Qunyi Li

Background: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced or unresectable hepatocellular carcinoma (HCC). However, the comparative clinical efficacy and cost-effectiveness of various ICI-based combination therapies remain unclear. Objectives: This study aimed to evaluate the cost-effectiveness and clinical efficacy of the 12 first-line ICI-based therapies for advanced or unresectable HCC under two distinct healthcare systems—China and the United States. Design: A model-based pharmacoeconomic analysis. Methods: A network meta-analysis (NMA) was conducted to compare first-line ICI-based therapies in terms of overall survival (OS) and progression-free survival (PFS). A partitioned survival model with three health states (progression-free, progressive disease, death) was developed for cost-effectiveness analysis. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER) were primary outcomes. One-way and probabilistic sensitivity analyses were performed to assess model uncertainty. Results: The NMA included 11 randomized clinical trials comprising 7289 patients. Four ICI-based regimens (atezolizumab plus bevacizumab, camrelizumab plus rivoceranib, sintilimab plus bevacizumab biosimilar, and penpulimab plus anlotinib) were associated with significantly improved OS and PFS compared with sorafenib. In China, camrelizumab plus rivoceranib and tislelizumab were cost-effective compared with sorafenib, with ICERs of $17,624.64 and $1971.14 per QALY, respectively. In the United States, no ICI-based therapy was cost-effective at a willingness-to-pay threshold of $150,000 per QALY; sorafenib remained the most cost-effective option. ICERs were most sensitive to drug costs, utility values, and discount rates. Conclusion: Camrelizumab plus rivoceranib represents a cost-effective first-line therapy for advanced or unresectable HCC in China. In the United States, current ICI-based therapies are not cost-effective at existing prices. These findings may inform treatment selection and health policy decision-making in different healthcare systems.

The Warburg effect, which is aerobic glycolysis, constitutes a major driver of various cancer progression. Therefore, we aimed to examine the role of peroxisome proliferator-activated receptor-gamma coactivator-1α (PGC1α) and its competing endogenous RNA (ceRNA) network in colorectal cancer (CRC) metabolic reprogramming. We used bioinformatics analysis and dual-luciferase reporter gene experiments and identified the DNMBP-AS1/hsa-miR-30a-5p/PGC1α ceRNA network. Additionally, we investigate the impact of PGC1α expression alterations on CRC proliferation and metabolic reprogramming. Moreover, we studied the influence of PGC1α on pyruvate kinase M2 (PKM2), and CRC malignant behavior manifestation. Our study has uncovered a significant association between the DNMBP-AS1/hsa-miR-30a-5p/PGC1α ceRNA network and CRC patient prognosis. Additionally, PGC1α overexpression impeded CRC growth, reduced glycolytic capacity, and enhanced anti-PD-1 therapy efficacy. PGC1α inhibited tumor cell glycolysis by downregulating the WNT/β-catenin pathway depending on peroxisome proliferator-activated receptor gamma (PPARγ), thereby suppressing PKM2. The PPARγ agonist rosiglitazone could hinder CRC proliferation and glycolytic activity. Combined with the PGC1α agonist ZLN005, it exhibits synergistic effects for treating CRC. Moreover, we verified that ZLN005 significantly potentiated PD-1 induced tumor suppression in xenograft mice. Finally, we demonstrated that PGC1α and PKM2 expression patterns in tumor tissues were closely related to patient prognosis. Moreover, we constructed a predictive model to predict the 5-year survival events in CRC patients using random forest model. Our results offer novel perspectives on the role of DNMBP-AS1/hsa-miR-30a-5p/PGC1α network in controlling CRC proliferation, metabolism and immune responses. Furthermore, our investigation reveals that using rosiglitazone combined with PGC1α agonist presents a promising therapeutic approach for managing CRC.

Background: V‐set and immunoglobulin domain containing 4 (VSIG4) is a B7‐family‐related protein almost exclusively expressed on macrophages. The difference in its expression mediates the dynamic transformation of the polarization state of macrophages, but the underlying mechanism is still unclear. We sought to reveal the correlation between VSIG4 and the polarization of tumour‐associated macrophages (TAMs) and the immune escape of tumour cells in colorectal cancer (CRC).Methods: THP‐1 monocyte‐derived macrophages expressing different levels of VSIG4 were used for in vitro investigations. In addition, the co‐culture system was used to verify the effect of tumour cells on the expression of VSIG4 in macrophages, and the effect of VSIG4 expression level on tumour cells in turn. Subcutaneous xenograft models evaluated the tumour growth inhibition efficacy of VSIG4 blockade as monotherapy and combined with immune checkpoint inhibitors (ICIs).Results: CRC cells secreted lactate to promote VSIG4 expression in macrophages. On the contrary, VSIG4 promoted macrophage M2 polarization and induced malignant progression of tumour cells by promoting M2 macrophage secretion of heparin‐bound epidermal growth factor. In vivo experiments confirmed that knockdown VSIG4 inhibited tumour growth and improved the efficacy of ICIs therapy. Mechanistically, lactate secreted by CRC cells promoted its expression by influencing the epigenetic modification of VSIG4 in macrophages. In addition, VSIG4 enhanced the fatty acid oxidation (FAO) of macrophages and upregulated PPAR‐γ expression by activating the JAK2/STAT3 pathway, which ultimately induced M2 polarization of macrophages. Downregulation of VSIG4 or blocking of FAO reversed the M2 polarization process of macrophages.Conclusions: Our findings provide a molecular basis for VSIG4 to influence TAMs polarization by regulating the reprogramming of FAO, suggesting that targeting VSIG4 in macrophages could enhance the ICIs efficacy and represent a new combination therapy strategy for immunotherapy of CRC.Key points: Colorectal cancer cells secrete lactate to upregulate VSIG4 in macrophages via the H3K18la‐METTL14‐m6A axis. VSIG4 promotes fatty acid oxidation of macrophages and drives its M2‐type polarization. These VSIG4‐expressing M2 macrophages promote tumour progression and an immunosuppressive microenvironment. Inhibition of VSIG4 expression can synergistically enhance the therapeutic effect of anti‐PD‐1 antibody.

Colorectal cancer (CRC) progression is driven by complex metabolic alterations, including aberrant N‐glycosylation patterns that critically influence tumor development. However, the metabolic and functional roles of N‐glycosylation in CRC remain poorly understood. Herein, comprehensive proteomic and N‐linked intact glycoproteomics analyses are performed on 45 CRC tumors, and normal adjacent tissues (NATs) are matched, identifying 7125 intact N‐glycopeptides from 704 glycoproteins. Through analysis of glycoform expression profiles and structural characteristics, a glycosylation site–protein function association network is constructed to uncover metabolic dysregulation driven by N‐glycosylation in CRC. Moreover, an arithmetic model is developed that integrates N‐glycan expression patterns, which effectively distinguishes tumors from NATs, reflecting metabolic reprogramming in cancer. These findings identify Chloride Channel Accessory 1 (CLCA1) and Olfactomedin 4 (OLFM4) as potential metabolic biomarkers for CRC diagnosis. Immunohistochemistry and Cox regression analyses validated the prognostic power of these markers. Notably, the critical role of specific N‐glycosylation at N196 of Adipocyte plasma membrane‐associated protein (APMAP) is highlighted, a key player in tumor metabolism and CRC progression, providing a potential target for therapeutic intervention. These findings offer valuable insights into the metabolic roles of N‐glycosylation in CRC, advancing biomarker discovery, enhancing metabolic‐based diagnostic precision, and improving personalized treatment strategies targeting cancer metabolism.

Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlighting the role of pharmacogenomics in developing better predictive models. We analyzed 379 CRC patients receiving fluorouracil-based chemotherapy, collecting data on fluorouracil metabolism-related SNPs (TYMS, MTHFR, DPYD, RRM1), blood inflammatory markers, and clinical status. Six machine learning models—K-nearest neighbors, support vector machine, gradient boosting decision trees (GBDT), eXtreme Gradient Boosting (XGBoost), LightGBM, and random forest—were compared against multivariate logistic regression and a deep learning model (i.e., multilayer perceptron, MLP). Feature importance analysis highlighted seven predictors: histological grade, N and M staging, monocyte count, platelet-to-lymphocyte ratio, MTHFR rs1801131, and RRM1 rs11030918. In a five-fold cross-validation, XGBoost and GBDT exhibited superior performance, with Area Under Curve (AUC) of 0.88 ± 0.02. XGBoost excelled in identifying favorable prognosis (recall = 0.939). GBDT demonstrated balance in recognizing both categories, with a recall for favorable prognosis of 0.908 and a precision for unfavorable prognosis of 0.863. MLP had a similar AUC (0.87) with high precision for favorable prognosis (recall = 0.946). In external validation, XGBoost model achieved an accuracy of 0.79. An online prognostic tool based on XGBoost was developed, integrating metabolism-related SNPs and inflammatory markers, enhancing CRC treatment precision and supporting tailored chemotherapy.

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients.