Low Expression of DDX60 Gene Might Associate with the Radiosensitivity for Patients with Breast Cancer

Deciphering the potential ability of DExD/H-box helicase 60 (DDX60) on the proliferation, diagnostic and prognostic biomarker in pancreatic cancer: a research based on silico, RNA-seq and molecular biology experiment

BACKGROUND

Pancreatic cancer is one of the most malignant abdominal tumors. DDX60 has been shown to be associated with a variety of tumor biological processes. However, DDX60 in pancreatic cancer has not been reported. Our study confirmed that DDX60 can serve as a novel biomarker for diagnosis and treatment of pancreatic cancer.

MATERIALS AND METHODS

We downloaded pancreatic cancer datasets from GEO and TCGA databases, respectively. To investigate the relationship between DDX60 expression and prognosis in pancreatic cancer. GSEA analysis was performed on DDX60. We performed RNA-seq to further explore the downstream biological targets of DDX60 and the signaling pathways that may be involved in pancreatic cancer. Finally, we tested it through molecular biology experiments. First, we constructed the plasmid and tested the plasmid effect by WB. Then MTT assay was performed to explore the effect of DDX60 knockout on the proliferation of pancreatic cancer cells. LDH assay was performed to explore the effect of DDX60 on the release of lactate dehydrogenase from tumor cells. The effect of DDX60 on cell proliferation was further explored by clonal formation experiment. Continue to explore clinical therapeutic drugs sensitive to DDX60 targets.

RESULTS

By analyzing the GSE71729, GSE183795, GSE16515, GSE28735 and GSE62452 data sets, we found that DDX60 was highly expressed in pancreatic cancer. And is associated with poorer outcomes for pancreatic patients. The mRNA expression level of DDX60 was correlated with lymph node metastasis and grade in clinical pancreatic patients. Through the results of RNA-seq analysis, GO and KEGG analysis showed that DDX60 may be associated with cell cycle in pancreatic cancer. Through molecular biology experiments (MTT, LDH, and clonal formation experiment), we found that When DDX60 is knocked down in pancreatic cancer cells, the proliferation ability of tumor cells is significantly decreased. Several drugs targeting about DDX60 have been found, such as JW-7-52-1, this could provide direction for drug therapy against the DDX60 target.

CONCLUSION

In summary, DDX60 can be used as a novel biomarker related to the diagnosis and treatment of pancreatic cancer, participate in tumor proliferation, and is associated with poor prognosis in patients.

KIF26B and CREB3L1 Derived from Immunoscore Could Inhibit the Progression of Ovarian Cancer

Background

Ovarian cancer (OV) is characteristic of high incidence rate and fatality rate in the malignant tumors of female reproductive system. Researches on pathogenesis and therapeutic targets for OV need to be continued. This study mainly analyzed the immune-related pathogenesis and discovered the key immunotherapy targets for OV.

Methods

WGCNA was used for excavating hub gene modules and hub genes related to the immunity of OV. Enrichment analysis was aimed to analyze the related pathways of hub gene modules. Biological experiments were used for exploring the effect of hub genes on SKOV3 cells.

Results

We identified two hub gene modules related to the immunoscore of OV and found that these genes in the modules were related to the extracellular matrix and viral infections. At the same time, we also discovered six hub genes related to the immunity of OV. Among them, KIF26B and CREB3L1 can affect the proliferation, migration, and invasion of SKOV3 cells by the Wnt/β-catenin pathway.

Conclusions

The local infection or inflammation of ovarian may affect the immunity of OV. KIF26B and CREB3L1 are expected to be potential targets for the immunotherapy of OV.

The DEAD-box RNA helicase, DDX60, Suppresses immunotherapy and promotes malignant progression of pancreatic cancer

Excessive proliferation, invasion, metastasis, and immune resistance in pancreatic cancer (PC) makes it one of the most lethal malignant tumors. Recently, DDX60 was found to be involved in the development of various tumors and in immunotherapy. Therefore, we aimed to investigate whether DDX60 is a new factor involved in PC immunotherapy. The DDX60 mRNA was screened using transcriptome sequencing (RNA-seq). The Cox and survival analysis of DDX60 was performed using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. In addition, clinical and immune infiltration data in the databases were analyzed and plotted using the R language. Clinical samples and in vitro experiments were used to determine the molecular evolution of DDX60 during PC progression. We found that DDX60 was upregulated in PC tissues (P value = 0.0083) and was associated with poor prognosis and short survival time of patients with PC. Results of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set variation analyses showed that viral defense, tumor, and immune-related pathways were significantly enriched in samples with high DDX60 expression. The Pearson correlation test demonstrated that DDX60 expression correlated strongly with immune checkpoint and immune system-related metagene clusters. Our results indicated that DDX60 promoted cell proliferation, migration, and invasion and was related to poor prognosis and immune resistance. Therefore, DDX60 may be a promising novel target for PC immunotherapy.

Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer

Immune checkpoint blockade (ICB) therapies induce durable responses in approximately 15% of colorectal cancer (CRC) patients who exhibit microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR). However, more than 80% of CRC patients do not respond to current immunotherapy. The main challenge with these patients is lack of MHC-I signaling to unmask their cancer cells so the immune cells can detect them. Here, we started by comparing IFNγ signature genes and MHC-I correlated gene lists to determine the potential candidates for MHC-I regulators. Then, the protein expression level of listed potential candidates in normal and cancer tissue was compared to select final candidates with enough disparity between the two types of tissues. ISG15 and DDX60 were further tested by wet-lab experiments. Overexpression of DDX60 upregulated the expression of MHC-I, while knockdown of DDX60 reduced the MHC-I expression in CRC cells. Moreover, DDX60 was downregulated in CRC tissues, and lower levels of DDX60 were associated with a poor prognosis. Our data showed that DDX60 could regulate MHC-I expression in CRC; thus, targeting DDX60 may improve the effects of immunotherapy in some patients.

DDX60 selectively reduces translation off viral type II internal ribosome entry sites

Co-opting host cell protein synthesis is a hallmark of many virus infections. In response, certain host defense proteins limit mRNA translation globally, albeit at the cost of the host cell's own protein synthesis. Here, we describe an interferon-stimulated helicase, DDX60, that decreases translation from viral internal ribosome entry sites (IRESs). DDX60 acts selectively on type II IRESs of encephalomyocarditis virus (EMCV) and foot and mouth disease virus (FMDV), but not by other IRES types or by 5' cap. Correspondingly, DDX60 reduces EMCV and FMDV (type II IRES) replication, but not that of poliovirus or bovine enterovirus 1 (BEV-1; type I IRES). Furthermore, replacing the IRES of poliovirus with a type II IRES is sufficient for DDX60 to inhibit viral replication. Finally, DDX60 selectively modulates the amount of translating ribosomes on viral and in vitro transcribed type II IRES mRNAs, but not 5' capped mRNA. Our study identifies a novel facet in the repertoire of interferon-stimulated effector genes, the selective downregulation of translation from viral type II IRES elements.

DNA and RNA Binding Proteins: From Motifs to Roles in Cancer

DNA and RNA binding proteins (DRBPs) are a broad class of molecules that regulate numerous cellular processes across all living organisms, creating intricate dynamic multilevel networks to control nucleotide metabolism and gene expression. These interactions are highly regulated, and dysregulation contributes to the development of a variety of diseases, including cancer. An increasing number of proteins with DNA and/or RNA binding activities have been identified in recent years, and it is important to understand how their activities are related to the molecular mechanisms of cancer. In addition, many of these proteins have overlapping functions, and it is therefore essential to analyze not only the loss of function of individual factors, but also to group abnormalities into specific types of activities in regard to particular cancer types. In this review, we summarize the classes of DNA-binding, RNA-binding, and DRBPs, drawing particular attention to the similarities and differences between these protein classes. We also perform a cross-search analysis of relevant protein databases, together with our own pipeline, to identify DRBPs involved in cancer. We discuss the most common DRBPs and how they are related to specific cancers, reviewing their biochemical, molecular biological, and cellular properties to highlight their functions and potential as targets for treatment.

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma

Background

Interferon plays a crucial role in the pathogenesis and progression of tumors. Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant urinary system tumor. An effective predictive model is required to evaluate the prognosis of patients to optimize treatment.

Materials and Methods

RNA-sequencing data and clinicopathological data from TCGA were involved in this retrospective study. The IFN-γ response genes with significantly different gene expression were screened out. Univariate Cox regression, LASSO regression and multivariate Cox regression were used to establish a new prognostic scoring model for the training group. Survival curves and ROC curves were drawn, and nomogram was constructed. At the same time, we conducted subgroup analysis and experimental verification using our own samples. Finally, we evaluated the relatedness between the prognostic signature and immune infiltration landscapes. In addition, the sensitivity of different risk groups to six drugs and immune checkpoint inhibitors was calculated.

Results

The IFN-γ response-related signature included 7 genes: C1S, IFI44, ST3GAL5, NUP93, TDRD7, DDX60, and ST8SIA4. The survival curves of the training and testing groups showed the model's effectiveness (P = 4.372e-11 and P = 1.08e-08, respectively), the ROC curves showed that the signature was stable, and subgroup analyses showed the wide applicability of the model (P<0.001). Multivariate Cox regression analysis showed that the risk model was an independent prognostic factor of ccRCC. A high-risk score may represent an immunosuppressive microenvironment, while the high-risk group exhibited poor sensitivity to drugs.

Conclusion

Our findings strongly indicate that the IFN-γ response-related signature can be used as an effective prognostic indicator of ccRCC.

Identification of latent biomarkers in connection with progression and prognosis in oral cancer by comprehensive bioinformatics analysis

BACKGROUND

Oral cancer (OC) is a common and dangerous malignant tumor with a low survival rate. However, the micro level mechanism has not been explained in detail.

METHODS

Gene and miRNA expression micro array data were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and miRNAs (DE miRNAs) were identified by R software. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of genes and genomes (KEGG) pathway analysis were used to assess the potential molecular mechanisms of DEGs. Cytoscape software was utilized to construct protein-protein interaction (PPI) network and miRNA-gene network. Central genes were screened out with the participation of gene degree, molecular complex detection (MCODE) plugin, and miRNA-gene network. Then, the identified genes were checked by The Cancer Genome Atlas (TCGA) gene expression profile, Kaplan-Meier data, Oncomine, and the Human Protein Atlas database. Receiver operating characteristic (ROC) curve was drawn to predict the diagnostic efficiency of crucial gene level in normal and tumor tissues. Univariate and multivariate Cox regression were used to analyze the effect of dominant genes and clinical characteristics on the overall survival rate of OC patients.

RESULTS

Gene expression data of gene expression profiling chip(GSE9844, GSE30784, and GSE74530) were obtained from GEO database, including 199 tumor and 63 non-tumor samples. We identified 298 gene mutations, including 200 upregulated and 98 downregulated genes. GO functional annotation analysis showed that DEGs were enriched in extracellular structure and extracellular matrix containing collagen. In addition, KEGG pathway enrichment analysis demonstrated that the DEGs were significantly enriched in IL-17 signaling pathway and PI3K-Akt signaling pathway. Then, we detected three most relevant modules in PPI network. Central genes (CXCL8, DDX60, EIF2AK2, GBP1, IFI44, IFI44L, IFIT1, IL6, MMP9,CXCL1, CCL20, RSAD2, and RTP4) were screened out with the participation of MCODE plugin, gene degree, and miRNA-gene network. TCGA gene expression profile and Kaplan-Meier analysis showed that high expression of CXCL8, DDX60, IL6, and RTP4 was associated with poor prognosis in OC patients, while patients with high expression of IFI44L and RSAD2 had a better prognosis. The elevated expression of CXCL8, DDX60, IFI44L, RSAD2, and RTP44 in OC was verified by using Oncomine database. ROC curve showed that the mRNA levels of these five genes had a helpful diagnostic effect on tumor tissue. The Human Protein Atlas database showed that the protein expressions of DDX60, IFI44L, RSAD2, and RTP44 in tumor tissues were higher than those in normal tissues. Finally, univariate and multivariate Cox regression showed that DDX60, IFI44L, RSAD2, and RTP44 were independent prognostic indicators of OC.

CONCLUSION

This study revealed the potential biomarkers and relevant pathways of OC from publicly available GEO database, and provided a theoretical basis for elucidating the diagnosis, treatment, and prognosis of OC.

ANKRD22 enhances breast cancer cell malignancy by activating the Wnt/β-catenin pathway via modulating NuSAP1 expression

Breast cancer is one of the most prevalent malignancies in women worldwide. Although great advancements have been achieved in the diagnosis and treatment of breast cancer, the prognosis of patients with breast cancer is still poor due to distal recurrence and metastasis after surgery. This study aimed to assess the role of ankyrin repeat domain 22 (ANKRD22) in the progression of breast cancer and investigate the molecular mechanism. Using immunohistochemistry, we demonstrated that the expression level of ANKRD22 in human breast cancer tissues was significantly higher than that in normal breast tissues. ANKRD22 knockdown inhibited the proliferation, invasion, and epithelial-mesenchymal transition (EMT) of breast cancer cells, as confirmed by BrdU, colony formation, transwell, and immunoblot assays. Immunoblot assays further indicated that ANKRD22 regulated the expression of nucleolar and spindle-associated protein 1 (NuSAP1) and then caused the activation of Wnt/β-catenin signaling pathway. Moreover, overexpression of NUSAP1 reversed the inhibitory effects of ANKRD22 knockdown on the proliferation, invasion, and EMT of breast cancer cells. In summary, this study demonstrated that ANKRD22 enhanced breast cancer cell malignancy by activating the Wnt/β-catenin pathway via modulating NuSAP1 expression, which might shed light on new therapeutic approaches for breast cancer.

More evidence for prediction model of radiosensitivity

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high- and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.