Background: Firstly, 5-hydroxytryptamine G-protein-coupled receptors (HTGPCRs) are a family of 13 genes associated with cancer progression. Nevertheless, a comprehensive understanding of HTGPCRs in cancer remains largely lacking. Method: We tested the gene expression levels and prognostic values for the HTGPCRs in relation to pan-cancer. A subsequent analysis examined the relationships among HTGPCR expression and clinical characteristics, immune subtypes, stemness scores, tumor microenvironments (TMEs), single-cell analyses, and drug sensitivity. Result: A significant difference in HTGPCR expression was found between normal tissues and tumors. HTR1D/2C expressed higher levels in breast invasive carcinoma (BRCA), colon adenocarcinoma, and liver hepatocellular carcinoma. HTGPCR gene expression was correlated with prognosis in many cancers. HTR1D/2C were associated with poorer overall survival for head and neck squamous cell carcinoma. In addition, HTGPCR expression correlated significantly with the stemness scores of RNA and DNA, TMB, and MSI, as well as stromal and immune scores of pan-cancer patients. Additionally, the expression of HTR2A/2B/7 was correlated significantly with immune cells and immune checkpoint genes in a variety of cancers, such as BRCA, brain lower-grade glioma, and lung adenocarcinoma. Immune regulation and TME were both regulated by HTGPCRs. Using single-cell analysis, we found that the gene set of HTGPCRs correlated with many cancer-related functional states in retinoblastoma. Moreover, drug sensitivity and HTR4 were significantly correlated. Furthermore, we validated results in breast cancer and found knockdown of HTR1D inhibited breast cancer cell growth and metastasis. Conclusion: As prognostic indicators, HTGPCRs hold considerable promise and offer insights into the therapeutic targets for malignancy.

Background: Tumor initiation and progression are closely associated with glycosylation. However, glycosylated molecules have not been the subject of extensive studies as prognostic markers for pancreatic cancer. The objectives of this study were to identify glycosylation-related genes in pancreatic cancer and use them to construct reliable prognostic models. Materials and Methods: The Cancer Genome Atlas and Gene Expression Omnibus databases were used to assess the differential expression of glycosylation-related genes; four clusters were identified based on consistent clustering analysis. Kaplan–Meier analyses identified three glycosylation-related genes associated with overall survival. LASSO analysis was then performed on The Cancer Genome Atlas and International Cancer Genome Consortium databases to identify glycosylation-related signatures. We identified 12 GRGs differently expressed in pancreatic cancer and selected three genes (SEL1L, TUBA1C, and SDC1) to build a prognostic model. Thereafter, patients were divided into high and low-risk groups. Eventually, we performed Quantitative real-time PCR (qRT-PCR) to validate the signature. Results: Clinical outcomes were significantly poorer in the high-risk group than in the low-risk group. There were also significant correlations between the high-risk group and several risk factors, including no-smoking history, drinking history, radiotherapy history, and lower tumor grade. Furthermore, the high-risk group had a higher proportion of immune cells. Eventually, three glycosylation-related genes were validated in human PC cell lines. Conclusion: This study identified the glycosylation-related signature for pancreatic cancer. It is an effective predictor of survival and can guide treatment decisions.

Background: Accumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes.Methods: Immune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups.Result: A total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p < 0.01), higher T stage (p < 0.05), higher clinical stage (p < 0.05), higher pathological grade (p < 0.05), low immune cell infiltration (CD4⁺ T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p < 0.001), 5-fluorouracil (p < 0.001), gemcitabine (p < 0.001), and sorafenib (p < 0.01).Conclusion: Our study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.

The recruitment and polarization of tumor-associated macrophages (TAMs) play a pivotal role in shaping the immunosuppressive tumor microenvironment in breast cancer. Interleukin-1 receptor accessory protein (IL1RAP), a critical co-receptor for IL-1 family cytokines, is emerging as a potential regulator of macrophage function, though its specific role in TAM biology remains to be explained. In this study, we investigated the impact of IL1RAP on macrophage recruitment and M2-like polarization. Initial bioinformatics analysis of public databases revealed a significant correlation between elevated IL1RAP expression in macrophages and signatures of immune suppression and poor prognosis in breast cancer. To functionally validate these findings, we performed IL1RAP knockdown in a murine macrophage cell line. Our results demonstrated that IL1RAP deficiency markedly impaired the migratory capacity of macrophages towards classic chemotactic stimuli. Furthermore, under M2-polarizing conditions, IL1RAP-knockdown macrophages exhibited a significantly attenuated M2 phenotype, as evidenced by the decreased expression of canonical M2 markers (e.g., Arg1, Mrc1) and reduced functional outputs. Collectively, our integrated approach combining bioinformatics and in vitro experimentation identifies IL1RAP as a novel regulator that potentiates both the recruitment and the M2 polarization of macrophages. These findings suggest that targeting the IL1RAP pathway could represent a promising therapeutic strategy for reprogramming the tumor-immune microenvironment by limiting pro-tumoral macrophage infiltration and polarization.

Upstream binding transcription factor (UBTF) is a nuclear transcription factor implicated in ribosome biogenesis, yet its pancancer relevance and immunological associations remain incompletely understood. We integrated datasets from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Human Protein Atlas (HPA), Cancer Cell Line Encyclopedia (CCLE), and cBioPortal databases to characterize UBTF expression, genomic alterations, and prognostic value across 33 cancer types. Immune microenvironment analyses were performed using ESTIMATE and multiple deconvolution algorithms. CRISPR-Cas9–mediated UBTF depletion was conducted in breast invasive carcinoma (BRCA) cell lines to evaluate functional roles. UBTF was broadly upregulated in multiple tumors with recurrent copy number gains. Survival analyses revealed cancer type–dependent prognostic associations. UBTF expression correlated with immune/stromal contexture, checkpoint features, and predicted immunotherapy response. In BRCA, UBTF depletion reduced proliferation and migration while increasing apoptosis. A UBTF-related prognostic signature effectively stratified patient outcomes and was associated with immune infiltration and predicted immunotherapy response. UBTF represents a pancancer biomarker linked to tumor immunity, with validated functional significance in BRCA and potential utility for risk stratification.

Introduction: Given the poor prognosis associated with hepatocellular carcinoma (HCC), identifying reliable biomarkers is critical to aid in prognostication and customized treatment planning. Objective: This study aimed to develop a prognostic gene signature based on anoikis- and autophagy-related genes (AARGs) to predict HCC prognosis. Methods: We analyzed differentially expressed AARGs associated with HCC prognosis using messenger RNA expression profiles and clinicopathological data from the Cancer Genome Atlas Liver Hepatocellular Carcinoma Database. Validation was performed using data from the International Cancer Genome Consortium. AARG signatures were constructed using univariate Cox regression and the least absolute shrinkage and selection operator method. Results: We identified 13 AARGs, of which nine showed significant associations with overall survival (OS). Three AARGs—baculoviral IAP repeat containing 5, mitogen-activated protein kinase 3, and BRI1-associated kinase 1—were selected to establish the prognostic AARGs signature. Statistical analyses confirmed the prognostic capacity of the model. Gene set enrichment analysis was performed to explore potential molecular mechanisms. Further investigations included assessments of clinical parameters, immune landscape, immune checkpoint blockade response, tumor stemness, and chemotherapy sensitivity. Immunohistochemistry was conducted to compare protein expression between normal and tumor tissues. Patients in the high-risk group exhibited advanced tumor stage, shorter survival time, and poorer prognosis, along with anoikis resistance, elevated autophagy, and immunosuppression. The nomogram incorporating the AARG signature showed strong predictive performance for OS. Conclusion: We developed and validated a new AARGs-based signature that effectively predicts prognosis for HCC patients.