Quantitative multi-Omics analysis of paclitaxel-loaded Poly(lactide-co-glycolide) nanoparticles for identification of potential biomarkers for head and neck cancer
Project description:Discover potential biomarkers of the response for anti-cancer therapies, including traditional Chinese medicine (TCM), is a critical but much different task in the field of cancer research. Based on accumulated data and sophisticated methods, multi-omics analysis provides a feasible strategy for the discovery of potential therapeutic biomarkers. Here, we screened the potential therapeutic biomarkers for anti-cancer compounds in TCM through multi-omics data analysis. Firstly, compounds in TCM were collected from the public databases. Then, the molecules that those compounds can intervene on cell lines were carefully filtered out from existing drug bioactivity datasets. Finally, multi-omics analysis including gene mutation analysis, differential expression gene analysis, copy number variation analysis and clinical survival analysis for pan-cancer were conducted to screen potential therapeutic biomarkers for compounds in TCM. 13 molecules of compounds in TCM namely ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3 and HDAC4 are differentially expressed, high frequently mutated, obtain high copy number variation rate and also significant in survival, are considered as the potential therapeutic biomarkers.
Project description:During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.
Project description:Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein-protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the "Human Protein Atlas" database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.
Project description:BackgroundPapillary thyroid carcinoma (PTC), the most common endocrine cancer, accounts for 80-85% of all malignant thyroid tumors. This study focused on identifying targets that affect the multifocality of PTC. In a previous study, we determined 158 mRNAs related to multifocality in BRAF-mutated PTC using The Cancer Genome Atlas.MethodsWe used multi-omics data (miRNAs and mRNAs) to identify the regulatory mechanisms of the investigated mRNAs. miRNA inhibitors were used to determine the relationship between mRNAs and miRNAs. We analyzed the target protein levels in patient sera using ELISA and immunohistochemical staining of patients' tissues.ResultsWe identified 44 miRNAs that showed a negative correlation with mRNA expression. Using in vitro experiments, we identified four miRNAs that inhibit TEK and/or AXIN2 among the target mRNAs. We also showed that the downregulation of TEK and AXIN2 decreased the proliferation and migration of BRAF ( +) PTC cells. To evaluate the diagnostic ability of multifocal PTC, we examined serum TEK or AXIN2 in unifocal and multifocal PTC patients using ELISA, and showed that the serum TEK in multifocal PTC patients was higher than that in the unifocal PTC patients. The immunohistochemical study showed higher TEK and AXIN2 expression in multifocal PTC than unifocal PTC.ConclusionsBoth TEK and AXIN2 play a potential role in the multifocality of PTC, and serum TEK may be a diagnostic marker for multifocal PTC.
Project description:The pathogenesis of cancer is complicated, and different types of cancer often exhibit different gene mutations resulting in different omics profiles. The purpose of this study was to systematically identify cancer-specific biological pathways and potential cancer-targeting drugs. We collectively analyzed the transcriptomics and proteomics data from 16 common types of human cancer to study the mechanism of carcinogenesis and seek potential treatment. Statistical approaches were applied to identify significant molecular targets and pathways related to each cancer type. Potential anti-cancer drugs were subsequently retrieved that can target these pathways. The number of significant pathways linked to each cancer type ranged from four (stomach cancer) to 112 (acute myeloid leukemia), and the number of therapeutic drugs that can target these cancer related pathways, ranged from one (ovarian cancer) to 97 (acute myeloid leukemia and non-small-cell lung carcinoma). As a validation of our method, some of these drugs are FDA approved therapies for their corresponding cancer type. Our findings provide a rich source of testable hypotheses that can be applied to deconvolute the complex underlying mechanisms of human cancer and used to prioritize and repurpose drugs as anti-cancer therapies.
Project description:Prognostic biomarkers dedicating to treat cancer are very difficult to identify. Although high-throughput sequencing technology allows us to mine prognostic biomarkers much deeper by analyzing omics data, there is lack of effective methods to comprehensively utilize multi-omics data. In this work, we integrated multi-omics data [DNA methylation (DM), gene expression (GE), somatic copy number alternation, and microRNA expression (ME)] and proposed a method to rank genes by desiring a "Score." Applying the method, cancer-specific prognostic biomarkers for 13 cancers were obtained. The prognostic powers of the biomarkers were further assessed by C-indexes (ranged from 0.76 to 0.96). Moreover, by comparing the 13 survival-related gene lists, seven genes (SLK, API5, BTBD2, PTAR1, VPS37A, EIF2B1, and ZRANB1) were found to be associated with prognosis in a variety of cancers. In particular, SLK was more likely to be cancer-related due to its high missense mutation rate and associated with cell adhesion. Furthermore, after network analysis, EPRS, HNRNPA2B1, BPTF, LRRK1, and PUM1 were demonstrated to have a broad correlation with cancers. In summary, our method has a better integration of multi-omics data that can be extended to the researches of other diseases. And the prognostic biomarkers had a better prognostic power than previous methods. Our results could provide a reference for translational medicine researchers and clinicians.
Project description:Breast cancer (BC) is a highly heterogeneous disease with diverse molecular subtypes, which complicates prognosis and treatment. In this study, we performed a multi-omics clustering analysis using the Cancer Integration via MultIkernel LeaRning (CIMLR) method on a large BC dataset from The Cancer Genome Atlas (TCGA) to identify key prognostic biomarkers. We identified three genes-LMO1, PRAME, and RSPO2-that were significantly associated with poor prognosis in both the TCGA dataset and an additional dataset comprising 146 metastatic BC patients. Patients' stratification based on the expression of these three genes revealed distinct subtypes with markedly different overall survival (OS) outcomes. Further validation using almost 2000 BC patients' data from the METABRIC dataset and RNA sequencing data from therapy-resistant cell lines confirmed the upregulation of LMO1 and PRAME, respectively, in patients with worse prognosis and in resistant cells, also suggesting their potential role in drug resistance. Our findings highlight LMO1 and PRAME as potential biomarkers for identifying high-risk BC patients and informing targeted treatment strategies. This study provides valuable insights into the multi-omics landscape of BC and underscores the importance of personalized therapeutic approaches based on molecular profiles.
Project description:Epigenetic mechanisms such as DNA methylation or histone modifications are essential for the regulation of gene expression and development of tissues. Alteration of epigenetic modifications can be used as an epigenetic biomarker for diagnosis and as promising targets for epigenetic therapy. A recent study explored cancer-cell specific epigenetic biomarkers by examining different types of epigenetic modifications simultaneously. However, it was based on microarrays and reported biomarkers that were also present in normal cells at a low frequency. Here, we first analyzed multi-omics data (including ChIP-Seq data of six types of histone modifications: H3K27ac, H3K4me1, H3K9me3, H3K36me3, H3K27me3, and H3K4me3) obtained from 26 lung adenocarcinoma cell lines and a normal cell line. We identified six genes with both H3K27ac and H3K4me3 histone modifications in their promoter regions, which were not present in the normal cell line, but present in ≥85% (22 out of 26) and ≤96% (25 out of 26) of the lung adenocarcinoma cell lines. Of these genes, NUP210 (encoding a main component of the nuclear pore complex) was the only gene in which the two modifications were not detected in another normal cell line. RNA-Seq analysis revealed that NUP210 was aberrantly overexpressed among the 26 lung adenocarcinoma cell lines, although the frequency of NUP210 overexpression was lower (19.3%) in 57 lung adenocarcinoma tissue samples studied and stored in another database. This study provides a basis to discover epigenetic biomarkers highly specific to a certain cancer, based on multi-omics data at the cell population level.
Project description:BackgroundGastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.MethodsWe integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.ResultsWe comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.ConclusionsThese findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.
Project description:Multiple sclerosis (MS) is an autoimmune disorder caused by chronic inflammatory reactions in the central nervous system. Currently, little is known about the changes of plasma proteomic profiles in Chinese patients with MS (CpwMS) and its relationship with the altered profiles of multi-omics such as metabolomics and gut microbiome, as well as potential molecular networks that underlie the etiology of MS. To uncover the characteristics of proteomics landscape and potential multi-omics interaction networks in CpwMS, Plasma samples were collected from 22 CpwMS and 22 healthy controls (HCs) and analyzed using a Tandem Mass Tag (TMT)-based quantitative proteomics approach. Our results showed that the plasma proteomics pattern was significantly different in CpwMS compared to HCs. A total of 90 differentially expressed proteins (DEPs), such as LAMP1 and FCG2A, were identified in CpwMS plasma comparing to HCs. Furthermore, we also observed extensive and significant correlations between the altered proteomic profiles and the changes of metabolome, gut microbiome, as well as altered immunoinflammatory responses in MS-affected patients. For instance, the level of LAMP1 and ERN1 were significantly and positively correlated with the concentrations of metabolite L-glutamic acid and pro-inflammatory factor IL-17 (Padj < 0.05). However, they were negatively correlated with the amounts of other metabolites such as L-tyrosine and sphingosine 1-phosphate, as well as the concentrations of IL-8 and MIP-1α. This study outlined the underlying multi-omics integrated mechanisms that might regulate peripheral immunoinflammatory responses and MS progression. These findings are potentially helpful for developing new assisting diagnostic biomarker and therapeutic strategies for MS.