Multi-omics perspectives for gastrointestinal malignancy: A systematic review

Table 1 Characteristics of included articles for this study

Ref.	Study designs	Location	Sample size	Purpose	Outcomes
Qian et al[9], 2024	Prospective cohort	China	813 patients	To investigate the proteomic landscape of EOC and identify potential biomarkers for early detection and monitoring	Study discovered genetic pathways of treatment resistance in EOC, identified eight malignancy-associated proteins as putative biomarkers, and created machine learning models for recurrence prediction
Wang et al[11], 2024	Retrospective cohort	China	1903 patients (925 patients from three public databases and 978 patients from four independent Chinese medical cohorts)	To investigate the role of pyroptosis in tumor immune microenvironment remodeling and its impact on optimizing neoadjuvant immunotherapy for GC	Low pyroptosis risk score suggests a high infiltration of anti-tumor immune cells, good immunotherapy response, and better survival for patients, while pyroptosis risk score can serve as a predictive biomarker for guiding neoadjuvant immunotherapy strategy in GC
Sun et al[12], 2024	Case-control and retrospective cohort	China and United Kingdom	Discovery cohort: 150 CRC cases, 50 controls; validation cohort: 731 CRC cases, 51500 controls	To establish a prediction model for CRC onset by incorporating proteomics, polygenic risk scores, and QCancer-15	Further improvement in accuracy with respect to the prediction of risk for CRC was achieved after integrating the proteomic biomarkers with polygenic risk scores
Wang et al[13], 2024	Literature review	China	N/A	To summarize the biomarkers, molecular mechanisms and therapeutic strategies for liver metastasis in GC	The importance of molecular biomarkers like TP53, HER2, and MET is outlined in guiding precision therapy for liver metastasis. It has proposed a systemic and targeted therapy with genomic profiling that improves survival outcomes
Ai et al[1], 2024	Retrospective cohort	China	515 patients	To investigate gastrointestinal tumor heterogeneity through integrated analysis of multimodal data at transcriptomic, imaging, and immune profiles	Highlighted hub genes (e.g., MAGI2-AS3, MALAT1, and SPARC) linked to tumor behavior and therapeutic targets
Ye et al[14], 2025	Retrospective cohort	China	939 patients	To build a predictive model of CDI for prognosis and treatment in serous ovarian carcinoma	High CDI was associated with poor survival, immunosuppression, and drug resistance. It predicted possible therapy options for high CDI cohorts including trametinib and BMS-754807
Xie et al[15], 2024	Retrospective cohort	China	1340 patients	To discuss the role of BAP1-mediated MAFF deubiquitylation in CRC growth and its implications on considerations of patient outcome, particularly focusing mainly on conceptions of protein stability and potential therapeutic interventions	Low expression of both MAFF and low levels of BAP1 correlate with a poor prognosis and at an advanced disease stage of CRC, while BAP1-MAFF axis emerging as a potential therapeutic target
Cai et al[3], 2024	Retrospective cohort	China	944 participants from four cohorts	To construct a serum lipid metabolic signature towards early detection and prognosis of GC	Serum lipid metabolic signature showed a distinctive performance in effectively discriminating GC patients from healthy donors and was ultra-high effective for early-stage diagnosis and prognosis in GC
Gao et al[8], 2024	Prospective cohort	China	373 patients (training cohort: 93 CRC patients and 96 healthy controls; validation cohort: 89 CRC patients and 95 healthy controls)	To develop a multi-omics liquid biopsy assay for the early detection of CRC	Multi-omics model attained an AUC, AUC of 0.981 during validation, 94.7% specificity, with high detection rates for stage I at 80% and stage II at 89.2% CRC
Yin et al[7], 2024	Retrospective cohort	China	949 patients (discovery: 37 cases; training: 338 cases; test: 328 cases; external validation: 246 cases)	To find out the key proteomic biomarkers in serum extracellular vesicles by validating a machine learning-based diagnostic model for CRC diagnosis	PF4 and AACT were identified as top biomarkers. A random forest model attained AUC between 0.960 and 0.963 in the non-invasive diagnosis of CRC, even in its early stages
Dai et al[2], 2024	Scientific report	China	867 GC tissue samples and 32 normal tissue samples	To investigate ICD and its impact on the tumor microenvironment in GC	Developed ICD-related gene signature for prognosis. LBH was identified as a key gene promoting GC progression via EMT pathway
Matsuoka et al[6], 2024	Review	Japan	N/A	To assess the role of genetic testing and emerging technologies in GI cancer management	Emphasized new technologies in genetic testing (e.g., multi-gene panels, liquid biopsies) and their application in personalized treatment for patients with diverse GI cancers
Mazloomnejad et al[4], 2023	Systematic review	Iran	N/A	To review multi-omics approaches for the identification of upper GI cancers, biomarkers for diagnosis, prognosis, and treatment response	Identified prognostic and diagnostic biomarkers, patient stratifications, and therapeutic targets through omics integration approaches
Kiran et al[19], 2024	Review	India	N/A	To discuss precision medicine advancements in CRC by emphasizing molecular profiling and targeted therapy	Emphasized the importance of biomarkers such as microsatellite instability, KRAS, and targeted therapies like EGFR inhibitors and immune checkpoint inhibitors, emphasizing their transformative role in personalized care
Yu et al[17], 2022	Review	China	455 patients	To assess the role of patient-derived organoid biobanks’ roles in the advancement of personalized medicine for GI cancers	Highlighted that patient-derived organoids accurately reflect tumor features, aiding chemotherapy, radiotherapy, and targeted therapy decisions, with further proposed improvements for biobank protocols and drug screenings
Zhang et al[5], 2024	Review	China	N/A	To explore CRC organoids as models for clinical research, drug screening, and precision medicine	Focus on organoids as reliable models in the study of tumor heterogeneity, therapeutic response, and gene editing, while discussing challenges like culture standardization and clinical translation
Uyar et al[18], 2021	Observational computational analysis	Germany	6775 patients from 21 cancer types	To demonstrate how multi-omics integration with deep learning could be used for cancer subtype classification, prognosis, and treatment response prediction	Presented a deep learning model for integrating multi-omics data that reached high accuracy in the classification of cancer and the prediction of personalized therapy
Xie et al[10], 2024	Review	China	N/A	To review progression in multi-omics studies and the survival mechanisms of circulating tumor cells in cancer metastasis	Discusses the genetic, transcriptomic, proteomic, and metabolomic profiles of circulating tumor cells and emphasizing their role in metastasis, drug resistance as targets for therapy in precision medicine
Bi et al[16], 2024	Review	China	N/A	To discuss the role of intratumoral microbiota in gastrointestinal cancer with a particular focusing on its metabolic effects and biomarker potential for diagnosis, prognosis, and treatment	Emphasizing how intratumoral microbiota influences tumor development, immune response, and resistance to therapy, and considers future perspective through advanced omics and clinical studies towards integrating microbiota-based strategies in precision oncology
Paverd et al[20], 2024	Perspective	United Kingdom and Italy	N/A	To explore strategy for integrating radiological imaging with other multi-omics data to help precision oncology	Discusses challenges and developments in the fusion of imaging-omics data, outlining on deep learning, and multimodal integration as promising for enhanced prediction and improved outcomes in cancer treatment

EOC: Epithelial ovarian cancer; GC: Gastric cancer; CRC: Colorectal cancer; TP53: Tumor protein p53; HER2: Human epidermal growth factor receptor 2; MET: Mesenchymal-epithelial transition; CDI: Cell death index; BAP1: BRCA1-associated protein 1; MAFF: V-maf avian musculoaponeurotic fibrosarcoma oncogene homolog basic leucine zipper transcriptional factor F; AUC: Area under the curve; PF4: Platelet factor 4; AACT: Alpha-1 antichymotrypsin; ICD: Immunogenic cell death; LBH: Limb bud-heart; GI: Gastrointestinal; EMT: Epithelial-mesenchymal transition; KRAS: Kirsten rat sarcoma viral oncogene homolog; EGFR: Epidermal growth factor receptor inhibitors; N/A: Not applicable.