A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

Identification of tumor stemness and immunity related prognostic factors and sensitive drugs in head and neck squamous cell carcinoma

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.

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq

BACKGROUND

The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered.

METHODS

UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types.

RESULTS

A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm.

CONCLUSION

CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

A ferroptosis-related LncRNAs signature for predicting prognoses and screening potential therapeutic drugs in patients with lung adenocarcinoma: A retrospective study

BACKGROUND

Lung adenocarcinoma (LUAD) has a high mortality rate. Ferroptosis is linked to tumor initiation and progression.

AIMS

This study aims to develop prognostic models of ferroptosis-related lncRNAs, evaluate the correlation between differentially expressed genes and tumor microenvironment, and identify prospective drugs for managing LUAD.

METHODS AND RESULTS

In this study, transcriptomic and clinical data were downloaded from the TCGA database, and ferroptosis-related genes were obtained from the FerrDb database. Through correlation analysis, Cox analysis, and the LASSO algorithm for constructing a prognostic model, we found that ferroptosis-related lncRNA-based gene signatures (FLncSig) had a strong prognostic predicting ability in the LUAD patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments reconfirmed that ferroptosis is related to receptor-ligand activity, enzyme inhibitor activity, and the IL-17 signaling pathway. Next, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) algorithms, and pRRophetic were used to predict immunotherapy response and chemotherapy sensitivity. The IMvigor210 cohort was also used to validate the prognostic model. In the tumor microenvironment, Type_II_IFN_Response and HLA were found to be a group of low-risk pathways, while MHC_class_I was a group of high-risk pathways. Patients in the high-risk subgroup had lower TIDE scores. Exclusion, MDSC, CAF, and TAMM2 were significantly and positively correlated with risk scores. In addition, we found 15 potential therapeutic drugs for LUAD. Finally, differential analysis of stemness index based on mRNA expression (mRNAsi) indicated that mRNAsi was correlated with gender, primary tumor (T), distant metastasis (M), and the tumor, node, and metastasis (TNM) stage in LUAD patients.

CONCLUSIONS

In conclusion, the prognostic model based on FLncSig can alleviate the difficulty in predicting the prognosis and immunotherapy of LUAD patients. The identified FLncSig and the screened drugs exhibit potential for clinical application and provide references for the treatment of LUAD.

Deciphering the immune landscape dominated by cancer-associated fibroblasts to investigate their potential in indicating prognosis and guiding therapeutic regimens in high grade serous ovarian carcinoma

Limited immunotherapeutic effect in high-grade serous ovarian carcinoma (HGSOC) propels exploration of the mechanics behind this resistance, which may be partly elucidated by investigating characters of cancer-associated fibroblasts (CAFs), a significant population in HGSOC involved in shaping tumor immune microenvironment. Herein, leveraging gene expression data of HGSOC samples from The Cancer Genome Atlas and Gene Expression Omnibus datasets, we suggested that CAFs detrimentally affected the outcomes of HGSOC patients. Subsequently, we performed weighted gene co-expression network analysis (WGCNA) to identify a CAFs-related module and screened out seven hub genes from this module, all of which were positively correlated with the infiltration of immunosuppressive macrophages. As one of the hub genes, the expression of fibrillin 1 (FBN1) and its relevance to CD206 were further verified by immunohistochemistry staining in HGSOC samples. Meanwhile, we extracted genes that correlated well with CAF signatures to construct a CAFscore. The capacity of the CAFscore as an independent prognostic factor was validated by Cox regression analyses, and its relevance to components as well as signals in the tumor immune microenvironment was also investigated. Under the evaluation by the CAFscore, HGSOC patients with relatively high CAFscore had worse outcomes, activated mesenchymal signaling pathways, and immune checkpoint blockade (ICB) resistance signatures, which was consistent with the fact that non-responders in anti-PD-1 treatment cohorts tended to have higher CAFscore. Besides, the possibility of CAFscore to guide the selection of sensitive chemotherapeutic agents was explored. In conclusion, individualized assessment of the CAFscore could uncover the extent of stroma activation and immunosuppression and inform therapeutic strategies to improve the benefit of therapies.