scholarly journals Molecular Mechanisms of PD-1 and PD-L1 Activity on a Pan-Cancer Basis: A Bioinformatic Exploratory Study

2021 ◽  
Vol 22 (11) ◽  
pp. 5478
Author(s):  
Siddarth Kannan ◽  
Geraldine Martina O’Connor ◽  
Emyr Yosef Bakker
Keyword(s):  
Clinical Relevance ◽  
Molecular Mechanisms ◽  
Patient Survival ◽  
Drug Repurposing ◽  
Immune Checkpoint Blockade ◽  
Multiple Cancers ◽  
Significant Survival ◽  
Cancer Types ◽  
Repurposed Drugs ◽  
Pan Cancer

Immune checkpoint blockade targeting PD-1 (PDCD1)/PD-L1 (CD274) is increasingly used for multiple cancers. However, efficacy and adverse-related events vary significantly. This bioinformatic study interrogated molecular differences pertaining to PDCD1/CD274 and their correlated genes on a pan-cancer basis to identify differences between cancer types. Patient RNA-seq data from fifteen cancer types were accessed on cBioPortal to determine the role of PDCD1/CD274 in patient survival and to identify positively and negatively correlated genes, which were also assessed for clinical relevance. Genes correlating with PDCD1/CD274 across multiple cancers were taken forward for drug repurposing via DRUGSURV and microRNA analysis using miRDB and miRabel. MicroRNAs were also screened for clinical relevance using OncomiR. Forty genes were consistently correlated with PDCD1/CD274 across multiple cancers, with the cancers themselves exhibiting a differential role for the correlated genes in terms of patient survival. Esophageal and renal cancers in particular stood out in this regard as having a unique survival profile. Forty-nine putative microRNAs were identified as being linked to the PDCD1/CD274 network, which were taken forward and further assessed for clinical relevance using OncomiR and previously published literature. One hundred and thirty significant survival associations for 46 microRNAs across fourteen groups of cancers were identified. Finally, a total of 23 putative repurposed drugs targeting multiple components of the PDCD1/CD274 network were identified, which may represent immunotherapeutic adjuvants. Taken together, these results shed light on the varying PDCD1/CD274 networks between individual cancers and signpost a need for more cancer-specific investigations and treatments.

scholarly journals Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

2018 ◽  
Author(s):  
Matthew H. Ung ◽  
Evelien Schaafsma ◽  
Daniel E. Mattox ◽  
George L. Wang ◽  
Chao Cheng
Keyword(s):  
Drug Targets ◽  
Patient Survival ◽  
Cancer Prognosis ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Cancer Types ◽  
Microarray Studies ◽  
Systematic Identification ◽  
Pan Cancer ◽  
Novel Associations

AbstractThe “dark matter” of the genome harbors several non-coding RNA species including IncRNAs, which have been implicated in neoplasias but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

scholarly journals Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8797 ◽  
Author(s):  
Matthew Ung ◽  
Evelien Schaafsma ◽  
Daniel Mattox ◽  
George L. Wang ◽  
Chao Cheng
Keyword(s):  
Drug Targets ◽  
Patient Survival ◽  
Cancer Prognosis ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Cancer Types ◽  
Non Coding Rnas ◽  
Microarray Studies ◽  
Systematic Identification ◽  
Pan Cancer

Background The “dark matter” of the genome harbors several non-coding RNA species including Long non-coding RNAs (lncRNAs), which have been implicated in neoplasia but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. Methods In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. Results In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

scholarly journals Characterization of Class-3 Semaphorin Receptors, Neuropilins and Plexins, as Therapeutic Targets in a Pan-Cancer Study

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1816
Author(s):  
Xiaoli Zhang ◽  
Shuai Shao ◽  
Lang Li
Keyword(s):  
Patient Survival ◽  
Cancer Type ◽  
Dependent Manner ◽  
Metastatic Progression ◽  
Drug Responses ◽  
Signal Transducers ◽  
Surface Receptors ◽  
Downstream Signaling ◽  
Cancer Types ◽  
Pan Cancer

Class-3 semaphorins (SEMA3s), initially characterized as axon guidance cues, have been recognized as key regulators for immune responses, angiogenesis, tumorigenesis and drug responses. The functions of SEMA3s are attributed to the activation of downstream signaling cascades mainly mediated by cell surface receptors neuropilins (NRPs) and plexins (PLXNs), yet their roles in human cancers are not completely understood. Here, we provided a detailed pan-cancer analysis of NRPs and PLXNs in their expression, and association with key signal transducers, patient survival, tumor microenvironment (TME), and drug responses. The expression of NRPs and PLXNs were dysregulated in many cancer types, and the majority of them were further dysregulated in metastatic tumors, indicating a role in metastatic progression. Importantly, the expression of these genes was frequently associated with key transducers, patient survival, TME, and drug responses; however, the direction of the association varied for the particular gene queried and the specific cancer type/subtype tested. Specifically, NRP1, NRP2, PLXNA1, PLXNA3, PLXNB3, PLXNC1, and PLXND1 were primarily associated with aggressive phenotypes, whereas the rest were more associated with favorable prognosis. These data highlighted the need to study each as a separate entity in a cancer type- and subtype-dependent manner.

scholarly journals A pan‐cancer analysis reveals genetic alterations, molecular mechanisms, and clinical relevance of m 5 C regulators

2020 ◽  
Vol 10 (5) ◽  
Author(s):  
E Du ◽  
Jingxian Li ◽  
Fei Sheng ◽  
Shuai Li ◽  
Jianqiang Zhu ◽  
...  
Keyword(s):  
Clinical Relevance ◽  
Molecular Mechanisms ◽  
Genetic Alterations ◽  
Pan Cancer

scholarly journals Glutamine Metabolism Regulators Associated with Cancer Development and the Tumor Microenvironment: A Pan-Cancer Multi-Omics Analysis

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1305
Author(s):  
Jingwen Zou ◽  
Kunpeng Du ◽  
Shaohua Li ◽  
Lianghe Lu ◽  
Jie Mei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Drug Sensitivity ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Metabolic Reprogramming ◽  
Glutamine Metabolism ◽  
Molecular Networks ◽  
Cancer Types ◽  
Pan Cancer

Background: In recent years, metabolic reprogramming has been identified as a hallmark of cancer. Accumulating evidence suggests that glutamine metabolism plays a crucial role in oncogenesis and the tumor microenvironment. In this study, we aimed to perform a systematic and comprehensive analysis of six key metabolic node genes involved in the dynamic regulation of glutamine metabolism (referred to as GLNM regulators) across 33 types of cancer. Methods: We analyzed the gene expression, epigenetic regulation, and genomic alterations of six key GLNM regulators, including SLC1A5, SLC7A5, SLC3A2, SLC7A11, GLS, and GLS2, in pan-cancer using several open-source platforms and databases. Additionally, we investigated the impacts of these gene expression changes on clinical outcomes, drug sensitivity, and the tumor microenvironment. We also attempted to investigate the upstream microRNA–mRNA molecular networks and the downstream signaling pathways involved in order to uncover the potential molecular mechanisms behind metabolic reprogramming. Results: We found that the expression levels of GLNM regulators varied across cancer types and were related to several genomic and immunological characteristics. While the immune scores were generally lower in the tumors with higher gene expression, the types of immune cell infiltration showed significantly different correlations among cancer types, dividing them into two clusters. Furthermore, we showed that elevated GLNM regulators expression was associated with poor overall survival in the majority of cancer types. Lastly, the expression of GLNM regulators was significantly associated with PD-L1 expression and drug sensitivity. Conclusions: The elevated expression of GLNM regulators was associated with poorer cancer prognoses and a cold tumor microenvironment, providing novel insights into cancer treatment and possibly offering alternative options for the treatment of clinically refractory cancers.

scholarly journals A catalog of cis-regulatory mutations in 12 major cancer types

2019 ◽  
Author(s):  
Zhongshan Cheng ◽  
Michael Vermeulen ◽  
Micheal Rollins-Green ◽  
Brian DeVeale ◽  
Tomas Babak
Keyword(s):  
Gene Expression ◽  
Binding Sites ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Gene Expression Regulation ◽  
Patient Survival ◽  
Chromatin Accessibility ◽  
Regulatory Mutations ◽  
Cancer Types ◽  
Affect Gene Expression

AbstractDespite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants is notoriously challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, gene expression, chromatin accessibility, and genetic data from TCGA, we identified 301 non-coding somatic mutations that affect gene expression in cis. These mutations cluster into 36 hotspot regions with diverse molecular mechanisms of gene expression regulation. We further show that these mutations have hallmark features of noncoding drivers; namely, that they confer a positive selection on growth, functionally disrupt transcription factor binding sites, and contribute to disease progression reflected in decreased overall patient survival.

scholarly journals Hiding in the dark: pan-cancer characterization of expression and clinical relevance of CD40 to immune checkpoint blockade therapy

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Chi Yan ◽  
Ann Richmond
Keyword(s):  
Immune Checkpoint ◽  
Tumor Response ◽  
Immune Checkpoint Blockade ◽  
Checkpoint Blockade ◽  
Type I ◽  
List Type ◽  
Cancer Types ◽  
Pan Cancer ◽  
Cancer Bioinformatics

Highlights CD40 expression correlates with the type I anti-tumor response and better survival. Pan-cancer bioinformatics characterization reveals reduced CD40 expression in 11 cancer types, including RASmut melanoma compared to nevi. RAS mutation correlates with reduced CD40 expression in malignant melanoma. CD40 expression is associated with better response to immune checkpoint blockade therapy in melanoma.

scholarly journals Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaonan Liu ◽  
Pei Wang ◽  
Xufei Teng ◽  
Zhang Zhang ◽  
Shuhui Song
Keyword(s):  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Critical Role ◽  
Expression Regulation ◽  
The Cancer Genome Atlas ◽  
Aberrant Expression ◽  
Human Cancers ◽  
Cancer Types ◽  
Pan Cancer

BackgroundN6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely “writers,” “erasers,” and “readers.” Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers.ResultsHere we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients.ConclusionsOur findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators’ expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

2020 ◽  
Author(s):  
Zhuohui Wei ◽  
Yue Zhang ◽  
Wanlin Weng ◽  
Jiazhou Chen ◽  
Hongmin Cai
Keyword(s):  
Molecular Mechanisms ◽  
Genomic Data ◽  
Low Rank ◽  
Integrated Analysis ◽  
Data Sets ◽  
Omics Data ◽  
Data Types ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

scholarly journals Pan-Cancer Integrative Analysis of Whole-Genome De novo Somatic Point Mutations Reveals 17 Cancer Types

2021 ◽  
Author(s):  
Amirreza Kazemi ◽  
Amin Ghareyazi ◽  
Kimia Hamidieh ◽  
Hamed Dashti ◽  
Maedeh Tahaei ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Specific Treatment ◽  
Cancer Drug ◽  
Cancer Type ◽  
Cancer Subtypes ◽  
Genotypic Analysis ◽  
Cancer Types ◽  
Pan Cancer

The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, resulting in identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence there is no “silver bullet” for the treatment of a cancer type. This reveals the importance of developing a pipeline to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types. In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in a significant portion of samples to identify cancer subtypes. We applied our pipeline to 12270 samples collected from the International Cancer Genome Consortium (ICGC), covering 19 cancer types. Here we identified 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways, in which, for most of them, targeted treatment options are currently available. This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. We also comprehensive study the causes of mutations among samples in each subtype by mining the mutational signatures, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on “gene-motif” suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer.

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