The presence of cancer stem cells (CSCs) contributes significantly to treatment resistance in various cancers, including head and neck squamous cell carcinoma (HNSCC). Despite this, the relationship between cancer stemness and immunity remains poorly understood. In this study, we aimed to identify potential immunotherapeutic targets and sensitive drugs for CSCs in HNSCC. Using data from public databases, we analyzed expression patterns and prognostic values in HNSCC. The stemness index was calculated using the single-sample gene set enrichment analysis (ssgsea) algorithm, and weighted gene co-expression network analysis (WGCNA) was employed to screen for key stemness-related modules. Consensus clustering was then used to group samples for further analysis, and prognosis-related key genes were identified through regression analysis. Our results showed that tumor samples from HNSCC exhibited higher stemness indices compared to normal samples. WGCNA identified a module highly correlated with stemness, comprising 187 genes, which were significantly enriched in protein digestion and absorption pathways. Furthermore, we identified sensitive drugs targeting prognostic genes associated with tumor stemness. Notably, two genes, HLF and CCL11, were found to be highly associated with both stemness and immunity. In conclusion, our study identifies a stemness-related gene signature and promising drug candidates for CSCs of HNSCC. Additionally, HLF and CCL11, which are associated with both stemness and immunity, represent potential targets for immunotherapy in HNSCC.

Colon cancer (COAD) is the third-largest common malignant tumor and the fourth major cause of cancer death in the world. Endoplasmic reticulum (ER) stress has a great influence on cell growth, migration, proliferation, invasion, angiogenesis, and chemoresistance of massive tumors. Although ER stress is known to play an important role in various types of cancer, the prognostic model based on ER stress-related genes (ERSRGs) in colon cancer has not been constructed yet. In this study, we established an ERSRGs prognostic risk model to assess the survival of COAD patients.
The COAD gene expression profile and clinical information data of the training set were obtained from the GEO database (GSE40967) and the test set COAD gene expression profile and clinical informative data were downloaded from the TCGA database. The endoplasmic reticulum stress-related genes (ERSRGs) were obtained from Gene Set Enrichment Analysis (GSEA) website. Differentially expressed ERSRGs between normal samples and COAD samples were identified by R \"limma\" package. Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients. Finally, we verified the function of WFS1 in COAD through in vitro experiments.
We built a 9-gene prognostic risk model based on the univariate, lasso, and multivariate Cox regression analysis. Kaplan-Meier survival analysis and Receiver operating characteristic (ROC) curve revealed that the prognostic risk model has good predictive performance. Subsequently, we screened 60 compounds with significant differences in the estimated half-maximal inhibitory concentration (IC50) between high-risk and low-risk groups. In addition, we found that the ERSRGs prognostic risk model was related to immune cell infiltration and the expression of immune checkpoint molecules. Finally, we determined that knockdown of the expression of WFS1 inhibits the proliferation of colon cancer cells.
The prognostic risk model we built may help clinicians accurately predict the survival of patients with COAD. Our findings provide valuable insights into the role of ERSRGs in COAD and may provide new targets for COAD therapy.

Background: Previous studies have shown that glycosylation of proteins ofen plays an important role in HCC. However, the potential mechanism of glycosylation in HCC has not been described systematically. Methods: We comprehensively evaluated the glycosylation patterns in HCC samples based on 43 glycosylation regulators, and annotated the modification patterns with the enrichment of immune cells and stromal cells. Considering the heterogeneity of HCC patients, the glycosylation score was constructed using single-sample gene set enrichment analysis (ssGSEA). We also explored the drugs that different HCC patients were sensitive to based on glycosylation mode and score. Results: We identified three glycosylation-regulated gene subtypes. By annotating the subtypes, it was found that the glycosylation regulated gene subtypes was highly matched with three immunophenotypes of HCC (immune-inflamed, immune-excluded, and immune-desert), regardless of the characteristics of immune cell infiltration or prognosis. Based on the characteristic genes of glycosylation-regulated gene subtypes, we constructed a glycosylation-related model, and found that glycosylation-related model was highly consistent with the glycosylation regulated gene subtypes. The glycosylation score that evaluates the glycosylation characteristics of a single HCC sample has high prognostic value, and the prognosis of patients with high glycosylation score is significantly worse. Interestingly, we found that the glycosylation score was closely related to tumor node metastasis (TNM) staging. By applying glycosylation-regulated gene subtypes and glycosylation score to explore the sensitivity of different patients to anticancer drugs, it was found that the sensitivity of Thapsigargin, Shikonin, Embelin and Epothilone. B was closely related to the glycosylation mode. Conclusion: This study reveals that the diversity of glycosylation patterns plays an important role in HCC. Therefore, evaluating the glycosylation patterns of patients with HCC will be helpful in identifying the characteristics of immune cell infiltration and selecting accurate treatment methods.