Background: Liver hepatocellular carcinoma (LIHC) is one of the leading causes of cancer-related death. The prognostic outcomes of advanced LIHC patients are poor. Hence, reliable prognostic biomarkers for LIHC are urgently needed. Methods: Data for vesicle-mediated transport-related genes (VMTRGs) were profiled from 338 LIHC and 50 normal tissue samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the prognostic risk model. Five GEO datasets were used to validate the risk model. The roles of the differentially expressed genes (DEGs) were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. Differences in immune cell infiltration between the high- and low-risk groups were evaluated using five algorithms. The \"pRRophetic\" was used to calculate the anticancer drug sensitivity of the two groups. Transwell and wound healing assays were performed to assess the role of GDP dissociation inhibitor 2 (GDI2) on LIHC cells. Results: A total of 166 prognosis-associated VMTRGs were identified, and VMTRGs-based risk model was constructed for the prognosis of LIHC patients. Four VMTRGs (GDI2, DYNC1LI1, KIF2C, and RAB32) constitute the principal components of the risk model associated with the clinical outcomes of LIHC. Tumor stage and risk score were extracted as the main prognostic indicators for LIHC patients. The VMTRGs-based risk model was significantly associated with immune responses and high expression of immune checkpoint molecules. High-risk patients were less sensitive to most chemotherapeutic drugs but benefited from immunotherapies. In vitro cellular assays revealed that GDI2 significantly promoted the growth and migration of LIHC cells. Conclusions: A VMTRGs-based risk model was constructed to predict the prognosis of LIHC patients effectively. This risk model was closely associated with the immune infiltration microenvironment. The four key VMTRGs are powerful prognostic biomarkers and therapeutic targets for LIHC.

BACKGROUND: Vesicle-mediated transport, vital for substance exchange and intercellular communication, is linked to tumor initiation and progression. This work was designed to study the role of vesicle-mediated transport-related genes (VMTRGs) in breast cancer (BC)prognosis.
METHODS: Univariate Cox analysis was utilized to screen prognosis-related VMTRGs. BC samples underwent unsupervised clustering based on VMTRGs to analyze survival, clinical factors, and immune cell abundance across different subtypes. We constructed a risk model using univariate Cox and LASSO regression analysis, with validation conducted using GEO datasets. Subsequently, we performed tumor mutational burden analysis, and immune landscape analysis on both groups. Ultimately, we conducted immunophenoscore (IPS) scoring to forecast immunotherapy and performed drug sensitivity analysis.
RESULTS: We identified 102 VMTRGs associated with BC prognosis. Using these 102 VMTRGs, BC patients were classified into 3 subtypes, with Cluster3 patients showing significantly better survival rates. We constructed a prognostic model for BC based on 12 VMTRGs that effectively predicted patient survival. Riskscore was an independent prognostic factor for BC patients. According to median risk score, high-risk group (HRG) had higher TMB values. The immune landscape of the HRG exhibited characteristics of cold tumor, with higher immune checkpoint expression levels and lower IPS scores, whereas Gemcitabine, Nilotinib, and Oxaliplatin were more suitable for treating low-risk group.
CONCLUSIONS: We classified BC subtypes and built a prognostic model based on VMTRGs. The genes in the prognostic model may serve as potential targets for BC therapy.

BACKGROUND: Colorectal cancer (CRC) is impacted by various environmental and genetic variables. Dysregulation of vesicle-mediated transport-related genes (VMTRGs) has been observed in many malignancies, but their effect on prognosis in CRC remains unclear.
METHODS: CRC samples were clustered into varying subtypes per differential expression of VMTRGs. R package was utilized to explore differences in survival, immune, and drug sensitivity among different disease subtypes. According to differentially expressed genes (DEGs) between subtypes, regression analysis was employed to build a riskscore model and identify independent prognostic factors. The model was validated through a Gene Expression Omnibus (GEO) dataset. Immune landscape, immunophenoscore (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores for different risk groups were calculated.
RESULTS: Two subtypes of CRC were identified based on VMTRGs, which showed significant differences in survival rates, immune cell infiltration abundance, immune functional activation levels, and immune checkpoint expression levels. Cluster2 exhibited higher sensitivity to anti-tumor drugs such as Nilotinib, Cisplatin, and Oxaliplatin compared to Cluster1. DEGs were mainly enriched in biological processes such as epidermis development, epidermal cell differentiation, and receptor-ligand activity, and signaling pathways like pancreatic secretion. The constructed 13-gene riskscore model demonstrated good predictive ability for CRC patients\' prognosis. Furthermore, differences in immune landscape, IPS, and TIDE scores were observed among different risk groups.
CONCLUSIONS: This study successfully obtained two CRC subtypes with distinct survival statuses and immune levels based on differential expression of VMTRGs. A 13-gene risk model was constructed. The findings had important implications for prognosis and treatment of CRC.

Globally, lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. It is a progressive disorder that arises from multiple genetic and environmental factors. Dysregulated expression of vesicle-mediated transport-related genes (VMTRGs) have been reported in several cancers. However, the prognostic significance of VMTRGs in LUAD has yet to be established.
The VMTRG profiling data for 482 LUAD patients and 59 normal controls were downloaded from The Cancer Genome Altas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the risk model. Several GEO datasets were used to validate the risk model. The roles of these genes were investigated via the Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses. Differences in immune cell infiltrations between risk groups were evaluated using five algorithms. \"pRRophetic\" was used to investigate anti-cancer drug sensitivities in two groups. Expression of these five genes in LUAD samples and adjacent normal tissues were evaluated by qRT-PCR. Colony formation and wound healing assays were performed to assess the significance of CNIH1 and AP3S1 in LUAD cells.
We identified 85 prognosis-associated VMTRGs that could be constructed a risk model for LUAD patients, indicating their potential importance in LUAD development. The risk model including the five VMTRGs (CNIH1, KIF20A, GALNT2, GRIA1, and AP3S1) was associated with clinical outcomes. Tumor stage and risk score were found to be independent prognostic factors for LUAD patients. The five VMTRGs were also correlated with activation of the Notch and p53 signaling pathways. The risk model was significantly associated with immune responses and with high-level expression of immune checkpoints. High-risk group patients were more sensitive to several chemotherapeutic drugs and Lapatinib. Furthermore, CNIH1 and AP3S1 promoted LUAD cell growth and migration in vitro.
We constructed a VMTRG-based risk model for effective prediction of prognostic outcomes for LUAD patients. The risk model was associated with immune infiltration levels. These five hub genes are potential targets for immune therapy combined with chemotherapy in LUAD.