Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers.
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ABSTRACT:
SUBMITTER: Jiangzhou H
PROVIDER: S-EPMC8340129 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Tuberculosis (TB) accounts for disproportionate morbidity and mortality among persons living with HIV (PLWH). Conventional methods of TB diagnosis, including smear microscopy and Xpert MTB/RIF, have lower sensitivity in PLWH. Novel high-throughput approaches, such as miRNAomics and metabolomics, may advance our ability to recognize subclinical and difficult-to-diagnose TB, especially in very advanced HIV. We conducted a case-control study leveraging REMEMBER, a multi-country, open-label randomized controlled trial comparing 4-drug empiric standard TB treatment with isoniazid preventive therapy in PLWH initiating antiretroviral therapy (ART) with CD4 cell counts <50 cells/μL. Twenty-three cases of incident TB were site-matched with 32 controls to identify microRNAs (miRNAs), metabolites, and cytokines/chemokines, associated with the development of newly diagnosed TB in PLWH. Differentially expressed miRNA analysis revealed 11 altered miRNAs with a fold change higher than 1.4 or lower than -1.4 in cases relative to controls (p<0.05). Our analysis revealed no differentially abundant metabolites between cases and controls. We found higher TNFα and IP-10/CXCL10 in cases (p=0.011, p=0.0005), and higher MDC/CCL22 in controls (p=0.0072). A decision-tree algorithm identified gamma-glutamylthreonine and hsa-miR-215-5p as the optimal variables to classify incident TB cases (AUC 0.965; 95% CI 0.925-1.000). hsa-miR-215-5p, which targets genes in the TGF-β signaling pathway, was downregulated in cases. Gamma-glutamylthreonine, a breakdown product of protein catabolism, was less abundant in cases. To our knowledge, this is one of the first uses of a multi-omics approach to identify incident TB in severely immunosuppressed PLWH.
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Project description:Synthetic lethality is thought to play an important role in anticancer therapies. Herein, to understand the potential distributions and relationships between synthetic lethal interactions between genes, especially for pairs deriving from different sources, we performed an integrative analysis of genes at multiple molecular levels. Based on inter-species phylogenetic conservation of synthetic lethal interactions, gene pairs from yeast and humans were analyzed; a total of 37,588 candidate gene pairs containing 7,816 genes were collected. Of these, 49.74% of genes had 2-10 interactions, 22.93% were involved in hallmarks of cancer, and 21.61% were identified as core essential genes. Many genes were shown to have important biological roles via functional enrichment analysis, and 65 were identified as potentially crucial in the pathophysiology of cancer. Gene pairs with dysregulated expression patterns had higher prognostic values. Further screening based on mutation and expression levels showed that remaining gene pairs were mainly derived from human predicted or validated pairs, while most predicted pairs from yeast were filtered from analysis. Genes with synthetic lethality were further analyzed with their interactive microRNAs (miRNAs) at the isomiR level which have been widely studied as negatively regulatory molecules. The miRNA-mRNA interaction network revealed that many synthetic lethal genes contributed to the cell cycle (seven of 12 genes), cancer pathways (five of 12 genes), oocyte meiosis, the p53 signaling pathway, and hallmarks of cancer. Our study contributes to the understanding of synthetic lethal interactions and promotes the application of genetic interactions in further cancer precision medicine.
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Project description:BackgroundKIF15 plays a vital role in many biological processes and has been reported to influence the occurrence and development of certain human cancers. However, there are few systematic evaluations on the role of KIF15 in human cancers, and the role of KIF15 in the diagnosis and prognosis of nasopharyngeal carcinoma (NPC) also remains unexplored. Therefore, this study aimed to conduct a pan-cancer analysis of KIF15 and evaluate its diagnostic and prognostic potential in NPC.MethodsThe expression pattern, prognostic value, molecular function, tumor mutation burden, microsatellite instability, and immune cell infiltration of KIF15 were examined based on public databases. Next, the diagnostic value of KIF15 in NPC was analyzed using the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Kaplan-Meier curves, Cox regression analyses, and nomograms were used to evaluate the effects of KIF15 expression on NPC prognosis. Finally, the effect of KIF15 on NPC was explored by in vitro experiments.ResultsThe expression of KIF15 was significantly upregulated in 20 out of 33 cancer types compared to adjacent normal tissue. Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analysis showed that KIF15 could participate in several cancer-related pathways. The increased expression level of KIF15 was correlated with worse clinical outcomes in many types of human cancers. Additionally, KIF15 expression was related to cancer infiltration of immune cells, tumor mutation burden, and microsatellite instability. In the analysis of NPC, KIF15 was significantly upregulated based on the GEO database and immunohistochemistry. A high expression of KIF15 was negatively associated with the prognosis of patients with NPC. A nomogram model integrating clinical characteristics and KIF15 expression was established, and it showed good predictive ability with an area under the curve value of 0.73. KIF15 knockdown significantly inhibited NPC cell proliferation and migration.ConclusionsOur findings revealed the important and functional role of KIF15 as an oncogene in pan-cancer. Moreover, high expression of KIF15 was found in NPC tissues, and was correlated with poor prognosis in NPC. KIF15 may serve as a potential therapeutic target in NPC treatment.
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Project description:Microsatellite instability (MSI) is a frequent and clinically relevant molecular phenotype in colorectal cancer. MSI cancers have favorable survival compared with microsatellite stable cancers (MSS), possibly due to the pronounced tumor-infiltrating lymphocytes observed in MSI cancers. Consistent with the strong immune response that MSI cancers trigger in the host, previous transcriptome expression studies have identified mRNA signatures characteristic of immune response in MSI cancers. However, proteomics features of MSI cancers and the extent to which the mRNA signatures are reflected at the protein level remain largely unknown. Here, we performed a comprehensive comparison of global proteomics profiles between MSI and MSS colorectal cancers in The Cancer Genome Atlas (TCGA) cohort. We found that protein signatures of MSI are also associated with increased immunogenicity. To reliably quantify post-transcription regulation in MSI cancers, we developed a resampling-based regression method by integrative modeling of transcriptomics and proteomics data sets. Compared with the popular simple method, which detects post-transcriptional regulation by either identifying genes differentially expressed at the mRNA level but not at the protein level or vice versa, our method provided a quantitative, more sensitive, and accurate way to identify genes subject to differential post-transcriptional regulation. With this method, we demonstrated that post-transcriptional regulation, coordinating protein expression with key players, initiates de novo and enhances protective host response in MSI cancers.
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Project description:Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.
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Project description:BackgroundWhile there have been encouraging preliminary clinical results for immune checkpoint inhibitors (ICIs) in BTCs, it remains a challenge to identify the subset of patients who may benefit. In this study, we evaluated the efficacy of ICI treatment in patients with advanced BTCs, and explored potential biomarkers that are predictive of response.MethodsThe study enrolled 26 patients with advanced microsatellite stable BTCs (15 with gallbladder cancers [GCs] and 11 with intrahepatic cholangiocarcinoma [ICCs]) who received ICI treatment. Targeted next-generation sequencing (NGS) was performed on tumor tissue samples collected from 17 patients. Clinical and genomic characteristics were assessed for the correlation with clinical outcome.ResultsAnalysis of the baseline clinical characteristics showed that performance score (PS) of 0 was associated with a better prognosis than PS of 1 (HR = 1.08 × 109 ; 95% CI, 0∼Inf; P = .002). No significant correlations were found between clinical outcome and inflammation-related indicators. NGS profiling of the available tumor tissues, revealed largely non-overlapping somatic alterations between GCs and ICCs. Mutations in LRP1B (HR = 0.26; 95% CI, 0.06-1.21; P = .067), ERBB2 (HR = 0.15; 95% CI, 0.02-1.19; P = .04), or PKHD1 (HR < 0.01; 95% CI, 0-Inf; P = .04) showed strong association with increased progression-free survival (PFS) benefit. Subsequent analysis showed that alterations in the RTK-RAS pathway were associated with improved outcomes (HR = 0.12; 95% CI, 0.02-0.63; P = .003). Tumor mutation burden (TMB) was higher in patients with GC than those with ICC, and was associated with LRP1B mutations (P = .032). We found that patients with 19q amplification (19q Amp) and 9p deletion (9p Del) had poor PFS outcome (19q Amp, HR = 15.4; 95% CI, 2.7-88.5; P < .001; 9p Del; HR = 4.88 × 109 ; 95% CI, 0-Inf; P < .001), while those with chromosomal instability derived PFS benefit (HR = 0.24; 95% CI, 0.05-1.17; P = .057).ConclusionOur study identified several potential clinical and genomic features that may serve as biomarkers of clinical response to ICIs in advanced BTCs patients. A larger sample size is required for further verification.
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Project description:Gastrointestinal cancers, including oesophageal, gastric and colorectal cancers (CRC) have high rates of disease recurrence despite curative resection. There are a number of recent studies that have investigated the use of circulating tumor DNA (ctDNA) for prognostic value in these cancers. We reviewed studies that had been published prior to March 2018 that assessed the prognostic values of ctDNA in patients with oesophageal and gastric cancers, gastrointestinal stromal tumors (GIST) and CRC. We identified 63 eligible clinical studies that focussed on recurrence and survival. Studies assessed investigated various ctDNA biomarkers in patients with different stages of cancer undergoing surgical resection, chemotherapy and no treatment. For oesophageal squamous cell carcinoma and oesophageal adenocarcinoma, methylation of certain genes such as APC and DAPK have been highlighted as promising biomarkers for prognostication, but these studies are limited and more comprehensive research is needed. Studies focusing on gastric cancer patients showed that methylation of ctDNA in SOX17 and APC were independently associated with poor survival. Two studies demonstrated an association between ctDNA and recurrence and survival in GIST patients, but more studies are needed for this type of gastrointestinal cancer. A large proportion of the literature was on CRC which identified both somatic mutations and DNA methylation biomarkers to determine prognosis. ctDNA biomarkers that identified somatic mutations were more effective if they were personalized based on mutations found in the primary tumor tissue, but ctDNA methylation studies identified various biomarkers that predicted increased risk of recurrence, poor disease free survival and overall survival. While the use of non-invasive ctDNA biomarkers for prognosis is promising, larger studies are needed to validate the clinical utility for optimizing treatment and surveillance strategies to reduce mortality from gastrointestinal cancers.
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Project description:The development of novel targeted therapies for cancer treatment requires identification of reliable targets. FAM83 ('family with sequence similarity 83') family members A, B, and D were shown recently to have oncogenic potential. However, the overall oncogenic abilities of FAM83 family genes remain largely unknown. Here, we used a systematic and integrative genomics approach to investigate oncogenic properties of the entire FAM83 family members. We assessed transcriptional expression patterns of eight FAM83 family genes (FAM83A-H) across tumor types, the relationship between their expression and changes in DNA copy number, and the association with patient survival. By comparing the gene expression levels of FAM83 family members in cancers from 17 different tumor types with those in their corresponding normal tissues, we identified consistent upregulation of FAM83D and FAM83H across the majority of tumor types, which is largely driven by increased DNA copy number. Importantly, we found also that a higher expression level of a signature of FAM83 family members was associated with poor prognosis in a number of human cancers. In breast cancer, we found that alterations in FAM83 family genes correlated significantly with TP53 mutation, whereas significant, but inverse correlation was observed with PIK3CA and CDH1 (E-cadherin) mutations. We also identified that expression levels of 55 proteins were significantly associated with alterations in FAM83 family genes including a decrease in GATA3, ESR1, and PGR proteins in tumors with alterations in FAM83. Our results provide strong evidence for a critical role of FAM83 family genes in tumor development, with possible relevance for therapeutic target development.
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Project description:DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.
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