Identification of immune subtypes to guide immunotherapy and targeted therapy in clear cell renal cell carcinoma

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Being a frequent malignant tumor of the genitourinary system, Bladder Urothelial Carcinoma (BLCA) has a poor prognosis. This study focused on identifying and validating prognostic biomarkers utilizing methylation, transcriptomics, and clinical data from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA BLCA) cohort. The impact of altered differentially methylated hallmark pathway genes was subjected to clustering analysis to observe changes in the transcriptional landscape on BLCA patients and identify two subtypes of patients from the TCGA BLCA population where Subtype 2 was associated with the worst prognosis with a p-value of 0.00032. Differential expression and enrichment analysis showed that subtype 2 was enriched in immune-responsive and cancer-progressive pathways, whereas subtype 1 was enriched in biosynthetic pathways. Following, regression and network analyses revealed Epidermal Growth Factor Receptor (EGFR), Fos-related antigen 1 (FOSL1), Nuclear Factor Erythroid 2 (NFE2), ADP-ribosylation factor-like protein 4D (ARL4D), SH3 domain containing ring finger 2 (SH3RF2), and Cadherin 3 (CDH3) genes to be the most significant prognostic gene markers. These genes were used to construct a risk model that separated the BLCA patients into high and low-risk groups. The risk model was also validated in an external dataset by performing survival analysis between high and low-risk groups with a p-value < 0.001 and the result showed the high group was significantly associated with poor prognosis compared to the low group. Single-cell analyses revealed the elevated level of these genes in the tumor microenvironment and associated with immune response. High-grade patients also tend to have a high expression of these genes compared to low-grade patients. In conclusion, this research developed a six-gene signature that is pertinent to the prediction of overall survival (OS) and might contribute to the advancement of precision medicine in the management of bladder cancer.

Construction and validation of a model based on immunogenic cell death-associated lncRNAs to predict prognosis and direct therapy for kidney renal clear cell carcinoma

BACKGROUND

Immunogenic cell death (ICD) is an important part of the antitumor effect, yet the role played by long noncoding RNAs (lncRNAs) remains unclear. We explored the value of ICD-related lncRNAs in tumor prognosis assessment in kidney renal clear cell carcinoma (KIRC) patients to provide a basis for answering the above questions.

METHODS

Data on KIRC patients were obtained from The Cancer Genome Atlas (TCGA) database, prognostic markers were identified, and their accuracy was verified. An application-validated nomogram was developed based on this information. Furthermore, we performed enrichment analysis, tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis, and drug sensitivity prediction to explore the mechanism of action and clinical application value of the model. RT-qPCR was performed to detect the expression of lncRNAs.

RESULTS

The risk assessment model constructed using eight ICD-related lncRNAs provided insight into patient prognoses. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients (p<0.001). The model had good predictive value for different clinical subgroups, and the nomogram constructed based on this model worked well (risk score AUC=0.765). Enrichment analysis revealed that mitochondrial function-related pathways were enriched in the low-risk group. The adverse prognosis of the higher-risk cohort might correspond to a higher TMB. The TME analysis revealed a higher resistance to immunotherapy in the increased-risk subgroup. Drug sensitivity analysis can guide the selection and application of antitumor drugs in different risk groups.

CONCLUSIONS

This prognostic signature based on eight ICD-associated lncRNAs has significant implications for prognostic assessment and treatment selection in KIRC.

Galaxolide and Irgacure 369 are novel environmental androgens

Endocrine disruptors are environmental chemicals that can interfere with the endocrine system. However, research on endocrine disruptors that interfere with androgen's actions is still limited. The purpose of this study is to use in silico computation, i.e., molecular docking to facilitate the identification of environmental androgens. Computational docking was used to study the binding interactions of environmental/industrial compounds with the three dimensional structure of human androgen receptor (AR). Then reporter assay and cell proliferation assay using AR-expressing LNCaP prostate cancer cells were used to determine their in vitro androgenic activity. Animal studies using immature male rats were also carried out to test their in vivo androgenic activity. Two novel environmental androgens were identified. As a photoinitiator, 2-benzyl-2-(dimethylamino)-4'-morpholinobutyrophenone (Irgacure 369, abbreviated as IC-369) is widely used in the packaging and electronics industries. Galaxolide (HHCB) is widely used in the production of perfume, fabric softeners and detergents. We found that both IC-369 and HHCB could activate AR transcriptional activity and promote cell proliferation in AR-sensitive LNCaP cells. Furthermore, IC-369 and HHCB could induce cell proliferation and histological changes of seminal vesicles in immature rats. RNA sequencing and qPCR analysis showed that androgen-related genes in seminal vesicle tissue were up-regulated by IC-369 and HHCB. In conclusion, IC-369 and HHCB are new environmental androgens that bind AR and induce AR transcriptional activity, thereby exerting toxicological effects on the development of male reproductive organs.

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model's reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.