Systematic analysis of gene expression alterations and clinical outcomes of STAT3 in cancer

Cancer and Alzheimer's Inverse Correlation: an Immunogenetic Analysis

Numerous studies have demonstrated an inverse link between cancer and Alzheimer's disease (AD), with data suggesting that people with Alzheimer's have a decreased risk of cancer and vice versa. Although other studies have investigated mechanisms to explain this relationship, the connection between these two diseases remains largely unexplained. Processes seen in cancer, such as decreased apoptosis and increased cell proliferation, seem to be reversed in AD. Given the need for effective therapeutic strategies for AD, comparisons with cancer could yield valuable insights into the disease process and perhaps result in new treatments. Here, through a review of existing literature, we compared the expressions of genes involved in cell proliferation and apoptosis to establish a genetic basis for the reciprocal association between AD and cancer. We discuss an array of genes involved in the aforementioned processes, their relevance to both diseases, and how changes in those genes produce varying effects in either disease.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

Deregulated transcription factors and poor clinical outcomes in cancer patients

Transcription factors are a group of proteins, which possess DNA-binding domains, bind to DNA strands of promoters or enhancers, and initiate transcription of genes with cooperation of RNA polymerase and other co-factors. They play crucial roles in regulating transcription during embryogenesis and development. Their physiological status in different cell types is also important to maintain cellular homeostasis. Therefore, any deregulation of transcription factors will lead to the development of cancer cells and tumor progression. Based on their functions in cancer cells, transcription factors could be either oncogenic or tumor suppressive. Furthermore, transcription factors have been shown to modulate cancer stem cells, epithelial-mesenchymal transition (EMT) and drug response; therefore, measuring deregulated transcription factors is hypothesized to predict treatment outcomes of patients with cancers and targeting deregulated transcription factors could be an encouraging strategy for cancer therapy. Here, we summarize the current knowledge of major deregulated transcription factors and their effects on causing poor clinical outcome of patients with cancer. The information presented here will help to predict the prognosis and drug response and to design novel drugs and therapeutic strategies for the treatment of cancers by targeting deregulated transcription factors.

From biobank and data silos into a data commons: convergence to support translational medicine

Background

To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients.

Results

Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community.

Conclusions

Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03147-z.

The impact of STAT3 and phospho-STAT3 expression on the prognosis and clinicopathology of ovarian cancer: a systematic review and meta-analysis

Purpose

STAT3 and p-STAT3 are often overexpressed in various human tumours and participate in cancer development and progression. However, whether STAT3/p-STAT3 expression is associated with clinicopathologic characteristics and has prognostic significance for people suffering from ovarian cancer remains controversial. We conducted a systematic review and meta-analyses to clarify the associations between STAT3/p-STAT3 expression and clinicopathologic characteristics and prognostic factors of ovarian cancer.

Methods

A systematic electronic search in the PubMed, Embase, CNKI, and Wanfang databases was conducted to identify relevant articles published before 3 April 2021. All statistical analyses were performed using Stata 15.1.

Results

We included 16 eligible studies incorporating 1747 ovarian cancer patients. The expression of STAT3/p-STAT3 was upregulated in ovarian cancer samples versus normal ovarian tissue, benign tumours and borderline tumours (OR = 10.14, p < 0.00001; OR = 9.08, P < 0.00001; OR = 4.01, p < 0.00001, respectively). STAT3/p-STAT3 overexpression was significantly correlated with FIGO stage (I-II vs. III-IV) (OR = 0.36, p < 0.00001), tumour grade (G1 + G2 vs. G3) (OR = 0.55; p = 0.001) and lymph node metastasis (yes vs. no) (OR = 3.39; p < 0.00001). High STAT3/p-STAT3 expression was correlated with poorer prognosis of ovarian cancer patients for both overall survival (OS) (HR = 1.67, p < 0.00001) and progression-free survival (PFS) (HR = 1.40, p = 0.007).

Conclusion

The present meta-analysis indicated that high STAT3/p-STAT3 expression is likely predictive of an unfavourable prognosis in ovarian cancer patients. Nonetheless, prospective trials are required to confirm these associations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00918-6.

Nature-Inspired Compressed Sensing for Transcriptomic Profiling From Random Composite Measurements

Transcriptomic profiling is a high-throughput approach to measure gene expression levels under different experimental conditions at different timings. With the development of the related technologies such as single-cell RNA-Seq, the dimensions of gene expression data are increased to hundreds of thousands or more for high-resolution insights. There is a long-lasting challenge in exploiting the relations between transcriptomic profiles and random composite measurements. To address it, we proposed a mathematical framework based on differential evolution (global search) with the help of compressed sensing (local search) termed as DECS. Exploiting the inherent sparse nature of gene expression data, the proposed DECS method can learn the sparse module dictionaries and levels from the low-dimensional random composite measurements for reconstructing the high-dimensional gene expression data with significant orders of magnitude (e.g. 200 × ). Several experiments were conducted to compare DECS with three benchmark methods, demonstrating that the proposed DECS outperforms the benchmark methods and can recover most of the gene expression patterns. The underlying reasons are discussed and illustrated by revealing the related mechanistic insights through extensive benchmarks on nine GSE datasets and their sensitivity analysis.

The Mutational Landscape of Myeloid Leukaemia in Down Syndrome

Children with Down syndrome (DS) are particularly prone to haematopoietic disorders. Paediatric myeloid malignancies in DS occur at an unusually high frequency and generally follow a well-defined stepwise clinical evolution. First, the acquisition of mutations in the GATA1 transcription factor gives rise to a transient myeloproliferative disorder (TMD) in DS newborns. While this condition spontaneously resolves in most cases, some clones can acquire additional mutations, which trigger myeloid leukaemia of Down syndrome (ML-DS). These secondary mutations are predominantly found in chromatin and epigenetic regulators-such as cohesin, CTCF or EZH2-and in signalling mediators of the JAK/STAT and RAS pathways. Most of them are also found in non-DS myeloid malignancies, albeit at extremely different frequencies. Intriguingly, mutations in proteins involved in the three-dimensional organization of the genome are found in nearly 50% of cases. How the resulting mutant proteins cooperate with trisomy 21 and mutant GATA1 to promote ML-DS is not fully understood. In this review, we summarize and discuss current knowledge about the sequential acquisition of genomic alterations in ML-DS.

Expression and Gene Regulation Network of Adenosine Receptor A2B in Lung Adenocarcinoma: A Potential Diagnostic and Prognostic Biomarker

Adenosinereceptor A2B (ADORA2B) encodes a protein belonging to the G protein–coupled receptor superfamily. Abnormal expression of ADORA2B may play a pathophysiological role in some human cancers. We investigated whether ADORA2B is a potential diagnostic and prognostic biomarker for lung adenocarcinoma (LUAD). The expression, various mutations, copy number variations, mRNA expression levels, and related network signaling pathways of ADORA2B were analyzed using bioinformatics-related websites, including Oncomine, UALCAN, cBioPortal, GeneMANIA, LinkedOmics, KM Plotter, and TIMER. We found that ADORA2B was overexpressed and amplified in LUAD, and a high ADORA2B expression predicted a poor prognosis for LUAD patients. Pathway analyses of ADORA2B in LUAD revealed ADORA2B-correlated signaling pathways, and the expression level of ADORA2B was associated with immune cell infiltration. Furthermore, ADORA2B mRNA and protein levels were significantly higher in human LUAD cell lines (A549 cells and NCl-H1299 cells) than in normal human bronchial epithelial (HBE) cells, and the transcript levels of genes positively or negatively correlated with ADORA2B were consistent and statistically significant. siRNA transfection experiments and functional experiments further confirmed these results. In vitro results were also consistent with those of bioinformatics analysis. Our findings provide a foundation for studying the role of ADORA2B in tumorigenesis and support the development of new drug targets for LUAD.

Comprehensive live-cell imaging analysis of cryptotanshinone and synergistic drug-screening effects in various human and canine cancer cell lines

Background

Several studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza, as well as other STAT3 inhibitors to fight cancer.

Methods

Data presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 μM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans.

Results

Results obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure.

Conclusions

CT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival.

PROM1 and PROM2 expression differentially modulates clinical prognosis of cancer: a multiomics analysis

Prominin 1 (PROM1) is considered a biomarker for cancer stem cells, although its biological role is unclear. Prominin 2 (PROM2) has also been associated with certain cancers. However, the prognostic value of PROM1 and PROM2 in cancer is controversial. Here, we performed a systematic data analysis to examine whether prominins can function as prognostic markers in human cancers. The expression of prominins was assessed and their prognostic value in human cancers was determined using univariate and multivariate survival analyses, via various online platforms. We selected a group of prominent functional protein partners of prominins by protein-protein interaction analysis. Subsequently, we investigated the relationship between mutations and copy number alterations in prominin genes and various types of cancers. Furthermore, we identified genes that correlated with PROM1 and PROM2 in certain cancers, based on their levels of expression. Gene ontology and pathway analyses were performed to assess the effect of these correlated genes on various cancers. We observed that PROM1 was frequently overexpressed in esophageal, liver, and ovarian cancers and its expression was negatively associated with prognosis, whereas PROM2 overexpression was associated with poor overall survival in lung and ovarian cancers. Based on the varying characteristics of prominins, we conclude that PROM1 and PROM2 expression differentially modulates the clinical outcomes of cancers.

Prognostic value of key genes of the JAK-STAT signaling pathway in patients with cutaneous melanoma

The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is involved in cell immunity, division and death, as well as in tumor formation. The expression of key genes in the JAK-STAT signaling pathway in different types of cancer serves different roles. However, few reports are available on the prognostic value of the genes of the JAK-STAT signaling pathway in skin cutaneous melanoma (SKCM). The potential prognostic value of gene expression in the JAK-STAT signaling pathway in patients with SKCM was analyzed in the present study using data obtained from The Cancer Genome Atlas. To predict the potential functions and mechanisms of these genes in SKCM, gene set enrichment analysis (GSEA) and bioinformatics analysis were performed. A nomogram model including gene expression level and high risk factors was used to predict the risk level of prognostic. High expression levels of STAT1, STAT3, STAT4 and STAT5B, and low expression levels of STAT6 were associated with favorable prognosis [adjusted P<0.001; hazard ratio (HR), 0.595; 95% confidence interval (CI), 0.455–0.778; adjusted P=0.018; HR, 0.725; 95% CI, 0.555–0.947; adjusted P<0.001; HR, 0.590; 95% CI, 0.450–0.773; adjusted P=0.007; HR, 0.690; 95% CI, 0.526–0.940; and adjusted P=0.026; HR, 0.737, 95% CI, 0.563–0.964, respectively]. GSEA results demonstrated that these genes were involved in cell differentiation, invasion, adhesion, migration, cycle, colony formation and mitogen-activated protein kinase signaling. The combination of genes with favorable prognosis had a better effect on the overall survival (univariate survival analysis, P<0.05). The results of the present study suggest that STAT1, STAT3, STAT4, STAT5B and STAT6 gene expression may be used as a potential prognostic biomarker of SKCM, and the combined outcomes may exhibit a stronger interaction and higher survival time for SKCM.

A Multi-Omics Analysis of Bone Morphogenetic Protein 5 (BMP5) mRNA Expression and Clinical Prognostic Outcomes in Different Cancers Using Bioinformatics Approaches

Cumulative studies have provided controversial evidence for the prognostic values of bone morphogenetic protein 5 (BMP5) in different types of cancers such as colon, breast, lung, bladder, and ovarian cancer. To address the inconsistent correlation of BMP5 expression with patient survival and molecular function of BMP5 in relation to cancer progression, we performed a systematic study to determine whether BMP5 could be used as a prognostic marker in human cancers. BMP5 expression and prognostic values were assessed using different bioinformatics tools such as ONCOMINE, GENT, TCGA, GEPIA, UALCAN, PrognoScan, PROGgene V2 server, and Kaplan-Meier Plotter. In addition, we used cBioPortal database for the identification and analysis of BMP5 mutations, copy number alterations, altered expression, and protein-protein interaction (PPI). We found that BMP5 is frequently down-regulated in our queried cancer types. Use of prognostic analysis showed negative association of BMP5 down-regulation with four types of cancer except for ovarian cancer. The highest mutation was found in the R321*/Q amino acid of BMP5 corresponding to colorectal and breast cancer whereas the alteration frequency was higher in lung squamous carcinoma datasets (>4%). In PPI analysis, we found 31 protein partners of BMP5, among which 11 showed significant co-expression (p-value < 0.001, log odds ratio > 1). Pathway analysis of differentially co-expressed genes with BMP5 in breast, lung, colon, bladder and ovarian cancers revealed the BMP5-correlated pathways. Collectively, this data-driven study demonstrates the correlation of BMP5 expression with patient survival and identifies the involvement of BMP5 pathways that may serve as targets of a novel biomarker for various types of cancers in human.

Analyses of an epigenetic switch involved in the activation of pioneer factor FOXA1 leading to the prognostic value of estrogen receptor and FOXA1 co-expression in breast cancer

Forkhead box protein A1 (FOXA1) is a pioneer factor of estrogen receptor α (ER)–chromatin binding and function, yet the role of FOXA1 in breast cancer and the underlying molecular mechanisms have not yet been elucidated. To evaluate gene expression alterations during breast carcinogenesis, FOXA1 expression was analyzed using the Serial Analysis of Gene Expression Genie suite, a gene expression profiling interactive analysis, and Oncomine analyses. The correlation between methylation and expression was analyzed using the MEXPRESS tool and UCSC Xena browser. Then, the expression and prognostic value of FOXA1 was validated by our own breast cancer samples using RT-PCR. We obtained the following important results. (1) The expression level of FOXA1 was significantly higher in breast cancer than normal tissues. (2) ER, PR, HEGR-2, and nodal status were positively correlated with FOXA1 expression. (3) Among patients with ER+ tumors, those with higher FOXA1 expression levels had better survival probabilities. (4) The major mutation type in FOXA1 in breast cancer samples was missense mutations. (5) FOXA1 expression was significantly higher in ER+ breast tumors than in ER− tumors or normal tissues. Our findings suggest that the aberrant DNA hypomethylation of promoter regions is one mechanism underlying the aberrant expression of FOXA1 in ER+ breast cancer, which might be a potential indicator of favorable prognosis.

Screening and identification of key biomarkers in bladder carcinoma: Evidence from bioinformatics analysis

Bladder cancer (BC) is one of the most common urogenital malignancies. However, present studies of its multiple gene interaction and cellular pathways remain unable to accurately verify the genesis and the development of BC. The aim of the present study was to investigate the genetic signatures of BC and identify its potential molecular mechanisms. The gene expression profiles of GSE31189 were downloaded from the Gene Expression Omnibus database. The GSE31189 dataset contained 92 samples, including 52 BC and 40 non-cancerous urothelial cells. To further examine the biological functions of the identified differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein-protein interaction (PPI) network was mapped using Cytoscape software. In total, 976 DEGs were identified in BC, including 457 upregulated genes and 519 downregulated genes. GO and KEGG pathway enrichment analyses indicated that upregulated genes were significantly enriched in the cell cycle and the negative regulation of the apoptotic process, while the downregulated genes were mainly involved in cell proliferation, cell adhesion molecules and oxidative phosphorylation pathways (P<0.05). From the PPI network, the 12 nodes with the highest degrees were screened as hub genes; these genes were involved in certain pathways, including the chemokine-mediated signaling pathway, fever generation, inflammatory response and the immune response nucleotide oligomerization domain-like receptor signaling pathway. The present study used bioinformatics analysis of gene profile datasets and identified potential therapeutic targets for BC.