Multi-omics integrated analysis to explore the molecular mechanisms of Xinkai Kujiang formula in treating gastric intestinal metaplasia in rats

By: Jiao Xiang · Yuqin Cheng · Zhaoling Wang · Jing Han · Ling Xiao · Jianping Wu · Yufen Meng · Zhaolai Hua · Bin Lu · Chun Cheng · Junfeng Zhang

10 September 2026 at 18:00

Front Pharmacol. 2026 Aug 26;17:1881703. doi: 10.3389/fphar.2026.1881703. eCollection 2026.

ABSTRACT

BACKGROUND: Gastric intestinal metaplasia (GIM) is a typical precancerous lesion of gastric cancer (PLGC). Previous studies have demonstrated that Xinkai Kujiang formula can effectively alleviate GIM, but its underlying mechanism remains largely unclear.

METHODS: The GIM rat model was established using 2% sodium salicylate and 20 mmol/L sodium deoxycholate, and then the rats were treated with Banxia Xiexin Decoction (BXD) and Xinkai Kujiang Decoction (XKD) for 4 weeks. Multi-omics analyses including 16 S ribosomal RNA gene sequencing, transcriptomics, single-cell RNA sequencing, network pharmacology, and component identification were performed to explore the therapeutic mechanisms of Xinkai Kujiang formula on GIM.

RESULTS: In the model rats, severe gastric mucosal atrophy was observed, characterized by disordered glands and goblet cells. Following intervention with BXD and XKD, gastric mucosal thickness was restored, glandular structures became regularly arranged, and the number of metaplastic goblet cells markedly decreased. Microbiota profiling of gastric mucosa revealed significant enrichment of Lactobacillus and Enterococcus in the model group. These abundances were reduced in the BXD group, and short-chain fatty acid-producing bacteria such as Alistipes and Lachnospira were enriched. In the intestine, opportunistic pathogens like Streptococcus and Enterococcus were enriched in the model group, whereas Corynebacterium and Bifidobacterium were enriched in the XKD group. Transcriptomic analysis presented that BXD upregulated innate immune-related genes in the gastric mucosa, and single-cell RNA sequencing (scRNA-Seq) showed that XKD alleviated GIM by inhibiting the VEGF and HIF-1α pathways, reducing angiogenesis, suppressing inflammatory infiltration, and regulating energy metabolism.

CONCLUSION: BXD and XKD improve gastrointestinal microbiota disorders and metabolic disorders, enhance gastric mucosal immunity, and inhibit the VEGF and HIF-1α pathway. Collectively, these multi-omics data provide novel insights into the therapeutic mechanisms of Xinkai Kujiang formula for GIM.

PMID:42718732 | PMC:PMC13553361 | DOI:10.3389/fphar.2026.1881703

Multi-Omics Biomarker Signatures for Precision Diagnosis and Prognosis in Primary Liver Cancer: A Literature Review

Omics in Hepatocellular

By: Ke Xu · Yu Liu · Yuan Peng · Yanli Zeng · Zhibin Luo

4 September 2026 at 18:00

Biofactors. 2026 Sep-Oct;52(5):e70136. doi: 10.1002/biof.70136.

ABSTRACT

Primary liver cancer (PLC) is a biologically heterogeneous group of malignancies dominated by hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and a smaller subset of combined hepatocellular-cholangiocarcinoma (cHCC-CCA), and its clinical burden remains high because current diagnostic and prognostic tools do not adequately capture molecular diversity. Conventional imaging, serum markers, and histopathological assessment remain insufficient for precise early diagnosis, subtype-resolved classification, and outcome stratification, while tissue and liquid biopsy approaches have expanded the range of analytes available for clinical assessment. Recent studies have identified candidate biomarker signatures across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and circulating layers, suggesting that integrated multi-omics profiling may better represent tumor lineage, clonal evolution, immune context, and therapeutic vulnerability than isolated molecular readouts. However, these layers are not equally mature for clinical use: genomic testing is closest to routine therapeutic application in iCCA, plasma methylation assays are advancing for HCC surveillance augmentation, and many proteomic or metabolomic panels remain validation-stage tools. Their clinical value remains constrained by sampling bias, biospecimen-dependent signal loss, assay standardization, cost, and the need for prospective validation across clinically diverse populations. This narrative review critically synthesizes current evidence on multi-omics biomarker signatures for precision diagnosis and prognosis in primary liver cancer and argues that clinically useful signatures should be question-specific, stage-aware, and specimen-aware rather than universal multi-analyte panels.

PMID:42697859 | PMC:PMC13545153 | DOI:10.1002/biof.70136

Multi-omics analysis and experimental validation uncovers prognosis significance of IKBIP in patients with hepatocellular carcinoma: a multicenter cohort study

Omics in Hepatocellular

By: Yuanjun Jiao · Lingling Guo · Yubin Lu · Mengqing Li · Yanfeng Zhong · Bo Wu · Hanxing Dong · Ting Wang · Erbao Chen

19 March 2026 at 18:00

BMC Gastroenterol. 2026 Mar 19. doi: 10.1186/s12876-026-04756-y. Online ahead of print.

NO ABSTRACT

PMID:41851828 | DOI:10.1186/s12876-026-04756-y

Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis

cs.AI, q-bio.NC updates on arXiv.org

By: Chunlei Meng · Jiabin Luo · Zhenglin Yan · Zhenyu Yu · Rong Fu · Zhongxue Gan · Chun Ouyang

24 February 2026 at 13:00

arXiv:2602.19585v1 Announce Type: cross Abstract: Multimodal Sentiment Analysis (MSA) integrates language, visual, and acoustic modalities to infer human sentiment. Most existing methods either focus on globally shared representations or modality-specific features, while overlooking signals that are shared only by certain modality pairs. This limits the expressiveness and discriminative power of multimodal representations. To address this limitation, we propose a Tri-Subspace Disentanglement (TSD) framework that explicitly factorizes features into three complementary subspaces: a common subspace capturing global consistency, submodally-shared subspaces modeling pairwise cross-modal synergies, and private subspaces preserving modality-specific cues. To keep these subspaces pure and independent, we introduce a decoupling supervisor together with structured regularization losses. We further design a Subspace-Aware Cross-Attention (SACA) fusion module that adaptively models and integrates information from the three subspaces to obtain richer and more robust representations. Experiments on CMU-MOSI and CMU-MOSEI demonstrate that TSD achieves state-of-the-art performance across all key metrics, reaching 0.691 MAE on CMU-MOSI and 54.9% ACC-7 on CMU-MOSEI, and also transfers well to multimodal intent recognition tasks. Ablation studies confirm that tri-subspace disentanglement and SACA jointly enhance the modeling of multi-granular cross-modal sentiment cues.

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