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Background: Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. Methods: We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. Results: A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Conclusions: Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.
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Introduction: Colorectal cancer shows high incidence and mortality rates. Immune checkpoint blockade can be used to treat colorectal carcinoma (CRC); however, it shows limited effectiveness in most patients. Methods: To identify patients who may benefit from immunotherapy using immune checkpoint inhibitors, we constructed an immune-related gene prognostic index (IRGPI) for predicting the efficacy of immunotherapy in patients with CRC. Transcriptome datasets and clinical information of patients with CRC were used to identify differential immune-related genes between tumor and para-carcinoma tissue. Using weighted correlation network analysis and Cox regression analysis, the IRGPI was constructed, and Kaplan-Meier analysis was used to evaluate its predictive ability. We also analyzed the molecular and immune characteristics between IRGPI high-and low-risk subgroups, performed sensitivity analysis of ICI treatment, and constructed overall survival-related receiver operating characteristic curves to validate the IRGPI. Finally, IRGPI genes and tumor immune cell infiltration in CRC model mice with orthotopic metastases were analyzed to verify the results. Results: The IRGPI was constructed based on the following 11 hub genes: ADIPOQ, CD36, CCL24, INHBE, UCN, IL1RL2, TRIM58, RBCK1, MC1R, PPARGC1A, and LGALS2. Patients with CRC in the high-risk subgroup showed longer overall survival than those in the low-risk subgroup, which was confirmed by GEO database. Clinicopathological features associated with cancer progression significantly differed between the high- and low-risk subgroups. Furthermore, Kaplan-Meier analysis of immune infiltration showed that the increased infiltration of naïve B cells, macrophages M1, and regulatory T cells and reduced infiltration of resting dendritic cells and mast cells led to a worse overall survival in patients with CRC. The ORC curves revealed that IRGPI predicted patient survival more sensitive than the published tumor immune dysfunction and rejection and tumor inflammatory signature. Discussion: Thus, the low-risk subgroup is more likely to benefit from ICIs than the high-risk subgroup. CRC model mice showed higher proportions of Tregs, M1 macrophages, M2 macrophages and lower proportions of B cells, memory B cell immune cell infiltration, which is consistent with the IRGPI results. The IRGPI can predict the prognosis of patients with CRC, reflect the CRC immune microenvironment, and distinguish patients who are likely to benefit from ICI therapy.
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Neoplasias Colorrectales , Inmunoterapia , Animales , Ratones , Pronóstico , Linfocitos B , Antígenos CD36 , Inhibidores de Puntos de Control Inmunológico/farmacología , Inhibidores de Puntos de Control Inmunológico/uso terapéutico , Neoplasias Colorrectales/genética , Neoplasias Colorrectales/terapia , Microambiente Tumoral/genéticaRESUMEN
Colon cancer poses a great threat to human health. Currently, there is no effective treatment for colon cancer due to its complex causative factors. Immunotherapy has now become a new method for tumor treatment. In this study, 487 DEGs were screened from The Cancer Genome Atlas (TCGA) database and ImmPort database, and GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Hierarchical clustering of all samples revealed a significant correlation between colon cancer and immunity. The weighted gene co-expression network analysis (WGCNA) algorithm was used to identify key gene modules associated with immunity in colon cancer, here, module grey60 showed the highest correlation. A protein-protein interaction (PPI) network was constructed using the STRING database to screen hub genes, and subsequently, 7 immune-related genes the most closely associated with colon cancer were identified by differential expression in cancer and paracancer. Finally, a risk prediction model was developed using least absolute shrinkage and selection operator (LASSO) COX analysis, and the accuracy of the model was validated by GSE14333. This study determined that IRF4 and TNFRSF17 were immune-related genes in colon cancer, providing immune-related prognostic biomarkers for colon cancer.
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Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC) with high immunogenicity. Research on immune-related gene (IRG) is of great significance in ccRCC in identifying new therapeutic targets and improving patient prognosis. In this study, the IRG patterns of ccRCC were investigated and correlated these patterns with tumor microenvironment infiltrating characteristics in immunotherapy. Moreover, an IRG score was constructed to quantify the pattern of individual tumors through the principal component analysis algorithm. Two distinct molecular subtypes (C1 and C2) were identified based on the IRGs expression profile. Subtype C1 was characterized with significantly high level of immune-checkpoint, immune score, stromal score, showed high drug sensitivity in Sorafenib, Sunitinib, Cisplatin, Vinblastine, Vinorelbine, Vorinostat, and Gemcitabine. Cytokine-cytokine receptor pathway, chemokine signaling pathway, and JAK signaling pathways were found enriched in the subtype C1 account for the poor prognosis. Subtype C2 was linked to a better survival outcome. By using the Connective Map database, subtype specific small molecular drugs identified that could facilitate the treatment of ccRCC patients. In addition, A immune index that used to evaluated the immune modification patterns and further validated in the other types RCC dataset, such as papillary renal cell carcinoma (pRCC) and chromophobe renal cell carcinoma (chRCC). Together, this study identified two distinct molecular subtypes with immune index, aid to the treatment of ccRCC and enhancing our cognition of the tumor microenvironment infiltration characterization in ccRCC immunotherapy.