Integrative analyses identify a DNA damage repair gene signature for prognosis prediction in lower grade gliomas

The Oncogenic Role of Cyclin-Dependent Kinase Inhibitor 2C in Lower-Grade Glioma

Lower-grade gliomas (LGGs) are slow-growing, indolent tumors that usually affect younger patients and present a therapeutic challenge due to the heterogeneity of their clinical presentation. Dysregulation of cell cycle regulatory factors is implicated in the progression of many tumors, and drugs that target cell cycle machinery have shown efficacy as promising therapeutic approaches. To date, however, no comprehensive study has examined how cell cycle-related genes affect LGG outcomes. The cancer genome atlas (TCGA) data were used as the training set for differential analysis of gene expression and patient outcomes; the Chinese glioma genome atlas (CGGA) was used for validation. Levels of one candidate protein, cyclin-dependent kinase inhibitor 2C (CDKN2C), and its relationship to clinical prognosis were determined using a tissue microarray containing 34 LGG tumors. A nomogram was constructed to model the putative role of candidate factors in LGG. Cell type proportion analysis was performed to evaluate immune cell infiltration in LGG. Various genes encoding cell cycle regulatory factors showed increased expression in LGG and were significantly related to isocitrate dehydrogenase and chromosome arms 1p and 19q mutation status. CDKN2C expression independently predicted the outcome of LGG patients. High M2 macrophage values along with elevated CDKN2C expression were associated with poorer prognosis in LGG patients. CDKN2C plays an oncogenic role in LGG, which is associated with M2 macrophages.

Prediction of immune infiltration and prognosis for patients with urothelial bladder cancer based on the DNA damage repair-related genes signature

Objectives

To analyze the correlations between the expression and effect of DNA damage repair genes and the immune status and clinical outcomes of urothelial bladder cancer (BLCA) patients. In addition, we evaluate the efficacy and value of utilizing the DNA damage repair genes signature as a prognosis model for BLCA.

Methods

Two subtype groups (C1 and C2) were produced based on the varied expression of DNA damage repair genes. Significantly differentiated genes and predicted enriched gene pathways were obtained between the two subtypes. Seven key genes were obtained from the DNA damage repair-related genes and a 7-gene signature prognosis model was established based on the key genes. The efficacy and accuracy of this model in prognosis prediction was evaluated and verified in two independent databases. Also, the difference in biological functions, drug sensitivity, immune infiltration and affinity between the high-risk group and low-risk group was analyzed.

Results

The DNA damage repair gene signature could significantly differentiate the BLCA into two molecular subgroups with varied genetic expression and enriched gene pathways. Seven key genes were screened out from the 232 candidate genes for prognosis prediction and a 7-gene signature prognosis model was established based on them. Two independent patient cohorts (TCGA cohort and GEO cohort) were utilized to validate the efficacy of the prognosis model, which demonstrated an effective capability to differentiate and predict the overall survival of BLCA patients. Also, the high-risk group and low-risk group derived from the 7-gene model exhibited significantly differences in drug sensitivity, immune infiltration status and biological pathways enrichment.

Conclusions

Our established 7-gene signature model based on the DNA damage repair genes could serve as a novel prognosis predictive tool for BLCA. The differentiation of BLCA patients based on the 7-gene signature model may be of great value for the appropriate selection of specific chemotherapy agents and immune-checkpoint blockade therapy administration.

The regulation loop of MARVELD1 interacting with PARP1 in DNA damage response maintains genome stability and promotes therapy resistance of cancer cells

The DNA damage response (DDR) plays crucial roles in cancer prevention and therapy. Poly(ADP-ribose) polymerase 1 (PARP1) mediates multiple signal transduction in the DDR as a master regulator. Uncovering the regulatory factors of PARP1 contributes to a more comprehensive view of tumorigenesis and treatment strategies. Here, we reveal that MARVELD1 acts as a mediator of DDR to perform early events and maintain genome stability. Mechanistically, PARP1 PARylates MARVELD1 at D102, D118 and D130, and in turn, MARVELD1 stabilizes PARP1 by enhancing NAA50-mediated acetylation, thus forming a positive feedback loop. MARVELD1 knockout mice and their embryo fibroblasts exhibit genomic instability and shorter half-life of PARP1. Moreover, MARVELD1 partnering with PARP1 facilitates resistance to genotoxic drugs and disrupts PARP inhibitor (PARPi) effect in PDX model of colorectal cancer (CRC). Overall, our results underline the link between MARVELD1 and PARP1 in therapeutic resistance based on DDR and provide new insights for clinical tumor therapy of PARPi.

Knockdown of hsa_circ_0008922 inhibits the progression of glioma

Background

A glioma is a tumor originating from glial cells in the central nervous system. Although significant progress has been made in diagnosis and treatment, most high-grade glioma patients are prone to recurrence. Therefore, molecular targeted therapy may become a new direction for adjuvant therapy in glioma. In recent years, many studies have revealed that circular RNA (circRNA) may play an important role in the occurrence and development of many tumors including gliomas. Our previous study found that the expression of hsa_circ_0008922 was up-regulated in glioma tissues upon RNA sequencing. The biological mechanism of circ_0008922 is still unreported in gliomas. Therefore, in this study, we preliminarily outlined the expression of hsa_circ_0008922 in glioma and explored its biological functions.

Methods

The expression of hsa_circ_0008922 in forty glioma tissues and four glioma cell lines (A172, U251, SF763 and U87) was detected by quantitative real-time polymerase chain reaction (qRT-PCR). The correlation between hsa_circ_0008922 expression and clinicopathological features of glioma patients was evaluated by Fisher's exact test. To understand the potential function of hsa_circ_0008922 in glioma, we constructed small interfering RNA (siRNA) to hsa_circ_0008922 to downregulate its expression in glioma cell lines A172 and U251. With these hsa_circ_0008922 downregulated cells, a series of assays were carried out as follows. Cell proliferation was detected by CCK8 assay, migration and invasion were determined by wound healing assay and transwell assay, respectively. Colony formation ability was evaluated by plate clonogenic assay. Moreover, flow cytometry combined with Western blot was performed to analyze apoptosis status and the expression of apoptotic related proteins (caspase 3 and caspase 9). Finally, the possible biological pathways and potential miRNA targets of hsa_circ_0008922 were predicted by bioinformatics.

Results

We found that the expression of hsa_circ_0008922 in glioma tissues was 3.4 times higher than that in normal tissues. The expression of has_circ_0008922 was correlated with WHO tumor grade. After down-regulating the expression of hsa_circ_0008922, malignant biological behavior of glioma cells was inhibited, such as cell proliferation, colony formation, migration, and invasion. At the same time, it also induced apoptosis of glioma cells. Predicted analysis by bioinformatics demonstrated that hsa_circ_0008922 may be involved in tumor-related pathways by acting as a molecular sponge for multiple miRNAs (hsa-let-7e-5p, hsa-miR-506-5p, hsa-let-7b-5p, hsa-let-7c-5p and hsa-let-7a-5p). Finally, we integrated our observation to build a circRNA-miRNA-mRNA predictive network.

DNA Damage Response Gene-Based Subtypes Associated With Clinical Outcomes in Early-Stage Lung Adenocarcinoma

DNA damage response (DDR) pathways play a crucial role in lung cancer. In this retrospective analysis, we aimed to develop a prognostic model and molecular subtype based on the expression profiles of DDR-related genes in early-stage lung adenocarcinoma (LUAD). A total of 1,785 lung adenocarcinoma samples from one RNA-seq dataset of The Cancer Genome Atlas (TCGA) and six microarray datasets of Gene Expression Omnibus (GEO) were included in the analysis. In the TCGA dataset, a DNA damage response gene (DRG)–based signature consisting of 16 genes was constructed to predict the clinical outcomes of LUAD patients. Patients in the low-DRG score group had better outcomes and lower genomic instability. Then, the same 16 genes were used to develop DRG-based molecular subtypes in the TCGA dataset to stratify early-stage LUAD into two subtypes (DRG1 and DRG2) which had significant differences in clinical outcomes. The Kappa test showed good consistency between molecular subtype and DRG (K = 0.61, p < 0.001). The DRG subtypes were significantly associated with prognosis in the six GEO datasets (pooled estimates of hazard ratio, OS: 0.48 (0.41–0.57), p < 0.01; DFS: 0.50 (0.41–0.62), p < 0.01). Furthermore, patients in the DRG2 group benefited more from adjuvant therapy than standard-of-care, which was not observed in the DRG1 group. In summary, we constructed a DRG-based molecular subtype that had the potential to predict the prognosis of early-stage LUAD and guide the selection of adjuvant therapy for early-stage LUAD patients.

Polo-like kinases as potential targets and PLK2 as a novel biomarker for the prognosis of human glioblastoma

The most prevalent malignant central nervous system (CNS) cancer is glioblastoma multiforme (GBM). PLKs (polo-like kinases) are a kind of serine-threonine kinase that modulate DNA replication, mitosis, and stress responses. PLKs in GBM need to be better studied and examined in terms of their expression, function, along with prognostic significance. Using an existing publicly available data set, we evaluated the expression level and prognostic relevance of PLKs in GBM patients at the molecular level. The biological processes along with cascades of the screened gene were predicted using the functional enrichment of Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways. The data illustrated that PLK1/3/4 contents were greater in GBM tissues than in non-tumorous tissues, but PLK2/5 expression levels were lower. PLK2 expression was also linked to patient outcome in GBM. Our findings imply that PLKs might be useful molecular indicators as well as prospective treatment targets for GBM. A PLK2 inhibitor has been studied for the first time in a glioma cell in this work. In glioma cells, ON1231320 has anticancer effects. Finally, a summary of PLK inhibitors is presented, along with projections for future progress.

High Expression of C1ORF112 Predicts a Poor Outcome: A Potential Target for the Treatment of Low-Grade Gliomas

Background: Glioma is the most common primary tumor of the central nervous system and is associated with poor overall survival, creating an urgent need to identify survival-associated biomarkers. C1ORF112, an alpha-helical protein, is overexpressed in some cancers; however, its prognostic role has not yet been explored in gliomas. Thus, in this study, we attempted to address this by determining the prognostic value and potential function of C1ORF112 in low-grade gliomas (LGGs).

Methods: The expression of C1ORF112 in normal and tumor tissues was analyzed using data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Oncomine, and Rembrandt databases. The genetic changes of C1ORF112 in LGG were analyzed using cBioPortal. Survival analysis was used to evaluate the relationship between C1ORF112 expression and survival in patients with LGG. Correlation between immune infiltration and C1ORF112 expression was determined using Timer software. Additionally, data from three online platforms were integrated to identify the co-expressed genes of C1ORF112. The potential biological functions of C1ORF112 were investigated by enrichment analysis.

Results: C1ORF112 mRNA was highly expressed in LGGs (p < 0.01). Area under the ROC curve (AUC) showed that the expression of C1ORF112 in LGG was 0.673 (95% confidence interval [CI] = 0.618–0.728). Kaplan-Meier survival analysis showed that patients with high C1ORF112 expression had lower OS than patients with low C1ORF112 expression (p < 0.05). Multivariate analysis showed that high expression of C1ORF112 was an independent prognostic factor for the overall survival in patients from TCGA and CGGA databases. C1ORF112 expression was positively correlated with six immunoinfiltrating cells (all p < 0.001). The enrichment analysis suggested the enrichment of C1ORF112 and co-expressed genes in cell cycle and DNA replication.

Conclusion: This study suggested that C1ORF112 may be a prognostic biomarker and a potential immunotherapeutic target for LGG.

Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.

Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.

Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.

Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.

An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients

Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status can identify patient subsets with different prognosis. However, in the era of precision medicine, there is still a lack of biomarkers that can accurately predict the individual prognosis of each patient. In this study, we found that most DNA damage response (DDR) genes were aberrantly expressed in LGG patients and were associated with their prognosis. Consequently, we developed an artificial neural network (ANN) model based on DDR genes to predict outcomes of LGG glioma patients. Then, we validated the predictive ability in an independent external dataset and found that the concordance indexes and area under time-dependent receiver operating characteristic curves of the predict index (PI) calculated based on the model were superior to those of the mutation markers. Subgroup analyses demonstrated that the model could accurately identify patients with the same mutation status but different prognosis. Moreover, the model can also identify patients with favorable prognostic mutation status but poor prognosis or vice versa. Finally, we also found that the PI was associated with the mutation status and with the altered immune microenvironment. These results demonstrated that the ANN model can accurately predict outcomes of LGG patients and will contribute to individualized therapies. In addition, a web-based application program for the model was developed.

Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma

Molecular prognostic signatures are critical for treatment decision-making in esophageal squamous cell cancer (ESCC), but the robustness of these signatures is limited. The aberrant DNA damage response (DDR) pathway may lead to the accumulation of mutations and thus accelerate tumor progression in ESCC. Given this, we applied the LASSO Cox regression to the transcriptomic data of DDR genes, and a prognostic DDR-related gene expression signature (DRGS) consisting of ten genes was constructed, including PARP3, POLB, XRCC5, MLH1, DMC1, GTF2H3, PER1, SMC5, TCEA1, and HERC2. The DRGS was independently associated with overall survival in both training and validation cohorts. The DRGS achieved higher accuracy than six previously reported multigene signatures for the prediction of prognosis in comparable cohorts. Furtherly, a nomogram incorporating DRGS and clinicopathological features showed improved predicting performance. Taken together, the DRGS was identified as a novel, robust, and effective prognostic indicator, which may refine the scheme of risk stratification and management in ESCC patients.