Small cell lung cancer (SCLC) exhibits a high incidence of perineural invasion (PNI), a clinical feature associated with poor prognosis. Here, we establish PNI as an independent adverse prognostic factor in a surgical SCLC cohort. We further show that the neural microenvironment upregulates stathmin-2 (STMN2) in SCLC cells. STMN2, in a concentration-dependent manner, activates the β-alanine metabolic pathway, leading to intracellular β-alanine accumulation, which enhances tumor cell migration and invasion. In vivo, STMN2 knockdown suppresses neural invasion, an effect reversible upon β-alanine supplementation. This work defines a neural-STMN2-β-alanine-invasion axis that drives PNI in SCLC, providing mechanistic insights and highlighting a promising metabolic vulnerability for therapeutic intervention.

Immune checkpoint blockade elicits durable responses in a subset of patients with gastric cancer, yet the cellular programs underlying therapeutic divergence remain unclear. Using integrative single-cell transcriptomics of tumors from Immune Checkpoint Inhibitor (ICI)-treated patients, we resolved the CD8 + T-cell landscape associated with response. Therapeutic outcome reflected not only differences in state abundance but also functional reprogramming within shared states. Trajectory analysis revealed bifurcation of naïve CD8 + T cells into effector and exhaustion-prone branches that were differentially enriched between responders and non-responders. Inference of transcription factor activity revealed lineage-specific modules associated with these divergent fates. Further modeling of ligand-receptor pairs uncovered how signaling between myeloid and T cells changes during different responses. Together, these findings delineate a regulatory and intercellular framework characterizing CD8 + T-cell differentiation in gastric cancer and illuminate mechanisms of immune-state divergence during immunotherapy.

Targeting glucose metabolism has long been pursued as an anticancer strategy, yet its clinical translation remains challenging. Achieving therapeutic selectivity requires identifying actionable metabolic distinctions between different malignant traits. Here, we uncover a noncanonical, lactate-independent glucose metabolic pathway facilitated by the glucose transporter 6 (GLUT6), which confers targeted therapy resistance in lung cancer. Downstream, GLUT6 promotes glucose influx and diversion toward methylglyoxal production, leading to kelch-like ECH-associated protein 1 (KEAP1) dimerization and nuclear factor erythroid 2-related factor 2 (NRF2) pathway activation, driving resistance. Upstream, GLUT6 expression is transcriptionally upregulated by therapy-induced MYC associated zinc finger protein (MAZ) activation. Targeting GLUT6 prevents and overcomes EGFR and KRAS inhibitors resistance. Moreover, the MAZ-GLUT6-NRF2 axis correlates with clinical treatment response and relapse. The preferential reliance on GLUT6-a noncanonical transporter with minimal systemic homeostasis perturbation-highlights its promise as a target for overcoming resistance and revitalizing glucose metabolism-based anticancer strategies.

e20726 Background: At present, third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are the standard first-line treatment for advanced EGFR-mutant non-small cell lung cancer (NSCLC). Nevertheless, the combination of first-/second-generation EGFR-TKIs and chemotherapy or anti-angiogenic agents remains commonly used in real-world practice in China. This study aimed to compare the real-world effectiveness of these two first-line strategies. Methods: In this retrospective study, data were collected from Shanghai Chest Hospital from January 2017 to December 2023. Previously-untreated patients with stage III/IV EGFR-mutant NSCLC were enrolled and classified into two groups: those receiving first-line first-/second-generation EGFR-TKI combination therapy, and those receiving third-generation EGFR-TKI monotherapy. Propensity-score matching (PSM) (1:2 ratio) was performed to balance baseline characteristics. The primary endpoint was progression-free survival (PFS). Overall survival (OS) was a secondary endpoint. Results: A total of 512 eligible patients were enrolled and the median follow-up was 40.4 months. After PSM, 364 patients were included. The median PFS was significantly longer in the third-generation TKI monotherapy group (n = 130, PFS: 20.5 months; 95% confidence interval [CI], 16.45-24.55) compared to the first-/second-generation TKI combination group (n = 234, median PFS: 16.0 months; 95% CI, 14.16-17.91; P < 0.001). Subgroup analysis of PFS consistently favored the third-generation EGFR-TKI across almost all variables. No significant difference in OS was observed between the two groups (46.1 months, 95%CI 39.64-52.49 vs. 41.6 months, 95%CI 38.51-45.14; P = 0.721). Besides, 165 (70.5%) patients in the combination group received third-generation EGFR-TKIs in later lines, and this subgroup demonstrated significantly longer OS than those who did not (45.4 vs 33.5 months, P = 0.017). Conclusions: This real-world study demonstrates that first-line third-generation EGFR-TKI monotherapy is associated with a significantly improved PFS compared to first-/second-generation EGFR-TKI combination therapy in EGFR-mutant NSCLC. The absence of OS difference may be attributed to the high proportion of patients in the combination group who subsequently received third-generation TKIs as a later-line therapy. These findings support the superior efficacy of third-generation EGFR-TKIs as the recommended first-line treatment and also indicate that the first-/second-generation EGFR-TKI combination therapy is a viable alternative in real-world practice.

BACKGROUND:Lung cancer remains the primary cause of cancer-related mortality globally, despite significant advancements in therapeutic strategies. Overall survival rates remain unsatisfactory. Chronic inflammation and microRNAs both play pivotal roles in cancer development. METHODS:This study aimed to elucidate the roles of key microRNAs in inflammation-associated non-small cell lung cancer (NSCLC) development. RESULTS:Our findings reveal a significant reduction in miR-125b expression within NSCLC cell lines when stimulated by IL-10. Furthermore, when stimulated by IFN-γ, the expression levels of miR-125b markedly increase. Enforced expression of miR-125b markedly bolstered cell proliferation, migration, and invasion, while diminishing cell apoptosis. Conversely, inhibition of miR-125b produced opposing effects. Mechanistically, DAZAP2 was identified as a direct regulatory target of miR-125b However, because both miR-125b inhibition and DAZAP2 knockdown suppressed malignant phenotypes, DAZAP2 may represent one component of a broader miR-125b-associated regulatory network rather than the sole mediator of miR-125b function. Combined inhibition of miR-125b and DAZAP2 produced more pronounced tumor-suppressive effects both in vitro and in vivo. CONCLUSIONS:Our data suggest that miR-125b and DAZAP2 are involved in the cytokine-responsive regulatory network of inflammation-related NSCLC progression.

Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

The clinical effect of KRAS G12C inhibitors (G12Ci) as monotherapy is poor, prompting the development of combination treatment strategies. Here, we demonstrate that the WEE1 kinase inhibitor (WEE1i), Adavosertib, can sensitize the effect of G12Ci through the MYBL2-RRM2 axis, which is associated with poor prognosis in lung cancer. Overexpressing the MYBL2-RRM2 axis or supplementing the products of the RRM2 enzyme, dNTPs/dNs, can partially reverse this synergistic inhibitory effect. We also observed marked effects of the combination therapy in tumor xenografts models. Collectively, these results uncover the WEE1 kinase inhibitors, some of which are available clinically, as effective enhancers for G12Ci therapy.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, with KRAS mutations present in approximately 20-25% of cases. The KRAS-G12C mutation, occurring in approximately 14% of lung adenocarcinomas, has emerged as a critical target for precision medicine strategies. While KRAS-G12C inhibitors, including sotorasib and adagrasib, have shown promise in clinical trials, their efficacy is limited by primary and acquired resistance mechanisms. This study explored the potential of combining anlotinib, a multi-target tyrosine kinase inhibitor, with KRAS-G12C inhibitors to overcome these resistance challenges in NSCLC treatment. Our results demonstrated that anlotinib improved the sensitivity to KRAS-G12C inhibitors in primary and acquired resistance settings, both in vitro and in vivo. Mechanistically, the combination therapy inhibited c-Myc/ORC2 signaling, leading to cell cycle arrest and apoptosis. These findings suggest that the combination of anlotinib and KRAS-G12C inhibitors represents a promising novel therapeutic approach for KRAS-G12C-mutant NSCLC.

Oncogenic KRAS mutations are frequently detected in NSCLC. It remains a major challenge to target all KRAS mutants. MEK inhibitors are considered candidates for treating KRAS-mutant NSCLC; however, their easy adaptive resistance precludes further application. Here, we found that MEK inhibitor-trametinib treatment results in the feedback activation of multiple receptor tyrosine kinases (RTKs) and that treatment with the pan-RTK inhibitor anlotinib effectively inhibits the progression of KRAS-mutant NSCLC. Furthermore, we evaluated this strategy in a clinical study (NCT04967079) involving 33 advanced non-G12C KRAS-mutant NSCLC patients. The phase Ia containing 13 patients showed that the recommended phase 2 dose (RP2D) is trametinib (2 mg) plus anlotinib (8 mg), the objective response rate (ORR) is 69.2% (95% CI: 38.6-90.9), the median progression-free survival (PFS) is 6.9 months (95% CI: 3.9 to could not be evaluated), disease control rate (DCR) is 92% (95% CI: 64.0–99.8) and the rate of adverse events (AEs) ≥grade 3 is 23%. The phase Ib containing 20 patients demonstrated the high efficacy of this combinational therapy with RP2D, with the ORR at 65% (95% CI: 40.8–84.6), the median PFS is 11.5 months (95% CI: 8.3–15.5), the median overall survival (OS) is 15.5 months (95% CI: 15.5 to could not be evaluated), the DCR at 100% (95% CI: 83.2–100.0), the median duration of overall response (DoR) is 9.3 months (95% CI: 2.5–12.1), and the rate of AEs ≥ grade 3 at 35%. Overall, this study provides a potential combinational therapeutic strategy for KRAS-mutant NSCLC through the cotargeting of MEK and RTKs.

Patients with head and neck squamous cell carcinoma (HNSCC) often exhibit only partial responses to immunotherapy, resulting in poor prognosis and potential overtreatment. While pseudogenes are known to significantly impact the tumor microenvironment (TME) and tumor prognosis, their specific role in HNSCC remains unclear. A prognostic risk profile for HNSCC patients was developed and validated using pseudogene pairs. The prognostic value of the risk signiture was assessed using survival analysis, ROC analysis, and Cox regression models. Correlations between our risk profile and immunologic characteristics of the TME were analyzed, with TIDE scores used to predict immunotherapy responses. LAT, identified as a central gene in the risk model through WGCNA and Friend analysis, was further investigated. LAT expression and its association with immune cells and immune checkpoints within the TME were examined using an internal cohort and immunofluorescence. The model based on pseudogene pairs demonstrated strong prognostic power, with significantly longer overall survival in low-risk patients compared to high-risk ones. Additionally, risk scores were inversely related to immune infiltration and predictive of immunotherapy response. LAT, identified as a potential hub gene in the low-risk group, showed stable performance across multiple validation sets and was positively correlated with T cell infiltration and high expression in an inflammatory TME. A pseudogene pair-based survival prediction model for HNSCC was developed and validated, providing valuable insights for HNSCC treatment. LAT may serve as a novel biomarker for predicting immune response.

Background:Treatment of tyrosine kinase inhibitor (TKI)-resistant anaplastic lymphoma kinase (ALK) rearranged non-small cell lung cancer (NSCLC) remains an unmet need. Among these patients, the efficacy of immunotherapy has not been thoroughly investigated. The purpose of our study was to evaluate the efficacy of immunotherapy in patients with ALK-TKI-resistant NSCLC, stratified by programmed cell death ligand-1 (PD-L1) expression. Methods:We retrospectively collected the data of advanced NSCLC patients with ALK-rearrangement, who were treated with immunotherapy or chemotherapy after the development of ALK-TKI resistance at the Shanghai Chest Hospital. Progression-free survival (PFS) was used to evaluate the outcomes. Results:The final analysis included 89 patients between June 1, 2018, and December 31, 2022, who met the selection criteria. The entire cohort had a median follow-up time of 33.4 months. The patients who received immunotherapy had better PFS than those who received non-immunotherapy (median PFS: 5.3 vs. 2.5 months; P=0.009). The PD-L1-positive patients who received immunotherapy had a median PFS of 7.1 months, while those who received non-immunotherapy had a median PFS of 2.5 months (P=0.02). No such statistically significant difference was observed in the PD-L1-negative patients (median PFS for with immunotherapy vs. without immunotherapy: 1.5 vs. 2.9 months; P=0.68). The PD-L1-positive patients who underwent re-biopsy after the development of TKI resistance and who received immunotherapy had a PFS of 7.8 months, while those who received non-immunotherapy had a PFS of 2.7 months (P=0.002). Conclusions:This was the first real-world retrospective study to show that some patients with positive PD-L1 expression may benefit from immune-based therapy after the development of ALK-TKI resistance. However, we still recommend biopsy for patients who develop ALK-TKI resistance to provide further treatment guidance.

BackgroundUnlike lung adenocarcinoma, patients with advanced squamous carcinoma exhibit a low proportion of driver gene positivity, with fewer effective treatment strategies available. Chemoimmunotherapy has now become the standard first-line treatment for individuals diagnosed with advanced lung squamous carcinoma. Serum metabolomics holds significant potential for application in predicting responses to chemoimmunotherapy and is capable of identifying and validating potential biomarkers. The aim of our study was to establish a model that can predict the prognosis of chemoimmunotherapy in patients with advanced lung squamous cell carcinoma, integrating metabolomics with machine learning techniques.MethodsWe collected 79 serum samples from patients with advanced lung squamous cell carcinoma before receiving combined immunotherapy and performed untargeted metabolomics analysis. Patients were divided into non-response (NR) and response (R) groups according to overall survival (OS), and prognostic models were constructed and validated using different machine learning methods. The patients were further categorized into high-risk and low-risk groups based on the median risk score, to assess the model's predictive performance.ResultsThere were significant differences in metabolites and metabolic pathways between NR and R groups, and 117 differential metabolites were preliminarily screened (p < 0.05, VIP > 1). Further, least absolute shrinkage and selection operator (LASSO) and random forest (RF) were used to identify metabolites, and then their common metabolites were used as the best biomarkers to build a prediction model containing 8 differential metabolites. Based on these biomarkers, RF, support vector machine (SVM) and logistic regression were used to randomly divide patients into training and validation sets in a 7:3 ratio, respectively. We found that the RF method resulted in area under curves (AUCs) of 0.973 and 0.944 for the training and validation sets, respectively, with the best predictive performance. Subsequently, both OS and progression-free survival (PFS) were notably reduced in the high-risk group when contrasted with the low-risk group.ConclusionsWe developed a model containing 8 metabolites based on metabolomics and machine learning that may predict survival outcomes in patients with advanced lung squamous cell carcinoma undergoing chemoimmunotherapy, helping to more accurately assess efficacy and prognosis in clinical practice.

BACKGROUND:Neoadjuvant therapy (NAT) is a cornerstone in the treatment of locally advanced gastric cancer, improving surgical outcomes and survival. However, the optimal timing for surgery following NAT remains controversial. This study evaluates the impact of the interval between NAT and surgery on overall survival (OS) and explores associated clinicopathological factors. METHODS:A retrospective analysis of 893 patients undergoing NAT and curative surgery for gastric adenocarcinoma across three centers in China was conducted. Surgical intervals were categorized (∼28 days, 29-42 days, >42 days). Survival analyses employed restricted cubic spline (RCS) models, Kaplan-Meier methods, and Cox proportional hazards regression. RESULTS:Patients operated on within 28 days post-NAT had the most favorable OS, while intervals longer than 28 days were independently associated with worse outcomes. RCS analysis revealed increased risks for intervals longer than 28 days. Prolonged intervals showed declining effectiveness in tumor regression. Stratified analyses indicated that patients with poor NAT response (TRG 3) particularly benefited from surgery within 4 weeks, while delays were detrimental. CONCLUSIONS:Timely surgery, especially within 4 weeks post-NAT, optimizes survival outcomes, particularly in patients with limited NAT response. This study underscores the need for individualized surgical timing and calls for prospective multicenter validation.

The objective of this study is to assess the prognostic efficacy of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET-CT) parameters in nasopharyngeal carcinoma (NPC) and identify the best machine learning (ML) prognostic model for NPC patients based on these 18F-FDG PET/CT parameters and clinical variables. A cohort of 678 patients diagnosed with NPC between 2016 and 2020 was analyzed in this study. The model was constructed using four advanced ML algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Least Absolute Shrinkage and Selection Operator (LASSO), and multifactor COX step-up regression. Statistical significance of the models was assessed using Kaplan–Meier (K–M) curves, with a significance level established at P < 0.05. The prognostic efficacy of the models was evaluated through the analysis of receiver operating characteristic (ROC) curves, with the area under the ROC curve (AUC) serving as a criterion for model selection. The decision curve analysis (DCA) and concordance index (C-index) were employed to assess the precision of the optimal model. Multivariate analysis revealed age, T stage, and metabolic tumor volume (MTV) for the primary nasopharyngeal tumor (MTVT) as significant independent prognostic factors for overall survival (OS) in NPC patients. Additionally, the LASSO model identified six key variables, including peak standardized uptake value (SUV-peak) for the primary nasopharyngeal tumor (SUV-peak(T)), MTVT, heterogeneity index for neck lymph nodes (HIN), age, pathological type, and T stage. Remarkably, the LASSO model demonstrated superior performance with a 5-year AUC of 0.849 compared to other models. Further assessment using the C-index and DCA confirmed the accuracy of the LASSO model. Subgroup analysis revealed notable risk factors, such as a high heterogeneity index (HI) for the primary nasopharyngeal tumor (HIT), MTV values for neck lymph nodes (MTVN), and HIN. We developed a novel prognostic machine learning model that integrates 18F-FDG PET-CT parameters and clinical characteristics, significantly enhancing prognosis prediction in NPC.

Our previous work showed that KRAS activation in gastric cancer cells leads to activation of an epithelial-to-mesenchymal transition (EMT) program and generation of cancer stem-like cells (CSCs). Here we analyze how this KRAS activation in gastric CSCs promotes tumor angiogenesis and metastasis. Gastric cancer CSCs were found to secrete pro-angiogenic factors such as vascular endothelial growth factor A (VEGF-A), and inhibition of KRAS markedly reduced secretion of these factors. In a genetically engineered mouse model, gastric tumorigenesis was markedly attenuated when both KRAS and VEGF-A signaling were blocked. In orthotropic implant and experimental metastasis models, silencing of KRAS and VEGF-A using shRNA in gastric CSCs abrogated primary tumor formation, lymph node metastasis, and lung metastasis far greater than individual silencing of KRAS or VEGF-A. Analysis of gastric cancer patient samples using RNA sequencing revealed a clear association between high expression of the gastric CSC marker CD44 and expression of both KRAS and VEGF-A, and high CD44 and VEGF-A expression predicted worse overall survival. In conclusion, KRAS activation in gastric CSCs enhances secretion of pro-angiogenic factors and promotes tumor progression and metastasis.

8542 Background: It remains as a big challenge to provide therapeutic regime for the non-G12C KRAS-mutant non-small cell lung cancer (NSCLC) patients. The strategy of co-inhibition of MEK/RTKs pathways via trametinib and anlotinib showed preliminary activity in non-G12C KRAS-mutant NSCLC. Methods: The phase I clinical trial (NCT04967079) was divided into 2 parts including part A and part B. The primary endpoint of part A was to determine the recommended phase 2 dose (RP2D) and the primary endpoint of part B was to evaluate the objective response rate (ORR). The secondary endpoints were progression-free survival (PFS), disease control rate (DCR) and safety. Results: The part A containing 13 patients showed that the RP2D is trametinib (2 mg) plus anlotinib (8 mg), the ORR is 69.2%, the PFS is 207 days, DCR is 92% and the rate of adverse events (AEs) ≥ grade 3 is 23%. The part B containing 20 patients showed high efficacy of this combinational therapy (trametinib (2 mg) plus anlotinib (8 mg)), with the ORR at 65%, the PFS is 330 days, the DCR at 100%, and the rate of AEs ≥ grade 3 at 35%. An integrative analysis for the part A plus part B (33 patients) indicated that the ORR is 66.7%, the PFS is 300 days, the DCR is 97% and the rate of adverse events (AEs) ≥ grade 3 is 30%. Conclusions: This study provides a potential combinational therapeutic strategy for those non-G12C KRAS-mutant lung cancer patients via oral administration of trametinib and anlotinib. Clinical trial information: NCT04967079 .

Background Malnutrition and inflammation can affect the prognosis of patients with gastric cancer (GC). This study aimed to explore the value of fat-free mass index (FFMI) combined with the neutrophil–lymphocyte ratio (NLR) on the short- and long-term outcomes of patients with GC. Methods We retrospectively analyzed 1603 patients with GC in at a tertiary referral teaching hospital between 2016 and 2019. Patients in the 1st quartile of FFMI were defined as the low FFMI group and the remaining patients as the normal FFMI group, according to sex-specific quartiles. Patients were divided into high and low NLR groups according to the median NLR. Patients with a low FFMI/high NLR were defined as the high-risk group, and the remaining patients were defined as the low-risk group. Results The postoperative recovery time of the high-risk group was significantly longer than that of the low-risk group (all P <0.05). Logistic regression analysis indicated that FNC could independently predict postoperative anastomotic leakage (OR=2.16, 95% CI: 1.03–4.54, P=0.041). The high-risk group had much worse 3-y overall survival (64.7% vs. 79.4%; P<0.001) and 3-y disease-free survival (62.8% vs. 78.6%; P<0.001) than the low-risk group. Multivariate Cox analysis showed that FNC was an independent prognostic factor for patients with GC (HR=1.54, 95% CI: 1.22–1.94, P<0.001). Further stratified analysis based on tumor stage showed that the high-risk group did not benefit from postoperative adjuvant chemotherapy. Conclusions FFMI combined with NLR can predict postoperative short- and long-term outcomes in patients with GC.

e16262 Background: As non-alcoholic fatty liver disease (NAFLD) emerges as a key factor in hepatocellular carcinoma (HCC) development, exacerbated by the global obesity epidemic, the prognostic role of body mass index (BMI) in NAFLD-HCC patients undergoing surgical resection remains unclear. This study aims to elucidate the impact of preoperative BMI on long-term outcomes after hepatectomy in patients with NAFLD-HCC, offering insights for more personalized treatment strategies. Methods: In this multicenter retrospective study, patients with early-stage (BCLC stage 0/A) NAFLD-HCC who underwent curative hepatectomy between 2009 and 2022 were analyzed. Based on preoperative BMI, patients were classified into lean ( < 23.0 kg/m 2 ), overweight (23.0-27.4 kg/m 2 ), and obese (≥ 27.5 kg/m 2 ) categories. The primary endpoints, overall survival (OS) and recurrence-free survival (RFS), were compared across these BMI groups. Results: This large multicenter cohort comprised 309 early-stage NAFLD-HCC patients: 66 lean, 176 overweight, and 67 obese. Liver-, tumor-, and surgery-related characteristics were similar across groups. Lean patients exhibited significantly lower 5-year OS and RFS (55.4% and 35.1%, respectively) compared to overweight patients (71.3% and 55.6%, P = 0.017 and P = 0.002), with outcomes comparable to obese patients (48.5% and 38.2%, P = 0.939 and P = 0.442). Multivariable Cox-regression analysis revealed lean BMI, but not obese BMI, as an independent predictor of decreased OS (HR: 1.69; 95%CI: 1.06-2.71; P = 0.029) and RFS (HR: 1.72; 95% CI: 1.17-2.52; P = 0.006). Conclusions: This study challenges conventional perceptions of BMI in cancer prognosis, revealing that lean NAFLD-HCC patients have poorer long-term surgical outcomes compared to their overweight and obese counterparts. These findings underscore the need for a nuanced understanding of BMI’s role in NAFLD-HCC management and prompt further investigation into the underlying biological mechanisms of this paradox.