Homologous repair deficiency-associated genes in invasive breast cancer revealed by WGCNA co-expression network analysis and genetic perturbation similarity analysis

BACKGROUND

Homologous repair deficiency (HRD) causes double-strand break repair to be impeded, which is a common driver of carcinogenesis. However, the therapeutic and prognostic potential of HRD in invasive breast cancer (BRCA) has not been fully explored using comprehensive bioinformatics analysis.

MATERIALS AND METHODS

HRD score was defined as the unweighted sum of LOH, TAI, and LST scores and obtained from the previous study according to Theo A et al. To characterize BRCA tumor microenvironment (TME) subtypes, "ConsensusClusterPlus" R package was used to conduct unsupervised clustering. The xCell algorithm was utilized for tumor composition analysis to estimate the TME in TCGA-BRCA. A WGCNA analysis was conducted to uncover the gene coexpression modules and hub genes in the HRD-related gene module of BRCA. The functional enrichment study was carried out using Metascape. A novel analysis pipeline, Genetic Perturbation Similarity Analysis (GPSA), was used to explore the single-gene perturbation closely related to HRD based on 3048 stable knockdown/knockout cell lines. The prognostic variables were evaluated using univariate COX analysis. Kaplan-Meier (KM) survival analysis was performed to assess the prognostic potential of HRD score. Receiver operator characteristic (ROC) curve was utilized to judge the diagnostic utility. Drug sensitivity was estimated through the R package "oncoPredict" and Genomics of Drug Sensitivity in Cancer (GDSC) database. XSum algorithm was performed to screen the candidate small molecule drugs based on the connectivity map (CMAP) database.

RESULTS

Low HRD score suggested a better prognosis in BRCA patients. The tumor with low HRD score had considerably greater degree of infiltration of stromal cells and infiltration of immunocytes was significantly enhanced in the high HRD score group. Using WGCNA, ten co-expression modules were obtained. The turquoise module and 25 hub genes were identified as the most correlated with HRD in BRCA. Functional enrichment analysis revealed that the turquoise gene module was mainly concentrated in the "cell cycle" pathways. Candidate HRD-related gene signatures (MELK) were screened out through WGCNA and GPSA analysis pipeline and then validated on independent validation sets. A small molecule drug (Clofibrate) that has the potential to reverse the increase of high HRD score was screened out to improve oncological outcomes in BRCA. Molecular docking suggested MELK to be one of possible molecular targets in the Clofibrate treatment of BRCA.

CONCLUSION

Based on bioinformatic analysis, we fully explored the therapeutic and prognostic potential of HRD in BRCA. A novel HRD-related gene signature (MELK) were identified through the combination of WGCNA and GPSA analysis. In addition, we detailed the TME landscape in BRCA and identified four unique TME subtypes in group with high or low HRD score group. Moreover, Clofibrate were screened out to improve oncological outcomes in BRCA by reversing the increase of high HRD score. Thus, our study contributes to the development of personalized clinical management and treatment regimens of BRCA.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.

Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.

Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.

Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Prognostic values and clinical relationship of TYK2 in laryngeal squamous cell cancer

ABSTRACT

Laryngeal squamous cell cancer (LSCC) is the second most common head and neck cancer with the increasing mortality. The tyrosine kinase 2 (TYK2) has previously been reported to play an important role in various cancers excepting LSCC. We used available data from the cancer genome atlas program (TCGA), gene expression omnibus, and gene expression profiling interactive analysis (GEPIA) to evaluate the role of TYK2 in LSCC.The difference of TYK2 expression level between normal and tumor samples was analyzed based on TCGA, gene expression omnibus, and GEPIA databases. The relationship between clinical features and TYK2 were analyzed using the Wilcoxon signed-rank test. We applied Cox regression and the Kaplan-Meier method to finding which clinical characteristics is associated with overall survival. Also, we used GEPIA database to validate the relationship between TYK2 and overall survival. At last, we performed gene set enrichment analysis based on TCGA data set.The expression level of TYK2 in LSCC was significantly associated with gender, lymph node status and metastasis (P-values <.05). Kaplan-Meier survival analysis, as same as GEPIA validation, demonstrated that LSCC with TYK2-low had a worse prognosis than that with TYK2-high. The univariate analysis showed that TYK2-high correlated significantly with a better overall survival (hazard ratio: 0.351, 95% confidence interval: 0.194-0.637, P < .001). The multivariate analysis revealed that TYK2 remained independently associated with overall survival (hazard ratio: 0.36, 95% confidence interval: 0.185-0.699, P = .003). Gene set enrichment analysis shows that Janus kinases-STAT signaling pathway, p53 signalling pathway and natural killer cell mediated cytotoxicity, etc are enriched in TYK2 high expression phenotype.Gene TYK2 may be a potential prognostic molecular marker for LSCC. Moreover, the Janus kinases-STAT signaling pathway and p53 signaling pathway are probably the key pathway associated with TYK2 in LC.