Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.

Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNγ. Activation of transforming growth factor β, epithelial–mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33–0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46–0.89; P = 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07–0.89; P = 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.

Cancer associated fibroblasts (CAFs) are key stroma cells that play dominant roles in tumor progression. However, the CAFs-derived molecular determinants that regulate colorectal cancer (CRC) metastasis and chemoresistance have not been fully characterized. CAFs and NFs were obtained from fresh CRC and adjacent normal tissues. Exosomes were isolated from conditioned medium and serum of CRC patients using ultracentrifugation method and ExoQuick Exosome Precipitation Solution kit, and characterized by transmission electronic microscopy, nanosight and western blot. MicroRNA microarray was employed to identify differentially expressed miRNAs in exosomes secreted by CAFs or NFs. The internalization of exosomes, transfer of miR-92a-3p was observed by immunofluorescence. Boyden chamber migration and invasion, cell counting kit-8, flow cytometry, plate colony formation, sphere formation assays, tail vein injection and primary colon cancer liver metastasis assays were employed to explore the effect of NFs, CAFs and exosomes secreted by them on epithelial-mesenchymal transition, stemness, metastasis and chemotherapy resistance of CRC. Luciferase report assay, real-time qPCR, western blot, immunofluorescence, and immunohistochemistry staining were employed to explore the regulation of CRC metastasis and chemotherapy resistance by miR-92a-3p, FBXW7 and MOAP1. CAFs promote the stemness, epithelial-mesenchymal transition (EMT), metastasis and chemotherapy resistance of CRC cells. Importantly, CAFs exert their roles by directly transferring exosomes to CRC cells, leading to a significant increase of miR-92a-3p level in CRC cells. Mechanically, increased expression of miR-92a-3p activates Wnt/β-catenin pathway and inhibits mitochondrial apoptosis by directly inhibiting FBXW7 and MOAP1, contributing to cell stemness, EMT, metastasis and 5-FU/L-OHP resistance in CRC. Clinically, miR-92a-3p expression is significantly increased in CRC tissues and negatively correlated with the levels of FBXW7 and MOAP1 in CRC specimens, and high expression of exosomal miR-92a-3p in serum was highly linked with metastasis and chemotherapy resistance in CRC patients. CAFs secreted exosomes promote metastasis and chemotherapy resistance of CRC. Inhibiting exosomal miR-92a-3p provides an alternative modality for the prediction and treatment of metastasis and chemotherapy resistance in CRC.

Background: XB130 has been reported to be expressed by various types of cells such as thyroid cancer and esophageal cancer cells, and it promotes the proliferation and invasion of thyroid cancer cells. Our previous study demonstrated that XB130 is also expressed in gastric cancer (GC), and that its expression is associated with the prognosis, but the role of XB130 in GC has not been well characterized. Methods: In this study, we investigated the influence of XB130 on gastric tumorigenesis and metastasis in vivo and in vitro using the MTT assay, clonogenic assay, BrdU incorporation assay, 3D culture, immunohistochemistry and immunofluorescence. Western blot analysis was also performed to identify the potential mechanisms involved. Results: The proliferation, migration, and invasion of SGC7901 and MNK45 gastric adenocarcinoma cell lines were all significantly inhibited by knockdown of XB130 using small hairpin RNA. In a xenograft model, tumor growth was markedly inhibited after shXB130-transfected GC cells were implanted into nude mice. After XB130 knockdown, GC cells showed a more epithelial-like phenotype, suggesting an inhibition of the epithelial-mesenchymal transition (EMT) process. In addition, silencing of XB130 reduced the expression of p-Akt/Akt, upregulated expression of epithelial markers including E-cadherin, α-catenin and β-catenin, and downregulated mesenchymal markers including fibronectin and vimentin. Expression of oncoproteins related to tumor metastasis, such as MMP2, MMP9, and CD44, was also significantly reduced. Conclusions: These findings indicate that XB130 enhances cell motility and invasiveness by modulating the EMT-like process, while silencing XB130 in GC suppresses tumorigenesis and metastasis, suggesting that it may be a potential therapeutic target.

Background Durable efficacy of immune checkpoint blockade (ICB) occurred in a small number of patients with metastatic gastric cancer (mGC) and the determinant biomarker of response to ICB remains unclear. Methods We developed an open-source TMEscore R package, to quantify the tumor microenvironment (TME) to aid in addressing this dilemma. Two advanced gastric cancer cohorts (RNAseq, N=45 and NanoString, N=48) and other advanced cancer (N=534) treated with ICB were leveraged to investigate the predictive value of TMEscore. Simultaneously, multi-omics data from The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) and Asian Cancer Research Group (ACRG) were interrogated for underlying mechanisms. Results The predictive capacity of TMEscore was corroborated in patient with mGC cohorts treated with pembrolizumab in a prospective phase 2 clinical trial (NCT02589496, N=45, area under the curve (AUC)=0.891). Notably, TMEscore, which has a larger AUC than programmed death-ligand 1 combined positive score, tumor mutation burden, microsatellite instability, and Epstein-Barr virus, was also validated in the multicenter advanced gastric cancer cohort using NanoString technology (N=48, AUC=0.877). Exploration of the intrinsic mechanisms of TMEscore with TCGA and ACRG multi-omics data identified TME pertinent mechanisms including mutations, metabolism pathways, and epigenetic features. Conclusions Current study highlighted the promising predictive value of TMEscore for patients with mGC. Exploration of TME in multi-omics gastric cancer data may provide the impetus for precision immunotherapy.

Tumour-infiltrating immune cells are a source of important prognostic information for patients with resectable colon cancer. We developed a novel immune model based on systematic assessments of the immune landscape inferred from bulk tumor transcriptomes of stage I–III colon cancer patients. The “Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT)” algorithm was used to estimate the fraction of 22 immune cell types from six microarray public datasets. The random forest method and least absolute shrinkage and selection operator model were then used to establish immunoscores for diagnosis and prognosis. By comparing immune cell compositions in samples of 870 colon cancer patients and 70 normal controls, we constructed a diagnostic model, designated the diagnostic immune risk score (dIRS), that showed high specificity and sensitivity in both the training [area under the curve (AUC) = 0.98, p < 0.001] and validation (AUC 0.96, p < 0.001) sets. We also established a prognostic immune risk score (pIRS) that was found to be an independent prognostic factor for relapse-free survival in every series (training: HR 2.23; validation: HR 1.65; entire: HR 2.01; p < 0.001 for all), which showed better prognostic value than TNM stage. In addition, integration of the pIRS with clinical characteristics in a composite nomogram showed improved accuracy of relapse risk prediction, providing a higher net benefit than TNM stage, with well-fitted calibration curves. The proposed dIRS and pIRS models represent promising novel signatures for the diagnosis and prognosis prediction of colon cancer.