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Background Fibroblast growth factors (FGFs) and their receptors (FGFRs) play a crucial role in cell fate and angiogenesis, with dysregulation of the signaling axis driving tumorigenesis. Therefore, many studies have targeted FGF/FGFR signaling for cancer therapy and several FGFR inhibitors have promising results in different tumors but treatment efficiency may still be improved. The clinical use of immune checkpoint blockade (ICB) has resulted in sustained remission for patients. Main Although there is limited data linking FGFR inhibitors and immunotherapy, preclinical research suggest that FGF/FGFR signaling is involved in regulating the tumor microenvironment (TME) including immune cells, vasculogenesis, and epithelial-mesenchymal transition (EMT). This raises the possibility that ICB in combination with FGFR-tyrosine kinase inhibitors (FGFR-TKIs) may be feasible for treatment option for patients with dysregulated FGF/FGFR signaling. Conclusion Here, we review the role of FGF/FGFR signaling in TME regulation and the potential mechanisms of FGFR-TKI in combination with ICB. In addition, we review clinical data surrounding ICB alone or in combination with FGFR-TKI for the treatment of FGFR-dysregulated tumors, highlighting that FGFR inhibitors may sensitize the response to ICB by impacting various stages of the “cancer-immune cycle”.

The hepatocyte growth factor (HGF) along with its receptor (c-MET) are crucial in preserving standard cellular physiological activities, and imbalances in the c-MET signaling pathway can lead to the development and advancement of tumors. It has been extensively demonstrated that immune checkpoint inhibitors (ICIs) can result in prolonged remission in certain patients. Nevertheless, numerous preclinical studies have shown that MET imbalance hinders the effectiveness of anti-PD-1/PD-L1 treatments through various mechanisms. Consequently, clarifying the link between the c-MET signaling pathway and the tumor microenvironment (TME), as well as uncovering the effects of anti-MET treatment on ICI therapy, is crucial for enhancing the outlook for tumor patients. In this review, we examine the impact of abnormal activation of the HGF/c-MET signaling pathway on the control of the TME and the processes governing PD-L1 expression in cancer cells. The review thoroughly examines both clinical and practical evidence regarding the use of c-MET inhibitors alongside PD-1/PD-L1 inhibitors, emphasizing that focusing on c-MET with immunotherapy enhances the effectiveness of treating MET tumors exhibiting elevated PD-L1 expression.

Background Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration’s (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC. Methods In this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs). Results Analyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001). Conclusion ICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients’ responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient’s unique response situations.

Despite great success, immunotherapy still faces many challenges in practical applications. It was previously found that family with sequence similarity 110 member A (FAM110A) participate in the regulation of the cell cycle and plays an oncogenic role in pancreatic cancer. However, the prognostic value of FAM110A in pan-cancer and its involvement in immune response remain unclear. The Human Protein Atlas (HPA) database was used to detect the expression of FAM110A in human normal tissues, the Tumor Immune Estimation Resource (TIMER) and TIMER 2.0 databases were used to explore the association of FAM110A expression with immune checkpoint genes and immune infiltration, and the Gene Set Cancer Analysis (GSCA) database was used to explore the correlation between FAM110A expression and copy number variations (CNV) and methylation. The LinkedOmics database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Statistical analysis and visualization of data from the The Cancer Genome Atlas (TCGA) or the Genotype-Tissue Expression (GTEx) databases were performed using the R software (version 3.6.3). Clinical samples were validated using immunohistochemistry. FAM110A expression was elevated in most tumor tissues compared with that in normal tissues. CNV and methylation were associated with abnormal FAM110A mRNA expression in tumor tissues. FAM110A affected prognosis and was associated with the expression of multiple immune checkpoint genes and abundance of tumor-infiltrating immune cells across multiple types of cancer, especially in liver hepatocellular carcinoma (LIHC). FAM110A-related genes were involved in multiple immune-related processes in LIHC. FAM110A participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in LIHC. FAM110A may serve as a prognostic and immunological biomarker for human cancer.

Chemoresistance is the most significant challenge affecting the clinical efficacy of the treatment of patients with gastric cancer (GC). Here we reported that transmembrane protein 160 (TMEM160) suppressed ferroptosis and induced chemoresistance in GC cells. Mechanistically, TMEM160 recruited the E3 ligase TRIM37 to promote K48-linked ubiquitination and degradation of KEAP1, thereby activating NRF2 and transcriptionally upregulating the target genes GPX4 and SLC7A11 to inhibit ferroptosis. Further in vitro and in vivo experiments demonstrated that the combination of TMEM160 targeting and chemotherapy had a synergistic inhibitory effect on the growth of GC cells, which was partially NRF2-dependent. Moreover, TMEM160 and NRF2 protein levels were markedly overexpressed in GC tissues, and their co-overexpression was an independent factor for poor prognosis. Collectively, these findings indicate that TMEM160, as a pivotal negative regulator of ferroptosis, exerts a crucial influence on the chemoresistance of GC through the TRIM37-KEAP1/NRF2 axis, providing a potential new prognostic factor and combination therapy strategy for patients with GC.

Background The effectiveness of anti-programmed cell death protein 1(PD-1)/programmed cell death 1 ligand 1(PD-L1) therapy in treating certain types of cancer is associated with the level of PD-L1. However, this relationship has not been observed in colorectal cancer (CRC), and the underlying regulatory mechanism of PD-L1 in CRC remains unclear. Methods Binding of TMEM160 to PD-L1 was determined by co-immunoprecipitation (Co-IP) and GST pull-down assay.The ubiquitination levels of PD-L1 were verified using the ubiquitination assay. Phenotypic experiments were conducted to assess the role of TMEM160 in CRC cells. Animal models were employed to investigate how TMEM160 contributes to tumor growth.The expression and clinical significance of TMEM160 and PD-L1 in CRC tissues were evaluated by immunohistochemistry(IHC). Results In our study, we made a discovery that TMEM160 interacts with PD-L1 and plays a role in stabilizing its expression within a CRC model. Furthermore, we demonstrated that TMEM160 hinders the ubiquitination-dependent degradation of PD-L1 by competing with SPOP for binding to PD-L1 in CRC cells. Regarding functionality, the absence of TMEM160 significantly inhibited the proliferation, invasion, metastasis, clonogenicity, and radioresistance of CRC cells, while simultaneously enhancing the cytotoxic effect of CD8 + T cells on tumor cells. Conversely, the upregulation of TMEM160 substantially increased these capabilities. In severely immunodeficient mice, tumor growth derived from lentiviral vector shTMEM160 cells was lower compared with that derived from shNC control cells. Furthermore, the downregulation of TMEM160 significantly restricted tumor growth in immune-competent BALB/c mice. In clinical samples from patients with CRC, we observed a strong positive correlation between TMEM160 expression and PD-L1 expression, as well as a negative correlation with CD8A expression. Importantly, patients with high TMEM160 expression exhibited a worse prognosis compared with those with low or no TMEM160 expression. Conclusions Our study reveals that TMEM160 inhibits the ubiquitination-dependent degradation of PD-L1 that is mediated by SPOP, thereby stabilizing PD-L1 expression to foster the malignant progress, radioresistance, and immune evasion of CRC cells. These findings suggest that TMEM160 holds potential as a target for the treatment of patients with CRC.

Background Trifluridine/Tipiracil (TAS-102) is an effective agent for the late-line treatment of metastatic colorectal cancer (mCRC). Combining TAS-102 with bevacizumab improves outcomes but may increase adverse events. We conducted a real-world, retrospective, exploratory comparison of two dosing schedules (bi-weekly vs. four-weekly) to describe efficacy, safety, and potential molecular and clinical correlates. Methods We analyzed patients with mCRC who were treated with TAS-102 in combination with bevacizumab as late-line therapy from January 2020 to February 2023. Regimen assignment followed physician-patient shared decision-making based on clinical factors and local practice changes after emerging evidence, not randomization. Endpoints included progression-free survival (PFS), overall survival (OS), adverse events (AEs). Analyses were exploratory and hypothesis-generating, with multivariable Cox models for selected covariates. Results A total of 92 patients were enrolled in this study. Median PFS was 3.2 months (bi-weekly) vs. 3.7 months (four-weekly), and median OS was 10.0 vs. 9.3 months, with no statistically significant differences. KRAS mutation was associated with inferior OS (7.7 vs. 11.8 months; P  = 0.018), whereas TP53 was not. Eastern Cooperative Oncology Group performance status (ECOG-PS) = 2 independently predicted shorter PFS and OS; prior bevacizumab exposure correlated with shorter PFS but not OS. Common adverse events in patients were neutropenia (63.0%), leukopenia (67.0%), anemia (44.6%), malaise (55.4%), nausea (45.7%), anorexia (31.5%), and diarrhea (23.9%). Conclusion In this retrospective, real-world study, the two regimens demonstrated comparable disease control, and the bi-weekly regimen appeared to be better tolerated, representing a reasonable potential alternative. Nevertheless, these findings should be interpreted as exploratory, and future prospective studies are warranted.

Mutation of the tumor suppressor gene TP53 promotes ovarian cancer progression and therapeutic resistance. Whether mutant p53 (mtp53) regulates alternative splicing and how this regulation can be exploited for cancer therapy remain unclear. Here, small nuclear ribonucleoprotein D2 polypeptide (SNRPD2) as a binding partner of mtp53 is identified. SNRPD2 is highly expressed in ovarian cancer and associated with an unfavorable prognosis. The overexpression of SNRPD2 promotes, whereas its depletion inhibits, the growth and migration of ovarian cancer cells. Mechanistically, mtp53 cooperates with SNRPD2 to facilitate the assembly of the Sm/SMN protein complex, an essential component of the spliceosome, modulating alternative splicing of pre‐mRNAs. Specifically, the co‐depletion of mtp53 and SNRPD2 reduces the level of OTUD3 oncogenic transcripts while increasing its tumor suppressor counterparts through an exon‐skipping event. Moreover, therapeutic engineered exosomes are developed with their surfaces decorated with iRGD and their interiors loaded with siRNAs targeting mtp53 and SNRPD2. These exosomes effectively suppress the growth of ovarian cancer cells and enhance their sensitivity to chemotherapy in vivo. Collectively, this study uncovers that mtp53 and SNRPD2 cooperatively regulate alternative splicing to drive ovarian cancer progression, and co‐targeting these two molecules via engineered exosomes represents a potential therapeutic strategy for ovarian cancer. Mutant p53 drives oncogenic splicing to promote the progression of ovarian cancer by partnering with the spliceosome factor SNRPD2. Therefore, it is engineered iRGD‐exosomes to co‐deliver siRNAs against both targets. This approach restored tumor‐suppressive mRNA isoforms, effectively enhanced sensitivity to cisplatin, and ultimately blocked tumor progression.

Hippo-Yes-associated protein 1 (YAP1) plays an important role in gastric cancer (GC) progression; however, its regulatory network remains unclear. In this study, we identified Copine III ( CPNE3 ) was identified as a novel direct target gene regulated by the YAP1/TEADs transcription factor complex. The downregulation of CPNE3 inhibited proliferation and invasion, and increased the chemosensitivity of GC cells, whereas the overexpression of CPNE3 had the opposite biological effects. Mechanistically, CPNE3 binds to the YAP1 protein in the cytoplasm, inhibiting YAP1 ubiquitination and degradation mediated by the E3 ubiquitination ligase β-transducin repeat-containing protein (β-TRCP). Thereby activating the transcription of YAP1 downstream target genes, which creates a positive feedback cycle to facilitate GC progression. Immunohistochemical analysis demonstrated significant upregulation of CPNE3 in GC tissues. Survival and Cox regression analyses indicated that high CPNE3 expression was an independent prognostic marker for GC. This study elucidated the pivotal involvement of an aberrantly activated CPNE3/YAP1 positive feedback loop in the malignant progression of GC, thereby uncovering novel prognostic factors and therapeutic targets in GC.

BackgroundGastric cancer (GC) is a major global health burden the third cause of cancer-related death. Accurate differentiation between benign and malignant gastric lesions is critical for clinical decision-making. Histopathology remains the gold standard but relies on subjective interpretation and often misses subtle cellular features. Recent advances in deep learning (DL) have shown great promise in enhancing diagnostic accuracy and supporting precision oncology. We aimed to develop a framework based on DL, capable of accurate diagnosis of gastric lesions, prognostic stratification, and treatment response prediction of GC.MethodsThis study is a retrospective multicenter study that included 6,876 whole-slide images from 5,249 patients across six centers.A weakly supervised DL model, GastroDiagNet, was developed, validated and visualized in both internal in external cohorts.GastroDiagNet-identified tumor regions were used to extract pathological feature indices via a pre-trained model. Based on survival data from 1,031 gastric cancer patients across multiple centers, a multimodal prognostic model was constructed by integrating deep learning-derived pathological features and clinicopathological variables, and independently validated in multi-center cohorts.Single-cell segmentation and classification were performed using HoverNet. Spatial topology features were extracted, and clustering identified tumor microenvironment (TME) cell subpopulations for downstream association analysis with molecular subtypes and treatment response.ResultsGastroDiagNet achieved a mean Accuracy over 0.93 and a mean F1-score above 0.9 in both the internal and external validation cohorts, with diagnostic region concordance ≥0.9 with pathologists (IDDF2025-ABS-0293 figure 1. The GastroDiagNet diagnostic model results).Pathological feature indices extracted from tumor regions—particularly epithelial subtypes—enabled effective prognostic stratification. The multimode survival model achieved a mean concordance index of 0.73 across all cohorts (IDDF2025-ABS-0293 figure 2. The GastroDiagNet prognostic model results).Single-cell classification achieved >0.8 accuracy and revealed distinct TME clusters. These clusters can predict treatment response (all areas under the curves (AUC) >0.74) and GC molecular subtypes (all AUCs >0.98) (IDDF2025-ABS-0293 figure 3. The GastroDiagNet tumor microenvironment TME model results).Abstract IDDF2025-ABS-0293 Figure 1Abstract IDDF2025-ABS-0293 Figure 2Abstract IDDF2025-ABS-0293 Figure 3ConclusionsGastroDiagNet enables accurate gastric lesion diagnosis, prognostic stratification, and treatment response prediction across multiple centers. By integrating pathological and clinical features, it supports precision oncology and demonstrates strong potential for clinical translation in AI-assisted GC management.