Promising Gene Therapy Using an Adenovirus Vector Carrying REIC/Dkk-3 Gene for the Treatment of Biliary Cancer

Treatment of congenital coagulopathies, from biologic to biotechnological drugs: The relevance of gene editing (CRISPR/Cas)

Congenital coagulopathies have, throughout the history of medicine, been a focus of scientific study and of great interest as they constitute an alteration of one of the most important and conserved pathways of evolution. The first therapeutic strategies developed to address them were aimed at restoring the blood components lost during hemorrhage by administering whole blood or plasma. Later on, the use of cryoprecipitates was a significant breakthrough as it made it possible to decrease the volumes of blood infused. In the 1970' and 80', clotting factor concentrates became the treatment and, from the 1990's to the present day, recombinant factors -with increasingly longer half-lives- have taken over as the treatment of choice for certain coagulopathies in a seamless yet momentous transition from biological to biotechnological drugs. The beginning of this century, however, saw the emergence of new advanced (gene and cell) treatments, which are currently transforming the therapeutic landscape. The possibility to use cells and viruses as well as specific or bispecific antibodies as medicines is likely to spark a revolution in the world of pharmacology where therapies will be individualized and have long-term effects. Specifically, attention is nowadays focused on the development of gene editing strategies, chiefly those based on CRISPR/Cas technology. Rare coagulopathies such as hemophilia A and B, or even ultra-rare ones such as factor V deficiency, could be among those deriving the greatest benefit from these new developments.

Prediction of lung cancer metastasis by gene expression

Tumor metastasis is the main cause of death in cancer patients. Early prediction of tumor metastasis can allow for timely intervention. At present, research on tumor metastasis mainly focuses on manual diagnosis by imaging or diagnosis by computational methods. With the deterioration of the tumor, gene expression levels in blood change greatly. It is feasible to measure the transcripts of key genes to predict whether cancer will metastasize. Therefore, in this paper, we obtained gene expression data from 226 patients from TCGA. These data included 239,322 transcripts. Background screening and LASSO analysis were used to select 31 transcripts as features. Finally, a deep neural network (DNN) was used to determine whether or not lung cancer would metastasize. We compared our methods with several other methods and found that our method achieved the best precision. In addition, in a previous study, we identified 7 genes that play a vital role in lung cancer. We added those gene transcripts into the DNN and found that the AUC and AUPR of the model were increased.

The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer

As the second leading cause of cancer worldwide, colorectal cancer (CRC) is associated with a poor prognosis. Although recent studies have explored prognostic markers in patients with CRC, whether tissue microbes carry prognostic information remains unknown. Here, by assessing the colorectal tissue microbes of 533 CRC patients, we found that Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%) dominated the colorectal tissue microbiota, which was different from the gut microbiota. Moreover, two clear clusters were obtained by clustering based on the tissue microbes across all samples. By comparison, the relative abundances of Proteobacteria and Bacteroidetes in cluster 1 were significantly higher than those in cluster 2; while compared with cluster 1, Firmicutes and Actinobacteria were more abundant in cluster 2. In addition, the Firmicutes/Bacteroidetes ratios in cluster 1 were significantly lower than those in cluster 2. Further, compared with cluster 2, patients in cluster 1 had relatively poor survival (Log-rank test, p = 0.0067). By correlating tissue microbes with patient survival, we found that the relative abundance of dominant phyla, including Proteobacteria, Firmicutes, and Bacteroidetes, was significantly associated with survival in CRC patients. Besides, the co-occurrence network of tissue microbes at the phylum level of cluster 2 was more complicated than that of cluster 1. Lastly, we detected some pathogenic bacteria enriched in cluster 1 that promote the development of CRC, thus leading to poor survival. In contrast, cluster 2 showed significant increases in the abundance of some probiotics and genera that resist cancer development. Altogether, this study provides the first evidence that the tissue microbiome of CRC patients carries prognostic information and can help design approaches for clinically evaluating the survival of CRC patients.

Engineered Oncolytic Adenoviruses: An Emerging Approach for Cancer Therapy

Cancer is among the major leading causes of mortality globally, and chemotherapy is currently one of the most effective cancer therapies. Unfortunately, chemotherapy is invariably accompanied by dose-dependent cytotoxic side effects. Recently, genetically engineered adenoviruses emerged as an alternative gene therapy approach targeting cancers. This review focuses on the characteristics of genetically modified adenovirus and oncology clinical studies using adenovirus-mediated gene therapy strategies. In addition, modulation of the tumor biology and the tumor microenvironment as well as the immunological responses associated with adenovirus-mediate cancer therapy are discussed.

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.

Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling

Bladder cancer is a highly complex and heterogeneous malignancy. Tumor heterogeneity is a barrier to effective diagnosis and treatment of bladder cancer. Human carcinogenesis is closely related to abnormal gene expression, and DNA methylation is an important regulatory factor of gene expression. Therefore, it is of great significance for bladder cancer research to characterize tumor heterogeneity by integrating genetic and epigenetic characteristics. This study explored specific molecular subtypes based on DNA methylation status and identified subtype-specific characteristics using patient samples from the TCGA database with DNA methylation and gene expression were measured simultaneously. The results were validated using an independent cohort from GEO database. Four DNA methylation molecular subtypes of bladder cancer were obtained with different prognostic states. In addition, subtype-specific DNA methylation markers were identified using an information entropy-based algorithm to represent the unique molecular characteristics of the subtype and verified in the test set. The results of this study can provide an important reference for clinicians to make treatment decisions.

DKK3, Downregulated in Invasive Epithelial Ovarian Cancer, Is Associated with Chemoresistance and Enhanced Paclitaxel Susceptibility via Inhibition of the β-Catenin-P-Glycoprotein Signaling Pathway

Simple Summary

Dickkopf-3 (DKK3) is considered a tumor suppressor as it possesses anti-tumoral properties and is frequently downregulated in various cancers. However, the role of DKK3 in ovarian cancer is not known. In this study, we showed that DKK3 loss occurred in 56.1% of patients with ovarian cancer and that it was significantly associated with poor survival and chemoresistance. Secreted DKK3 possessed anti-tumoral properties and enhanced paclitaxel susceptibility by inhibiting the β-catenin-P-glycoprotein signaling pathway in ovarian cancer. This study revealed promising therapeutic effects of secreted DKK3, which targets paclitaxel-resistant ovarian cancer.

Abstract

Dickkopf-3 (DKK3), a tumor suppressor, is frequently downregulated in various cancers. However, the role of DKK3 in ovarian cancer has not been evaluated. This study aimed to assess aberrant DKK3 expression and its role in epithelial ovarian carcinoma. DKK3 expression was assessed using immunohistochemistry with tissue blocks from 82 patients with invasive carcinoma, and 15 normal, 19 benign, and 10 borderline tumors as controls. Survival data were analyzed using Kaplan–Meier and Cox regression analysis. Paclitaxel-resistant cells were established using TOV-21G and OV-90 cell lines. Protein expression was assessed using Western blotting and immunofluorescence analysis. Cell viability was assessed using the MT assay and 3D-spheroid assay. Cell migration was determined using a migration assay. DKK3 was significantly downregulated in invasive carcinoma compared to that in normal, benign, and borderline tumors. DKK3 loss occurred in 56.1% invasive carcinomas and was significantly associated with disease-free survival and chemoresistance in serous adenocarcinoma. DKK3 was lost in paclitaxel-resistant cells, while β-catenin and P-glycoprotein were upregulated. Exogenous secreted DKK3, incorporated by cells, enhanced anti-tumoral effect and paclitaxel susceptibility in paclitaxel-resistant cells, and reduced the levels of active β-catenin and its downstream P-glycoprotein, suggesting that DKK3 can be used as a therapeutic for targeting paclitaxel-resistant cancer.

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women's health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers. Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data. Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively). Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.

Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.

Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.

Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

Exploration of Lung Cancer-Related Genetic Factors via Mendelian Randomization Method Based on Genomic and Transcriptomic Summarized Data

Lung carcinoma is one of the most deadly malignant tumors in mankind. With the rising incidence of lung cancer, searching for the high effective cures become more and more imperative. There has been sufficient research evidence that living habits and situations such as smoking and air pollution are associated with an increased risk of lung cancer. Simultaneously, the influence of individual genetic susceptibility on lung carcinoma morbidity has been confirmed, and a growing body of evidence has been accumulated on the relationship between various risk factors and the risk of different pathological types of lung cancer. Additionally, the analyses from many large-scale cancer registries have shown a degree of familial aggregation of lung cancer. To explore lung cancer-related genetic factors, Genome-Wide Association Studies (GWAS) have been used to identify several lung cancer susceptibility sites and have been widely validated. However, the biological mechanism behind the impact of these site mutations on lung cancer remains unclear. Therefore, this study applied the Summary data-based Mendelian Randomization (SMR) model through the integration of two GWAS datasets and four expression Quantitative Trait Loci (eQTL) datasets to identify susceptibility genes. Using this strategy, we found ten of Single Nucleotide Polymorphisms (SNPs) sites that affect the occurrence and development of lung tumors by regulating the expression of seven genes. Further analysis of the signaling pathway about these genes not only provides important clues to explain the pathogenesis of lung cancer but also has critical significance for the diagnosis and treatment of lung cancer.