Cell Rep Med. 2026 Oct 2:103085. doi: 10.1016/j.xcrm.2026.103085. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

PMID:42826719 | DOI:10.1016/j.xcrm.2026.103085

Cell Rep Med. 2026 Oct 2:103085. doi: 10.1016/j.xcrm.2026.103085. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

PMID:42826719 | DOI:10.1016/j.xcrm.2026.103085

Int J Biol Macromol. 2026 Sep 25:154618. doi: 10.1016/j.ijbiomac.2026.154618. Online ahead of print.

ABSTRACT

Inonotus obliquus is a medicinal fungus growing on birch bark. A neutral polysaccharide (IOP-W, M_w = 8.222 kDa) was isolated from its crude polysaccharides via sequential DEAE DE-52 cellulose column and Sephadex G-200 gel filtration column chromatography. IOP-W, composed primarily of galactose, glucose, and mannose, was structurally characterized by UV-Vis, FT-IR, GC-MS, and NMR as a glucan containing →4)-α-D-Glcp-(1→, →6)-β-D-Glcp-(1→, →3)-β-D-Glcp-(1→, →4,6)-α-D-Glcp-(1→, terminal α-D-Glcp, and β-D-Glcp reducing end. AFM confirmed its aggregated spherical morphology. IOP-W exerted antitumor activity against MIA PaCa-2 cells by modulating apoptosis and migration. Metabolomics revealed effects on amino acids, alkaloids, lipids, and nucleotides involving 20 pathways, while transcriptomic KEGG analysis showed regulation of MAPK, TNF, and p53 signaling. In vivo investigations employing small animal MRI technology have validated that tumor growth is significantly suppressed in animal models, accompanied by elevated spleen index and improved physiological parameters. Western blotting and immunohistochemistry revealed altered expression of Parp-1, p53, Bax/Bcl-2, p-ERK1/2, p-JNK1, NF-κB, vimentin, and MMP-9. These findings indicate that IOP-W inhibits pancreatic cancer via the p53/MAPK pathway, highlighting its potential as a fungal polysaccharide-based therapeutic candidate.

PMID:42790566 | DOI:10.1016/j.ijbiomac.2026.154618

J Thorac Dis. 2026 Aug 31;18(8):950. doi: 10.21037/jtd-2026-1704. Epub 2026 Aug 28.

ABSTRACT

BACKGROUND AND OBJECTIVE: Traditionally, two primary systems have been employed for staging small cell lung cancer (SCLC): the Veterans Administration Lung Study Group (VALG) system and the International Association for the Study of Lung Cancer (IASLC) tumor, node, metastasis (TNM) system. The term "limited disease" is defined differently: VALG characterizes it as disease encompassed within a single tolerable radiation field, while IASLC defines it as the lack of distant metastases (M0). Patients with N3 disease frequently satisfy VALG extensive-stage (ES) criteria while meeting IASLC limited-stage (LS) criteria, resulting in a notable staging discrepancy. Therefore, this review aims to clarify the clinical challenges posed by this staging overlap and provide insights for standardizing staging terminology and optimizing therapeutic decision-making in N3 SCLC.

METHODS: A narrative review utilizing a systematized search strategy was conducted. While strict adherence to PRISMA guidelines was not pursued because the extensive heterogeneity of the literature precluded a formal meta-analysis, rigorous search criteria were applied to minimize selection bias. Databases including PubMed, Web of Science, Embase, the Cochrane Library, and China National Knowledge Infrastructure (CNKI) were searched for literature from January 2000 to March 2026. Studies examining stage N3 SCLC, spatial metastatic burden, and definitional inconsistencies between the VALG and IASLC staging systems were analyzed to assess their effects on treatment dosimetry, systemic therapy, and survival outcomes.

KEY CONTENT AND FINDINGS: The staging overlap in N3 SCLC leads to heterogeneous clinical management depending on its spatial metastatic burden, and this highly variable cohort can be stratified into distinct prognostic subgroups based on the anatomical distribution (single-region vs. multi-region) of the involved lymph nodes.

CONCLUSIONS: These findings should guide clinical trial design and terminology. Clinical decision-making must transcend historical paradigms and technical constraints. Future strategies must incorporate spatial evaluations of metastatic burden alongside innovative multimodal tools, such as artificial intelligence (AI) and multi-omics, to facilitate tailored therapy for SCLC.

PMID:42724560 | PMC:PMC13559235 | DOI:10.21037/jtd-2026-1704

J Thorac Dis. 2026 Aug 31;18(8):950. doi: 10.21037/jtd-2026-1704. Epub 2026 Aug 28.

ABSTRACT

BACKGROUND AND OBJECTIVE: Traditionally, two primary systems have been employed for staging small cell lung cancer (SCLC): the Veterans Administration Lung Study Group (VALG) system and the International Association for the Study of Lung Cancer (IASLC) tumor, node, metastasis (TNM) system. The term "limited disease" is defined differently: VALG characterizes it as disease encompassed within a single tolerable radiation field, while IASLC defines it as the lack of distant metastases (M0). Patients with N3 disease frequently satisfy VALG extensive-stage (ES) criteria while meeting IASLC limited-stage (LS) criteria, resulting in a notable staging discrepancy. Therefore, this review aims to clarify the clinical challenges posed by this staging overlap and provide insights for standardizing staging terminology and optimizing therapeutic decision-making in N3 SCLC.

METHODS: A narrative review utilizing a systematized search strategy was conducted. While strict adherence to PRISMA guidelines was not pursued because the extensive heterogeneity of the literature precluded a formal meta-analysis, rigorous search criteria were applied to minimize selection bias. Databases including PubMed, Web of Science, Embase, the Cochrane Library, and China National Knowledge Infrastructure (CNKI) were searched for literature from January 2000 to March 2026. Studies examining stage N3 SCLC, spatial metastatic burden, and definitional inconsistencies between the VALG and IASLC staging systems were analyzed to assess their effects on treatment dosimetry, systemic therapy, and survival outcomes.

KEY CONTENT AND FINDINGS: The staging overlap in N3 SCLC leads to heterogeneous clinical management depending on its spatial metastatic burden, and this highly variable cohort can be stratified into distinct prognostic subgroups based on the anatomical distribution (single-region vs. multi-region) of the involved lymph nodes.

CONCLUSIONS: These findings should guide clinical trial design and terminology. Clinical decision-making must transcend historical paradigms and technical constraints. Future strategies must incorporate spatial evaluations of metastatic burden alongside innovative multimodal tools, such as artificial intelligence (AI) and multi-omics, to facilitate tailored therapy for SCLC.

PMID:42724560 | PMC:PMC13559235 | DOI:10.21037/jtd-2026-1704

J Gastrointest Oncol. 2026 Apr 30;17(2):96. doi: 10.21037/jgo-2025-aw-848. Epub 2026 Mar 27.

ABSTRACT

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) is characterized by high postoperative recurrence rates and poor long-term survival despite advances in surgical and systemic therapies. Accurate prediction of postoperative recurrence and survival risk is critical for individualized surveillance, adjuvant treatment selection, and precision management. With the rapid development of artificial intelligence (AI) and medical informatics, multimodal data-driven models integrating clinical, imaging, pathological, and omics information have emerged as a promising paradigm. This narrative review aims to systematically summarize recent advances in multimodal prediction models for postoperative recurrence and survival in HCC, compare modeling strategies and fusion approaches, and discuss current challenges and future directions for clinical translation.

METHODS: A narrative literature review was conducted by searching PubMed, Web of Science, and Google Scholar for studies published between 2020 and 2025. Articles focusing on postoperative recurrence or survival prediction in HCC using single-modal or multimodal data were included. Relevant studies were identified using keywords related to HCC, multimodal data, AI, machine learning, deep learning, recurrence, and prognosis.

KEY CONTENT AND FINDINGS: This review summarizes commonly used data modalities, including clinical variables, medical imaging, pathological features, and multi-omics data, and outlines their respective strengths and limitations. Conventional statistical models and AI-based approaches, including non-deep learning and deep learning algorithms, are compared. Particular emphasis is placed on multimodal fusion strategies at the feature level and decision level, with discussion of their methodological characteristics and suitable clinical scenarios. Overall, multimodal models consistently demonstrate superior predictive performance compared with single-modality approaches. However, key challenges remain, including data heterogeneity, limited interpretability of complex models, insufficient external validation, and the predominance of static baseline modeling.

CONCLUSIONS: Multimodal data-driven prediction models represent a promising strategy for improving postoperative risk stratification and personalized management in HCC. While current evidence highlights their potential advantages over traditional prognostic tools, broader clinical adoption is hindered by methodological limitations and a lack of standardized frameworks. Future research should focus on longitudinal multimodal modeling, multi-center prospective validation, and enhanced model interpretability to facilitate integration into clinical workflows and inform precision oncology-oriented decision-making.

PMID:42169935 | PMC:PMC13187996 | DOI:10.21037/jgo-2025-aw-848

Ann Med. 2026 Dec;58(1):2654291. doi: 10.1080/07853890.2026.2654291. Epub 2026 Apr 17.

ABSTRACT

BACKGROUND: To develop and validate an integrated radiopathomics nomogram combining multiphase CT images, H&E-stained slides, and clinicopathological variables for predicting microvascular patterns (MVPs) in non-small cell lung cancer (NSCLC).

METHODS: We retrospectively included consecutive surgically resected NSCLC patients from two centers (n = 258). Patients from center 1 were randomly divided into training and internal validation cohorts, while patients from center 2 formed external validation cohort. CD34-immunohistochemistry was used as the reference standard for MVPs to classify patients into non-angiogenic alveolar (NAA) and non-NAA groups. Radiomics and pathomics features were extracted to construct single-phase radiomics, combined radiomics, and pathomics models. Rad-score and Path-score were derived from combined radiomics and pathomics models, respectively. Rad-score, Path-score, and clinicopathological independent predictors were integrated to develop a nomogram. Model performance was assessed by area under the curve (AUC), calibration curve, decision curve analysis (DCA), and DeLong test.

RESULTS: On multivariable analysis, histological grade was an independent predictor of NAA MVP. Combined radiomics model for predicting MVPs achieved AUCs of 0.863, 0.856, and 0.849 in training, internal validation, and external validation cohorts, showing better performance than single-phase models. Pathomics model yielded AUCs of 0.878, 0.860, and 0.833, however, its specificity markedly decreased in validation cohorts. Nomogram model achieved the superior performance across all cohorts, with AUCs of 0.911, 0.903, and 0.901, outperforming single-modality models (DeLong test: all p < 0.05).

CONCLUSION: The nomogram demonstrated high accuracy and robustness in predicting MVPs in NSCLC, offering a promising tool for characterizing the tumor microenvironment and supporting individualized treatment.

PMID:41992828 | DOI:10.1080/07853890.2026.2654291

iScience. 2026 Mar 11;29(4):115310. doi: 10.1016/j.isci.2026.115310. eCollection 2026 Apr 17.

ABSTRACT

Small cell lung cancer (SCLC) is a highly aggressive tumor with poor prognosis. Ferroptosis is closely linked to tumor antigen presentation: it affects antigen presentation efficiency via immunostimulatory signals, while CD8⁺ T cell activation induced by antigen presentation promotes tumor cell ferroptosis by secreting IFNγ. This study used multi-omics analyses and machine learning to screen key genes, verified by in vitro/in vivo experiments. TRIM36 and CAMK2N2 were significantly upregulated in SCLC, negatively correlating with patient survival, effector memory CD8⁺ T cell infiltration, and tumor MHC I expression. They suppress SCLC antigen presentation via ferroptosis-dependent/independent mechanisms, limiting T cell function. TRIM36 and CAMK2N2 are promising SCLC biomarkers and therapeutic targets, providing clues to unravel ferroptosis-antigen presentation associations in tumor cells and optimize immunotherapeutic strategies.

PMID:41940332 | PMC:PMC13049528 | DOI:10.1016/j.isci.2026.115310

iScience. 2026 Mar 11;29(4):115310. doi: 10.1016/j.isci.2026.115310. eCollection 2026 Apr 17.

ABSTRACT

Small cell lung cancer (SCLC) is a highly aggressive tumor with poor prognosis. Ferroptosis is closely linked to tumor antigen presentation: it affects antigen presentation efficiency via immunostimulatory signals, while CD8⁺ T cell activation induced by antigen presentation promotes tumor cell ferroptosis by secreting IFNγ. This study used multi-omics analyses and machine learning to screen key genes, verified by in vitro/in vivo experiments. TRIM36 and CAMK2N2 were significantly upregulated in SCLC, negatively correlating with patient survival, effector memory CD8⁺ T cell infiltration, and tumor MHC I expression. They suppress SCLC antigen presentation via ferroptosis-dependent/independent mechanisms, limiting T cell function. TRIM36 and CAMK2N2 are promising SCLC biomarkers and therapeutic targets, providing clues to unravel ferroptosis-antigen presentation associations in tumor cells and optimize immunotherapeutic strategies.

PMID:41940332 | PMC:PMC13049528 | DOI:10.1016/j.isci.2026.115310

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126