DNA Repair-Based Gene Expression Signature and Distinct Molecular Subtypes for Prediction of Clinical Outcomes in Lung Adenocarcinoma

High Expression of COA6 Is Related to Unfavorable Prognosis and Enhanced Oxidative Phosphorylation in Lung Adenocarcinoma

Mitochondrial metabolism plays an important role in the occurrence and development of cancers. Cytochrome C oxidase assembly factor six (COA6) is essential in mitochondrial metabolism. However, the role of COA6 in lung adenocarcinoma (LUAD) remains unknown. Here we report that the expression of COA6 mRNA and protein were upregulated in LUAD tissues compared with lung normal tissues. We found that COA6 had high sensitivity and specificity to distinguish LUAD tissues from normal lung tissues shown by a receiver operating characteristic (ROC) curve. In addition, our univariate and multivariate Cox regression analysis indicated that COA6 was an independent unfavorable prognostic factor for LUAD patients. Furthermore, our survival analysis and nomogram showed that a high expression of COA6 mRNA was related to the short overall survival (OS) of LUAD patients. Notably, our weighted correlation network analysis (WGCNA) and functional enrichment analysis revealed that COA6 may participate in the development of LUAD by affecting mitochondrial oxidative phosphorylation (OXPHOS). Importantly, we demonstrated that depletion of COA6 could decrease the mitochondrial membrane potential (MMP), nicotinamide adenine dinucleotide (NAD) + hydrogen (H) (NADH), and adenosine triphosphate (ATP) production in LUAD cells (A549 and H1975), hence inhibiting the proliferation of these cells in vitro. Together, our study strongly suggests that COA6 is significantly associated with the prognosis and OXPHOS in LUAD. Hence, COA6 is highly likely a novel prognostic biomarker and therapeutic target of LUAD.

Prognostic value of GPC5 polymorphism rs2352028 and clinical characteristics in Chinese lung cancer patients

Background: GPC5 rs2352028 is associated with the risk of lung cancer, but its relationship with lung cancer prognosis is unclear. Materials & methods: The authors collected blood samples from 888 patients with lung cancer and used a Cox proportional hazards model to analyze the association between prognosis and GPC5 polymorphism rs2352028. Results: GPC5 rs2352028 C > T was associated with a better prognosis. Patients with CT genotype had longer overall survival than those with CC genotype. Additionally, older and early-stage patients with CT + TT genotype had a lower risk of death than those with CC genotype. Conclusion: GPC5 rs2352028 C > T may play a protective role in patients with lung cancer and GPC5 rs2352028 may be a potential genetic marker for lung cancer prognosis.

DNA damage repair gene signature model for predicting prognosis and chemotherapy outcomes in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is prone to metastasis and likely to develop resistance to chemotherapeutic drugs. DNA repair has been reported to be involved in the progression and chemoresistance of LUSC. However, the relationship between LUSC patient prognosis and DNA damage repair genes is still unclear.

METHODS

The clinical information of LUSC patients and tumour gene expression level data were downloaded from the TCGA database. Unsupervised clustering and Cox regression were performed to obtain molecular subtypes and prognosis-related significant genes based on a list including 150 DNA damage repair genes downloaded from the GSEA database. The coefficients determined by the multivariate Cox regression analysis and the expression level of prognosis-related DNA damage repair genes were employed to calculate the risk score, which divided LUSC patients into two groups: the high-risk group and the low-risk group. Immune viability, overall survival, and anticarcinogen sensitivity analyses of the two groups of LUSC patients were performed by Kaplan-Meier analysis with the log rank test, ssGSEA and the pRRophetic package in R software. A time-dependent ROC curve was applied to compare the survival prediction ability of the risk score, which was used to construct a survival prediction model by multivariate Cox regression. The prediction model was used to build a nomogram, the discriminative ability of which was confirmed by C-index assessment, and its calibration was validated by calibration curve analysis. Differentially expressed DNA damage repair genes in LUSC patient tissues were retrieved by the Wilcoxon test and validated by qRT-PCR and IHC.

RESULT

LUSC patients were separated into two clusters based on molecular subtypes, of which Cluster 2 was associated with worse overall survival. A prognostic prediction model for LUSC patients was constructed and validated, and a risk score calculated based on the expression levels of ten DNA damage repair genes was employed. The clinical utility was evaluated by drug sensitivity and immune filtration analyses. Thirteen-one genes were upregulated in LUSC patient samples, and we selected the top four genes that were validated by RT-PCR and IHC.

CONCLUSION

We established a novel prognostic model based on DNA damage repair gene expression that can be used to predict therapeutic efficacy in LUSC patients.

Epithelial-Mesenchymal Transition-Based Gene Signature and Distinct Molecular Subtypes for Predicting Clinical Outcomes in Breast Cancer

Purpose

Regulation of inducers and transcription factor families influence epithelial–mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression.

Methods

Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an independent prognostic indicator was assessed. A nomogram was constructed and validated by calibration analysis and decision curve analysis (DCA).

Results

Five molecular subtypes were characterized based on EMT genes. Patients in Cluster 2 exhibited an activated immune state and a better prognosis. An 11-EMT gene-signature was built to predict breast cancer prognosis. After validation, the signature showed independence and robustness in predicting clinical outcomes of patients. A nomogram combining the RiskScore and pTNM_stage accurately predicted 1-, 2-, 3-, and 5-year survival chance. In comparison with published model, the current model showed a higher area under the curve (AUC).

Conclusion

We characterized five breast cancer subtypes with distinct clinical outcomes and immune status. The study developed an 11-EMT gene-signature as an independent prognostic factor for predicting clinical outcomes of breast cancer.

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer

Background

As one of the main causes leading to female cancer deaths, cervical cancer shows malignant features of local infiltration and invasion into adjacent organs and tissues. This study was designed to categorize novel molecular subtypes according to cervical cancer invasion and screen reliable prognostic markers.

Methods

Invasion-related gene sets and expression profiles of invasion-related genes were collected from the CancerSEA database and The Cancer Genome Atlas (TCGA), respectively. Samples were clustered by nonnegative matrix factorization (NMF) to obtain different molecular subgroups, immune microenvironment characteristics of which were further systematically compared. Limma was employed to screen differentially expressed gene sets in different subtypes, followed by Lasso analysis for dimension reduction. Multivariate and univariate Cox regression analysis was performed to determine prognostic characteristics. The Kaplan-Meier test showed the prognostic differences of patients with different risks. Additionally, receiver operating characteristic (ROC) curves were applied to validate the prognostic model performance. A nomogram model was developed using clinical and prognostic characteristics of cervical cancer, and its prediction accuracy was reflected by calibration curve.

Results

This study filtered 19 invasion-related genes with prognosis significance in cervical cancer and 2 molecular subtypes (C1, C2). Specifically, the C1 subtype had an unfavorable prognosis, which was associated with the activation of the TGF-beta signaling pathway, focal adhesion, and PI3K-Akt signaling pathway. 875 differentially expressed genes were screened, and 8 key genes were finally retained by the dimension reduction analysis. An 8-gene signature was established as an independent factor predictive of the prognosis of cervical cancer. The signature performance was even stronger when combined with N stage.

Conclusion

Based on invasion-related genes, the present study categorized two cervical cancer subtypes with distinct TME characteristics and established an 8-gene marker that can accurately and independently predict the prognosis of cervical cancer.

A Novel DNA Damage Repair-Related Gene Signature for Predicting Glioma Prognosis

Background

Glioma is one of the most prevalent tumors in the central nervous system of adults and shows a poor prognosis. This study aimed to develop a DNA damage repair (DDR)-related gene signature to evaluate the prognosis of glioma patients.

Methods

Differentially expressed genes (DEGs) were extracted based on 276 DDR genes. Then, a gene signature was developed for the survival prediction in glioma patients by means of univariate, multivariate Cox, and least absolute shrinkage and selector operation (Lasso) analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. A total of 693 gliomas from the Chinese Glioma Genome Atlas (CGGA) were used for external validation. In addition, we used glioma tumor tissues for qPCR experiment to verify.

Results

A 12-DDR-related gene signature was identified from the 75 DEGs to stratify the survival risk of glioma patients. The overall survival of high-risk group was significantly shorter than that of low-risk group (P < 0.001). Besides, according to the risk score assessment, patients in high- or low-risk group also had significant correlations with clinicopathological parameters, including age (P < 0.01), grade (P < 0.001), IDH status (P < 0.001) and 1p19q codeletion status (P < 0.001). The nomogram provided favorable C-index and calibration plots. The C-index of training set and verification set was 0.761 and 0.746, respectively, and the calibration curve also showed that both training set and verification set were close to the standard curve. The qPCR results showed that there were significant differences in the expression of some typical DDR-related genes in tumor tissues and paracancer tissues (P_(WEE1)=0.0002, P_(RECQL)=0.0117, P_(RPA1)=0.021, P_(RRM1)=0.0035, P_(PARP4)=0.0006, P_(ELOA)=0.0023).

Conclusion

Our study developed a novel 12 DDR-related gene signature as a practical prognostic predictor for glioma patients. A nomogram combining the signature and clinical parameters was established as an individual clinical prediction tool.

The Relationship between Long Noncoding RNA H19 Polymorphism and the Epidermal Growth Factor Receptor Phenotypes on the Clinicopathological Characteristics of Lung Adenocarcinoma

The aim of the current study is to investigate potential associations among Long Noncoding RNA (LncRNA) H19 single nucleotide polymorphism (SNP) and epidermal growth factor receptor (EGFR) phenotypes on the clinicopathological characteristics of lung adenocarcinoma (LADC). Five loci of LncRNA H19 SNPs (rs217727, rs2107425, rs2839698, rs3024270, and rs3741219) were genotyped by using TaqMan allelic discrimination in 223 LADC patients with wild-type EGFR phenotype and 323 LADC individuals with EGFR mutations. After the statistical analyses, patients with the EGFR mutation were related to a higher distribution frequency of rs217727 SNP CT heterozygote (p = 0.030), and the female population with EGFR mutation demonstrated a higher distribution frequency of rs217727 SNP CT heterozygote (p < 0.001) and rs2107425 CT heterozygote (p = 0.002). In addition, the presence of LncRNA H19 SNP rs217727 T allele (CT + TT) in patients with EGFR wild-type was associated to higher tumor T status (stage III or IV, p = 0.037) and poorer cell differentiation status (poor differentiation, p = 0.012) compared to those EGFR wild-type individuals with LncRNA H19 SNP rs217727 CC allele. Besides, a prominently higher tumor T status was found in subjects with LncRNA H19 SNP rs2107425 T allele (CT + TT) (stage III or IV, p = 0.007) compared to EGFR wild-type LADC individuals with LncRNA CC allele in EGFR wild-type patients. Our findings suggest that the presence of LncRNA H19 SNP rs217727 is related to the EGFR mutation in LADC patients, and the LncRNA H19 SNP rs217727 and rs2107425 are associated with progressed tumor status for LADC patients with EGFR wild-type.