003e: 0.972, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.959), but the differences were not statistically significant. Patients with low-T3 syndrome had significantly shorter OS than those without TD (5.4 \u003cem\u003evs.\u003c/em\u003e 15.9 months). Thus, low-T3 syndrome was a risk factor for mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although overt hypothyroidism was a risk factor for mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1), the difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of overall survival (OS) between different types of thyroid dysfunction (TD) and the non-TD group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of TD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emOS (month)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e for OS (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-TD group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubclinical hypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.440 (0.178\u0026ndash;1.084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.629 (0.729\u0026ndash;3.640)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubclinical hyperthyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227 (0.030\u0026ndash;1.690)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperthyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972 (0.335\u0026ndash;2.820)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-T3 syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.617(1.805-171.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e The results of the multivariate Cox regression analysis showed that after excluding confounding factors (TD type as well as the influencing factors for OS obtained above (age, PS score, and liver metastasis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)), patients with low-T3 syndrome had a significantly higher risk of mortality than the non-TD group (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.632, 95% \u003cem\u003eCI (1.161-116.594)\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). In addition, PS score was an independent risk factor for mortality (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.460, 95% \u003cem\u003eCI (1.180\u0026ndash;5.129)\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere were differences in OS among different types of TD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mOS of patients with overt hypothyroidism and low-T3 syndrome was significantly shorter than that of patients with other types of TD (11.92 (overt hypothyroidism) \u003cem\u003evs.\u003c/em\u003e 28.13 (other types of TD except for overt hypothyroidism) months; 5.4 (low-T3 syndrome) \u003cem\u003evs.\u003c/em\u003e 20.85 (other types of TD except for low-T3 syndrome) months). Thus, hypothyroidism and low-T3 syndrome were risk factors for mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although overt hyperthyroidism was a risk factor for mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1), the difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mOS of patients with subclinical hypothyroidism and subclinical hyperthyroidism was longer than that of patients with other types of TD (42.78 (subclinical hypothyroidism) \u003cem\u003evs.\u003c/em\u003e 16.27 months (other types of TD except for subclinical hypothyroidism); not reached (subclinical hyperthyroidism) \u003cem\u003evs.\u003c/em\u003e 17.04 (other types of TD except for subclinical hyperthyroidism). Thus, subclinical hypothyroidism and subclinical hyperthyroidism were protective factors for mortality (HR: 0.474, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.097; HR: 0.426, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.159), but the difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the overall survival (OS) of patients with different types of thyroid dysfunction (TD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of TD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emOS (month)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e for OS (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubclinical hypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474 (0.196\u0026ndash;1.145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.672 (1.208\u0026ndash;11.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubclinical hyperthyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.426 (0.130\u0026ndash;1.398)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperthyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.515 (0.437\u0026ndash;5.253)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-T3 syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: NA, not applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOX regression analysis was only conducted on patients with TD to avoid the influence of confounding factors. According to univariate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), TD type, PS score, and liver metastasis were influencing factors for OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.1). Then, these factors were included in the multifactorial Cox regression model. The results showed that after excluding confounding factors, the risk of mortality in patients with subclinical hypothyroidism and subclinical hyperthyroidism was significantly lower than that of patients with low-T3 syndrome (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, 95% \u003cem\u003eCI (0.220\u0026ndash;0690)\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027); \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, 95% \u003cem\u003eCI (0.000-0.432)\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). In addition, PS score was an independent risk factor for mortality (HR\u0026thinsp;=\u0026thinsp;4.521, 95% CI (1.537\u0026ndash;13.302), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe relationship between the occurrence of TD and patient prognosis is currently a hot and controversial research topic. Zhou et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Chmielewska et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found that patients with TD had significantly longer OS and PFS (HR\u0026thinsp;\u0026lt;\u0026thinsp;0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than patients without TD. However, Xie et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] found that patients with TD had a significantly higher ORR (objective response rate) (44.4% \u003cem\u003evs.\u003c/em\u003e 16.4%), but there was no difference in PFS and OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Against this backdrop, this study was conducted. It was found that there was a complex relationship between the occurrence of TD and patient prognosis. In our previous research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the PFS of the TD group was significantly longer than that of the non-TD group (mPFS: 8.83 \u003cem\u003evs.\u003c/em\u003e 6.50 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041). However, in this study, although there was a difference in mOS between the TD and non-TD groups, i.e., the TD group had a longer OS (19.77 \u003cem\u003evs.\u003c/em\u003e 15.9 months), this difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.345). The longer the PFS, the longer the OS, but there are also inconsistencies, i.e., in some large-scale studies. The survival follow-up results of the study JO25567 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] showed that the combination of bevacizumab with erlotinib was significantly associated with an enhancement in PFS (mPFS: 16.4 \u003cem\u003evs.\u003c/em\u003e 9.8 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005), but there was no significant enhancement in OS (mOS: 47.0 \u003cem\u003evs.\u003c/em\u003e 47.4 months). This inconsistency may be related to multiple factors, including but not limited to disease staging, patient physical condition, comorbidities, and subsequent treatment. Although the influences of these factors were adjusted in this study, there may still be unidentified confounding factors. Secondly, this may be related to the small sample size and the resulting insufficient statistical power in this study. In future research, the sample size will be further expanded to more accurately evaluate the impact of TD on overall survival in advanced lung cancer patients. However, upon further analysis of the annual OS rate, it was found that the downward trend in OS rate for patients with TD over time was significantly slower than that of the non-TD group. The 2-, 3-, and 4-year OS rates were significantly higher than those of the non-TD group (NNT\u0026thinsp;\u0026lt;\u0026thinsp;10). As no cases were followed up for more than 5 years, the 5-year OS rate could not be calculated. The follow-up will continue in the future. This result suggests that compared with patients without TD, patients with TD have a significantly lower risk of mortality, and this protective mechanism becomes more apparent over time. One year may be an important turning point. If the patient's survival period exceeds one year, the correlation between TD occurrence and good prognosis will become more apparent.\u003c/p\u003e \u003cp\u003eLow-T3 syndrome is an abnormality of thyroxine metabolism that involves a variety of complex physiological and pathological processes and is related to acute critical illness and chronic diseases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, its pathogenesis is still unclear. It may be related to mechanisms such as inflammation-induced energy and nutrient consumption and metabolic failure. Study has found that a variety of cytokines are involved in the occurrence of low-T3 syndrome [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], especially in critically ill patients with hematological tumors who are often complicated by infection. The expression of a series of cytokines increases, such as interleukin-6 (IL-6), interferon gamma (IFNγ), and tumor necrosis factor α (TNFα), which inhibits the synthesis of thyroid hormone (T3), leading to low-T3 syndrome. Studies have pointed out that patients with long-term malignant tumors have a negative nitrogen balance, reduced ALB (albumin) and thyroglobulin levels, and accelerated T4 clearance, which exacerbates the decline in T3 and T4 levels, manifesting as low-T3 syndrome [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Recent studies have found that low-T3 syndrome is associated with poor prognosis of cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Low-T3 syndrome is also considered to be a predictive factor for poor prognosis of malignant tumors such as brain tumors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], Hodgkin lymphoma [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], chronic lymphocytic leukemia [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], diffuse large B-cell lymphoma [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and follicular lymphoma [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the specific mechanism is not yet clear. In this study, it was found that low-T3 syndrome was associated with poor prognosis (HR\u0026thinsp;\u0026gt;\u0026thinsp;0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To avoid the influence of confounding factors, a multivariate COX regression analysis was conducted. It was shown that after correcting for confounding factors, patients with low-T3 syndrome still had a poor prognosis. Therefore, this study speculates that the occurrence of low-T3 syndrome may be an adverse prognostic factor for patients with advanced lung cancer. However, the specific mechanism still needs to be further explored.\u003c/p\u003e \u003cp\u003eThere are still limitations to this study. Firstly, this is a retrospective study. Although potential confounding factors were corrected through multivariate Cox regression analysis, there may still be unidentified biases, such as recall bias and loss-to-follow-up-induced bias. These unidentified biases may affect the accuracy of the study's results. Secondly, the sample size is small, which may lead to insufficient statistical power and some subgroup analysis results lacking statistical significance, especially for some types of TD with lower incidence. In addition, due to the retrospective nature of the study, some patients were lost to follow-up. This may have resulted in the loss of some meaningful research findings. In addition, as the patients included in this study include patients using various types of PD-1/PD-L1 inhibitors, the heterogeneity of drugs may have a certain impact on the research results. Finally, although this study has identified some interesting trends and associations, the specific biological mechanisms behind TD and its role in improving the prognosis of advanced lung cancer patients treated with PD-1/PD-L1 inhibitors are still unclear. This requires further basic and clinical research to elucidate. Considering these limitations, a multicenter prospective study will be conducted in the future. The sample size will be expanded, more detailed and complete clinical data will be collected, and external validation cohorts and immune-inflammatory markers will be integrated to establish a prognostic prediction model for advanced lung cancer patients treated with PD-1/PD-L1 inhibitors. The innovation of this study lies not only in studying the correlation between the occurrence of TD and prognosis, but also in further analyzing the correlation between different types of TD and prognosis. In most previous studies, low-T3 syndrome was excluded from TD [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, low-T3 syndrome may be a risk marker for immunotherapy failure. This is one of the key findings of this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhen advanced lung cancer patients are treated with PD-1/PD-L1 inhibitors, the occurrence of TD may be an important predictor of good prognosis, and this positive effect becomes more significant as time goes by. Subgroup analysis further revealed the complexity of TD treatment. The treatment subgroup showed an insignificantly shorter OS compared with the non-treatment subgroup. In addition, there were differences in OS between different types of TD. Patients with overt hypothyroidism and low-T3 syndrome had a worse prognosis, while patients with subclinical hypothyroidism and subclinical hyperthyroidism had a longer OS, compared with patients with other types of TD. Therefore, TD type is important in evaluating prognosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eAnalysis of variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eCSCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eChinese Society of\u0026nbsp;Clinical\u0026nbsp;Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eCTCAE5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eCommon Terminology Criteria for Adverse Events Version 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003efT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eFree triiodothyronine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003efT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eFree thyroxine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eInternational Agency for Research on Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eICIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eImmune checkpoint inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eirAEs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eImmune-related adverse events\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIFN\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eInterferon gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eInterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003emOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eMedian overall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eNumber Need to Treat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eNon-small-cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eObjective response rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003ePD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eProgrammed Cell Death Protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003ePD-L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eProgrammed Death Ligand 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003ePS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003ePhysical Status score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eProgression-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epsPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePseudo progression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRECIST 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResponse Evaluation Criteria in Solid Tumors Version 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmall-cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSPSS 22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical Product and Service Solutions Version 22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003es\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThyroid Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrue Progressive Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThyroid-Stimulating Hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor Node Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNF\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor Necrosis Factor\u0026nbsp;\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study was approved by the Ethics Committee of the First Affiliated Hospital of Shihezi University under the ethical number KJX 2022-081-01. All participants have given informed consent to participate in this study, and all participants were aware of the study\u0026apos;s purpose, risks, and benefits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials for this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflict of interest regarding this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e This study was supported by both Shihezi University Young Talents Innovation Program, CXPY202319 and Shanghai Fourth People\u0026apos;s Hospital, School of Medicine, Tongji University, Talent Introduction and Scientific Research Startup Project, SYKYQD10101.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaoping Ma\u003c/strong\u003e: Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Data curation, Validation, Writing-original draft. \u003cstrong\u003eYanling Wang\u003c/strong\u003e: Methodology, Data curation, Funding Acquisition, Writing-original draft. \u003cstrong\u003eJing Li\u003c/strong\u003e: Software, Methodology.\u003cstrong\u003e\u0026nbsp;Zhiyi Lin:\u0026nbsp;\u003c/strong\u003eInvestigation. \u003cstrong\u003ePing Gong\u003c/strong\u003e: Investigation. \u003cstrong\u003eJing Fei\u003c/strong\u003e: Investigation. \u003cstrong\u003eMin Shu\u003c/strong\u003e: Conceptualization, Resources. \u003cstrong\u003eQuan Tao\u003c/strong\u003e: Conceptualization, Supervision, Resources, Project administration, Validation. \u003cstrong\u003ePing Dai\u003c/strong\u003e: Supervision, Project administration, Funding Acquisition, Methodology, Validation, Writing-review \u0026amp; editing. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the First Affiliated Hospital of Shihezi University in Xinjiang for providing data support and financial support, and especially thank the scientific team from Shanghai Fourth People\u0026rsquo;s Hospital Affiliated to Tongji University for the financial support and scientific guidance. We also thank all the authors for their selfless contribution to this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao Y, Yang D, He J, Krasna MJ. 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Pediatr Hematol Oncol. 2001;18(3):211\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao R, Chen RZ, Xia Y, Liang JH, Wang L, Zhu HY, Zhu Wu J, Fan L, Li JY, Yang T, et al. Low T3 syndrome as a predictor of poor prognosis in chronic lymphocytic leukemia. Int J Cancer. 2018;143(3):466\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao R, Liang JH, Wang L, Zhu HY, Wu W, Wu JZ, Xia Y, Cao L, Fan L, Yang T, et al. Low T3 syndrome is a strong prognostic predictor in diffuse large B cell lymphoma. Br J Haematol. 2017;177(1):95\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue LG, Shen HR, Gao R, Du KX, Xing TY, Wang WT, Wang L, Li JY, Liang JH, Xu W. Low T3 syndrome as a predictor of poor outcomes in patients with follicular lymphoma. Ann Hematol. 2023;102(4):851\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThuillier P, Joly C, Alavi Z, Crouzeix G, Descourt R, Quere G, Kerlan V, Roudaut N. Thyroid dysfunction induced by immune checkpoint inhibitors is associated with a better progression-free survival and overall survival in non-small cell lung cancer: an original cohort study. Cancer Immunol Immunother. 2021;70(7):2023\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thyroid dysfunction, PD-1/PD-L1 inhibitors, lung cancer, overall survival","lastPublishedDoi":"10.21203/rs.3.rs-8395613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to explore the correlation between the occurrence of thyroid dysfunction (TD) and the overall survival (OS) of advanced lung cancer patients treated with PD-1/PD-L1 inhibitors, providing a basis for personalized diagnosis and treatment.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe data of 60 patients who were initially diagnosed with advanced lung cancer at two hospitals in Shihezi, Xinjiang, China from January 2019 to August 2024 were retrospectively collected. The baseline thyroid function was normal. The patients were divided into TD and non-TD groups based on TD occurrence to analyze the correlation between TD occurrence and prognosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median OS was longer in the TD group than in the non-TD group (19.77 \u003cem\u003evs.\u003c/em\u003e 15.9 months), accompanied by a numerically lower risk of mortality (HR: 0.751, 95% CI (0.414\u0026ndash;1.362), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.345). The 1-year OS rate of the TD group was numerically lower than that of the non-TD group (66% \u003cem\u003evs.\u003c/em\u003e 68%, RD (risk difference)\u0026thinsp;=\u0026thinsp;0.02, NNT (number needed to treat)\u0026thinsp;=\u0026thinsp;50). However, the 2- (45% \u003cem\u003evs.\u003c/em\u003e 24%, RD = -0.21, NNT\u0026thinsp;=\u0026thinsp;5), 3- (30% \u003cem\u003evs.\u003c/em\u003e 12%, RD = -0.18, NNT\u0026thinsp;=\u0026thinsp;6), and 4-year OS rates (30% \u003cem\u003evs.\u003c/em\u003e 12%, RD = -0.18, NNT\u0026thinsp;=\u0026thinsp;6) were significantly higher than those of the non-TD group. There were differences in OS between different types of TD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The patients with low-T3 syndrome had a significantly shorter OS compared with the non-TD group (5.4 \u003cem\u003evs.\u003c/em\u003e 15.9 months); thus, it was a risk factor for mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with patients with other types of TD, patients with overt hypothyroidism and low-T3 syndrome had significantly shorter OS (11.92 \u003cem\u003evs.\u003c/em\u003e 28.13 months, 5.4 \u003cem\u003evs.\u003c/em\u003e 20.85 months), and they were also risk factors for mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe occurrence of TD is positively correlated with the good prognosis of advanced lung cancer patients treated with PD-1/PD-L1 inhibitors, and the correlation becomes increasingly significant over time. There are also some differences in prognosis between different types of TD.\u003c/p\u003e","manuscriptTitle":"Thyroid Dysfunction as a Prognostic Indicator for Overall Survival in Advanced Lung Cancer Patients Treated with PD-1/PD-L1 Inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:28:23","doi":"10.21203/rs.3.rs-8395613/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-13T12:21:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-19T16:35:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-19T05:55:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-19T05:53:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-18T12:29:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e0b1773-4011-4b49-b0cf-8f7ab16b6146","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T08:28:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:28:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8395613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8395613","identity":"rs-8395613","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}