scholarly journals Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, gender and vital status of patients

2019 ◽  
Author(s):  
Alexandra R. So ◽  
Jeong Min Si ◽  
David Lopez ◽  
Matteo Pellegrini
Keyword(s):  
Expression Profiles ◽  
Molecular Classification ◽  
Classification Systems ◽  
Cancer Classification ◽  
P Value ◽  
Molecular Heterogeneity ◽  
Molecular Signatures ◽  
Systematic Analysis ◽  
Vital Status ◽  
Cancer Types

AbstractCancer affects millions of individuals worldwide. One shortcoming of traditional cancer classification systems is that, even for tumors affecting a single organ, there is significant molecular heterogeneity. Precise molecular classification of tumors could be beneficial in personalizing patients’ therapy and predicting prognosis. To this end, here we propose to use molecular signatures to further refine cancer classification. Molecular signatures are collections of genes characterizing particular cell types, tissues or disease. Signatures can be used to interpret expression profiles from heterogeneous samples. Large collections of gene signatures have previously been cataloged in the MSigDB database. We have developed a web-based Signature Visualization Tool (SaVanT) to display signature scores in user-generated expression data. Here we have undertaken a systematic analysis of correlations between inflammatory signatures and cancer samples, to test whether inflammation can differentiate cancer types. Inflammatory response signatures were obtained from MsigDB and SaVanT and a signature score was computed for samples associated with 7 different cancer types. We first identified types of cancers that had high inflammation levels as measured by these signatures. The correlation between signature scores and metadata of these patients (gender, age at initial cancer diagnosis, cancer stage, and vital status) was then computed. We sought to evaluate correlations between inflammation with other clinical parameters and identified four cancer types that had statistically significant association (p-value < 0.05) with at least one clinical characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), and uveal melanoma (UVM). These results may allow future studies to use these approaches to further refine cancer subtyping and ultimately treatment.

MULTICLASS MOLECULAR CANCER CLASSIFICATION BY KERNEL SUBSPACE METHODS WITH EFFECTIVE KERNEL PARAMETER SELECTION

2005 ◽  
Vol 03 (05) ◽  
pp. 1071-1088 ◽  
Author(s):  
SATOSHI NIIJIMA ◽  
SATORU KUHARA
Keyword(s):  
High Performance ◽  
Molecular Classification ◽  
Parameter Selection ◽  
Cancer Classification ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Classification Problems ◽  
Cancer Treatments ◽  
Kernel Parameter ◽  
Cancer Types

Microarray techniques provide new insights into molecular classification of cancer types, which is critical for cancer treatments and diagnosis. Recently, an increasing number of supervised machine learning methods have been applied to cancer classification problems using gene expression data. Support vector machines (SVMs), in particular, have become one of the most effective and leading methods. However, there exist few studies on the application of other kernel methods in the literature. We apply a kernel subspace (KS) method to multiclass cancer classification problems, and assess its validity by comparing it with multiclass SVMs. Our comparative study using seven multiclass cancer datasets demonstrates that the KS method has high performance that is comparable to multiclass SVMs. Furthermore, we propose an effective criterion for kernel parameter selection, which is shown to be useful for the computation of the KS method.

scholarly journals Integrative Analysis the characterization of peroxiredoxins in pan-cancer

2020 ◽  
Author(s):  
Lei Gao ◽  
Jialin Meng ◽  
Chuang Yue ◽  
Xingyu Wu ◽  
Quanxin Su ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Genetic Alterations ◽  
Comprehensive Analysis ◽  
Biological Pathways ◽  
Cancer Cell Proliferation ◽  
Biological Functions ◽  
Systematic Analysis ◽  
Cancer Types ◽  
Pan Cancer

AbstractPeroxiredoxins (PRDXs) are antioxidant enzymes protein family members that involves the process of several biological functions, such as differentiation, cell growth. Considerable evidence demonstrates that PRDXs play critical roles in the occurrence and development of carcinomas. However, a systematic analysis of PRDXs in cancers is deficiency. Therefore, we perform a comprehensive analysis of PRDXs in 33 cancer types including mRNA expression profiles, genetic alterations, methylation, prognostic values, potential biological pathways and target drugs. Moreover, we validated that PRDX6 could regulate cancer cell proliferation via JAK2-STAT3 pathway and involve into the process of cell cycle in bladder cancer.

scholarly journals Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments

2022 ◽  
Vol 12 ◽  
Author(s):  
Yiran Zhou ◽  
Qinghua Cui ◽  
Yuan Zhou
Keyword(s):  
Signaling Pathway ◽  
Cancer Progression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Composition Analysis ◽  
Multiple Cancer ◽  
Systematic Analysis ◽  
Cancer Subtypes ◽  
Cancer Types

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

scholarly journals Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, sex and vital status of patients

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0221545
Author(s):  
Alexandra Renee So ◽  
Jeong Min Si ◽  
David Lopez ◽  
Matteo Pellegrini
Keyword(s):  
Tumor Stage ◽  
Molecular Signatures ◽  
Vital Status ◽  
Cancer Types

scholarly journals Current Trends in the Molecular Classification of Renal Neoplasms

2006 ◽  
Vol 6 ◽  
pp. 2505-2518 ◽  
Author(s):  
Andrew N. Young ◽  
Viraj A. Master ◽  
Mahul B. Amin
Keyword(s):  
Gene Expression ◽  
Kidney Cancer ◽  
Classification System ◽  
Who Classification ◽  
Expression Profiles ◽  
Molecular Classification ◽  
Classification Systems ◽  
World Health ◽  
Renal Tumors ◽  
Clinical Behavior

Renal cell carcinoma (RCC) is the most common form of kidney cancer in adults. RCC is a significant challenge for pathologic diagnosis and clinical management. The primary approach to diagnosis is by light microscopy, using the World Health Organization (WHO) classification system, which defines histopathologic tumor subtypes with distinct clinical behavior and underlying genetic mutations. However, light microscopic diagnosis of RCC subtypes is often difficult due to variable histology. In addition, the clinical behavior of RCC is highly variable and therapeutic response rates are poor. Few clinical assays are available to predict outcome in RCC or correlate behavior with histology. Therefore, novel RCC classification systems based on gene expression should be useful for diagnosis, prognosis, and treatment. Recent microarray studies have shown that renal tumors are characterized by distinct gene expression profiles, which can be used to discover novel diagnostic and prognostic biomarkers. Here, we review clinical features of kidney cancer, the WHO classification system, and the growing role of molecular classification for diagnosis, prognosis, and therapy of this disease.

scholarly journals Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis

2021 ◽  
pp. annrheumdis-2020-219760
Author(s):  
Julia Steinberg ◽  
Lorraine Southam ◽  
Andreas Fontalis ◽  
Matthew J Clark ◽  
Raveen L Jayasuriya ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Cell Adhesion ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Molecular Heterogeneity ◽  
Low Grade ◽  
Sequencing Data ◽  
Patient Subgroups

ObjectivesTo determine how gene expression profiles in osteoarthritis joint tissues relate to patient phenotypes and whether molecular subtypes can be reproducibly captured by a molecular classification algorithm.MethodsWe analysed RNA sequencing data from cartilage and synovium in 113 osteoarthritis patients, applying unsupervised clustering and Multi-Omics Factor Analysis to characterise transcriptional profiles. We tested the association of the molecularly defined patient subgroups with clinical characteristics from electronic health records.ResultsWe detected two patient subgroups in low-grade cartilage (showing no/minimal degeneration, cartilage normal/softening only), with differences associated with inflammation, extracellular matrix-related and cell adhesion pathways. The high-inflammation subgroup was associated with female sex (OR 4.12, p=0.0024) and prescription of proton pump inhibitors (OR 4.21, p=0.0040). We identified two independent patient subgroupings in osteoarthritis synovium: one related to inflammation and the other to extracellular matrix and cell adhesion processes. A seven-gene classifier including MMP13, APOD, MMP2, MMP1, CYTL1, IL6 and C15orf48 recapitulated the main axis of molecular heterogeneity in low-grade knee osteoarthritis cartilage (correlation ρ=−0.88, p<10−10) and was reproducible in an independent patient cohort (ρ=−0.85, p<10−10).ConclusionsThese data support the reproducible stratification of osteoarthritis patients by molecular subtype and the exploration of new avenues for tailored treatments.

Müller-Weiss Disease: The Descriptive Factors of Failure Conservative Treatment

2021 ◽  
pp. 107110072110028
Author(s):  
Thos Harnroongroj ◽  
Theerawoot Tharmviboonsri ◽  
Bavornrit Chuckpaiwong
Keyword(s):  
Regression Analysis ◽  
Conservative Treatment ◽  
Demographic Characteristics ◽  
Classification Systems ◽  
Clinical Severity ◽  
P Value ◽  
Level Of Evidence ◽  
Factors Associated ◽  
Radiographic Parameters ◽  
Significant Difference

Background: Conservative treatment is the first-line approach for Müller-Weiss disease (MWD). However, factors associated with the failure of conservative treatment have never been reported. Our objectives were to compare the differences in demographic and radiographic parameters between “successful” and “failure” conservative treatment in patients with MWD and identify descriptive factors associated with failure conservative treatment. Methods: We retrospectively reviewed 68 patients with MWD divided into 29 “failure” and 39 “successful” conservative treatment groups. Demographic characteristics, Foot and Ankle Outcome Score (FAOS), visual analog scale (VAS) scores for pain and walking disability, and radiographic parameters such as calcaneal pitch, lateral Meary, anteroposterior (AP) Meary angle, and talonavicular-naviculocuneiform arthritis were compared. Logistic regression analysis was performed to identify descriptive factors of failure conservative treatment. A P value <.05 was considered a statistically significant difference. Results: We found more severe VAS pain and walking disability scores and FAOS for the pain, activities of daily living, and quality of life subscales in the failure group ( P < .05). Regression analysis demonstrated 2 significant descriptive factors associated with failure conservative treatment: abducted AP Meary angle >13.0 degrees and radiographic talonavicular arthritis. No demographic characteristics were found to be associated with failure conservative treatment. Conclusion: Midfoot abduction (AP Meary angle, >13 degrees) and radiographic talonavicular arthritis were factors associated with failure conservative treatment in MWD and should be determined concurrently with the clinical severity. Classification systems for MWD should include these factors. Level of evidence: Level III, retrospective comparative study.

scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

scholarly journals Non-Coding RNAs as Prognostic Markers for Endometrial Cancer

2021 ◽  
Vol 22 (6) ◽  
pp. 3151 ◽  
Author(s):  
Roberto Piergentili ◽  
Simona Zaami ◽  
Anna Franca Cavaliere ◽  
Fabrizio Signore ◽  
Giovanni Scambia ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Prognostic Markers ◽  
Classification Systems ◽  
Prognostic Role ◽  
Protein Coding ◽  
Pathological Characteristics ◽  
Epigenetic Events ◽  
Cancer Types ◽  
Non Coding Rnas

Endometrial cancer (EC) has been classified over the years, for prognostic and therapeutic purposes. In recent years, classification systems have been emerging not only based on EC clinical and pathological characteristics but also on its genetic and epigenetic features. Noncoding RNAs (ncRNAs) are emerging as promising markers in several cancer types, including EC, for which their prognostic value is currently under investigation and will likely integrate the present prognostic tools based on protein coding genes. This review aims to underline the importance of the genetic and epigenetic events in the EC tumorigenesis, by expounding upon the prognostic role of ncRNAs.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

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