Transl Oncol. 2026 Sep 9;73:103020. doi: 10.1016/j.tranon.2026.103020. Online ahead of print.

ABSTRACT

BACKGROUND: The prognostic relevance of inflammatory bowel disease (IBD)-colorectal cancer (CRC) shared host-microbe signatures in non-intestinal epithelial malignancies remains unclear. This study aimed to evaluate the prognostic and biological significance of an IBD-CRC shared host-microbe interactome signature in bladder urothelial carcinoma (BLCA).

METHODS: Gene set variation analysis (GSVA) was used to assess the activity of the IBD-CRC shared signature across The Cancer Genome Atlas (TCGA) pan-cancer solid tumor cohorts, including lung, liver, colorectal, and urinary system tumors. In BLCA, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO)-Cox regression were applied to construct a prognostic risk model, which was validated in independent transcriptomic cohorts. An attention-based multiple instance learning (MIL) model was developed to predict the LASSO-derived high- or low-risk group from H&E whole-slide images (WSIs), using TCGA cases for training and internal validation and an independent institutional cohort of 39 BLCA patients for external validation. Molecular subtype, immune infiltration, immunohistochemistry (IHC), machine learning, single nucleotide variation/copy number variation (SNV/CNV), single-cell/spatial transcriptomics, and WSI-based deep learning analyses were integrated to characterize the biological relevance of the signature.

RESULTS: High GSVA scores were significantly associated with poor prognosis in BLCA. The LASSO-derived high-risk group was enriched in basal/squamous molecular features and exhibited an immune-infiltrated but immunosuppressive tumor microenvironment, characterized by increased immunosuppressive cell infiltration and elevated immune checkpoint expression. Conventional IHC markers supported distinct subtype-related protein phenotypes between risk groups. Single-cell and spatial transcriptomic analyses revealed that malignant cells with high signature activity were enriched in Wnt, Hippo, and cell adhesion pathways. The WSI-based MIL model achieved an area under the curve (AUC) of 0.852 in the internal validation cohort. Machine learning and SNV/CNV analyses further characterized key molecular features associated with the LASSO risk score, including AKR1B1, LY6E, MEST, and others. Pan-cancer characterization of AKR1B1 across multiple malignancies, including lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), and kidney renal clear cell carcinoma (KIRC), revealed cancer-type-specific associations with immunosuppressive microenvironmental features and tumor stemness.

CONCLUSION: The IBD-CRC shared host-microbe signature has significant prognostic value in BLCA and is associated with basal/squamous differentiation, immunosuppressive microenvironmental features, genomic alteration patterns, and malignant cell functional heterogeneity. The integrated multi-omics framework and externally validated pathology AI model provide potential tools for BLCA risk stratification and biological interpretation.

PMID:42715652 | DOI:10.1016/j.tranon.2026.103020

Curr Mol Med. 2026 May 11. doi: 10.2174/0115665240438824260113042223. Online ahead of print.

ABSTRACT

BACKGROUND: 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/ IMP cyclohydrolase(ATIC) is a 64-kDa bifunctional enzyme, 5-aminoimidazole- 4-carboxamide ribonucleotide formyltransferase (AICART) and IMP cyclohydrolase, respectively. catalyzes the last two steps of the purine ab initio biosynthetic pathway. ATIC has been implicated in cancer progression, but its pan-cancer profile and specific prognostic utility in liver hepatocellular carcinoma (LIHC) remain incompletely defined.

METHODS: We analyzed TCGA RNA-seq data across 33 tumor types to assess ATIC expression, diagnostic performance (ROC/AUC), and prognostic associations (OS, DSS, PFI). We correlated ATIC expression with immune infiltration, TMB, MSI, and predicted neoantigen load, and constructed a LIHC-specific prognostic nomogram integrating ATIC and clinicopathologic features. Enrichment analyses (STRING, GO/KEGG, GSEA) and pharmacogenomic correlations (GDSC, CTRP) were performed to explore mechanisms and drug sensitivities.

RESULTS: ATIC was significantly upregulated in 16 tumor types, including LIHC (p<0.001). Pan-cancer ROC analyses showed high diagnostic accuracy in several cancers (examples: CHOL AUC=1.000, LIHC AUC=0.936, LUAD AUC=0.947). High ATIC expression associated with poorer OS in ACC, HNSC, LIHC, and PAAD (eg, LIHC: HR=1.39(1.04-1.85), p=0.028). In LIHC, ATIC correlated with advanced T stage, higher grade, elevated AFP, and shorter OS. Multivariable Cox regression identified ATIC expression and pathological T stage as independent predictors; time-dependent ROC for the LIHC nomogram showed AUCs of 0.711, 0.649, and 0.653 at 1, 3, and 5 years, respectively. GSEA indicated enrichment of PI3K-AKT-mTOR, MYC targets, and cell-cycle pathways in ATIC-high LIHC. High ATIC expression correlated with predicted increased sensitivity to sorafenib, doxorubicin, cisplatin, epothilone, and mitomycin in the TCGA-LIHC cohort.

DISCUSSION: ATIC upregulation across cancers links to tumor progression, immune modulation, and prognosis (LIHC), suggesting oncogenic roles in pan-cancer contexts. TCGA multi-omics show ATIC associates with immune/molecular subtypes, MSI/TMB/neoantigens, and predicts drug sensitivity, indicating diagnostic/prognostic potential.

CONCLUSION: ATIC is broadly upregulated across cancers and functions as an independent prognostic biomarker in LIHC. The ATIC-integrated nomogram shows modest predictive accuracy for LIHC survival. Our results implicate ATIC in oncogenic signaling (PI3K-AKT-mTOR, MYC, and cell-cycle) and suggest ATIC as a candidate biomarker to guide targeted and chemotherapeutic strategies in LIHC. Further in vitro and in vivo validation is warranted.

PMID:42152649 | DOI:10.2174/0115665240438824260113042223

Cancer Lett. 2026 Mar 27;648:218461. doi: 10.1016/j.canlet.2026.218461. Online ahead of print.

ABSTRACT

Tertiary lymphoid structures (TLSs) have emerged as key regulators of anti-tumor immunity and biomarkers for immunotherapy response in liver cancer, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and combined hepatocellular-cholangiocarcinoma (cHCC-iCCA). Advances in single-cell and spatial multi-omics technologies have revealed unprecedented complexity in TLSs, challenging the traditional binary classification of TLSs as simply "good" or "bad". Their functional diversity appears to be shaped by spatiotemporal context, cellular composition, and maturation status. This review provides a comprehensive synthesis of TLSs in liver cancer, employing the Yin-Yang paradigm to navigate their functional dualism and prognostic contradictions through a detailed analysis of their identification, classification, and spatiotemporal interactions within the TME. Mechanistically, we elucidate how TLS functions are orchestrated by complex interactions between tumor cells, immune cell subsets, stromal components, and systemic factors. Within this framework, key metabolic drivers, notably ATP citrate lyase (ACLY), and signaling axes such as cGAS-STING/mTOR have emerged as pivotal regulators of TLS ontogeny. In addition, we evaluate current preclinical animal models and therapeutic strategies for clinical TLS induction. Furthermore, we have discussed the key unanswered questions in the field, including the three-dimensional architecture of TLSs and the mechanisms by which they establish durable immunological memory independent of the primary tumor. Clinically, TLSs exhibit great promise as prognostic and predictive biomarkers, particularly in the context of immune checkpoint blockade and locoregional therapies. Finally, we identify challenges in standardization, mechanistic understanding, and translational applications, providing directions for future research to harness TLSs for improving liver cancer outcomes.

PMID:41905709 | DOI:10.1016/j.canlet.2026.218461