CLDN4 as a Novel Diagnostic and Prognostic Biomarker and Its Association with Immune Infiltrates in Ovarian Cancer

Single‑cell RNA sequencing reveals heterogeneity in ovarian cancer and constructs a prognostic signature for prognostic prediction and immunotherapy

BACKGROUND

Ovarian cancer (OC) is one of the cancers with a high incidence at present, which poses a severe threat to women's health. This study focused on identifying the heterogeneity among malignant epithelial cell OC and constructing an effective prognostic signature to predict prognosis and immunotherapy according to a multidisciplinary study.

METHODS

The InterCNV algorithm was used to identify the heterogeneity of OC based on the scRNA-seq and bulk RNA-seq data. Six algorithms selected EMTscore. An effective prognostic signature was conducted using the COX and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The texting datasets were used to assess the accuracy of the prognostic signature. We evaluated different immune characteristics and immunotherapy response differences among other risk groups.

RESULTS

A prognostic signature including 14 genes was established. The patients in the high-risk group have poor survival outcomes. We also found that the patients in the low-risk group have higher immune cell infiltration, enrichment of immune checkpoints, and immunotherapy response, suggesting that the patients in the low-risk group may be more sensitive to immunotherapy. Finally, the laboratory test results showed that KREMEN2 was identified as a novel biomarker and therapeutic target for OC patients.

CONCLUSIONS

Our study established a GRG signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prediction of prognosis and treatment strategy for OC.

An immune-related exosome signature predicts the prognosis and immunotherapy response in ovarian cancer

BACKGROUND

Cancer-derived exosomes contribute significantly in intracellular communication, particularly during tumorigenesis. Here, we aimed to identify two immune-related ovarian cancer-derived exosomes (IOCEs) subgroups in ovarian cancer (OC) and establish a prognostic model for OC patients based on immune-related IOCEs.

METHODS

The Cancer Genome Atlas (TCGA) database was used to obtain RNA-seq data, as well as clinical and prognostic information. Consensus clustering analysis was performed to identify two IOCEs-associated subgroups. Kaplan-Meier analysis was used to compare the overall survival (OS) between IOCEs-high and IOCEs-low subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the mechanisms and biological effects of differentially expressed genes (DEGs) between the two subtypes. Besides, an IOCE-related prognostic model of OC was constructed by Lasso regression analysis, and the signature was validated using GSE140082 as the validation set.

RESULTS

In total, we obtained 21 differentially expressed IOCEs in OC, and identified two IOCE-associated subgroups by consensus clustering. IOCE-low subgroup showed a favorable prognosis while IOCE-high subgroup had a higher level of immune cell infiltration and immune response. GSEA showed that pathways in cancer and immune response were mainly enriched in IOCE-high subgroup. Thus, IOCE-high subgroup may benefit more in immunotherapy treatment. In addition, we constructed a risk model based on nine IOCE-associated genes (CLDN4, AKT2, CSPG5, ALDOC, LTA4H, PSMA2, PSMA5, TCIRG1, ANO6).

CONCLUSION

We developed a novel stratification system for OV based on IOCE signature, which could be used to estimate the prognosis as well as immunotherapy for OC patient.

Immune-related risk prognostic model for clear cell renal cell carcinoma: Implications for immunotherapy

Clear cell renal cell carcinoma (ccRCC) is associated with complex immune interactions. We conducted a comprehensive analysis of immune-related differentially expressed genes in patients with ccRCC using data from The Cancer Genome Atlas and ImmPort databases. The immune-related differentially expressed genes underwent functional and pathway enrichment analysis, followed by COX regression combined with LASSO regression to construct an immune-related risk prognostic model. The model comprised 4 IRGs: CLDN4, SEMA3G, CAT, and UCN. Patients were stratified into high-risk and low-risk groups based on the median risk score, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, confirming the reliability of the model from various perspectives. Further comparison of immune infiltration, tumor mutation load, and immunophenoscore (IPS) comparison between the 2 groups indicates that the high-risk group could potentially demonstrate a heightened sensitivity towards immunotherapy checkpoints PD-1, CTLA-4, IL-6, and LAG3 in ccRCC patients. The proposed model not only applies to ccRCC but also shows potential in developing into a prognostic model for renal cancer, thus introducing a novel approach for personalized immunotherapy in ccRCC.