• Blood-based biomarkers
• Colorectal cancer screening
• DNA methylation
• Protein markers
• RNA biomarkers

• Humans
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/blood
• Biomarkers, Tumor/blood
• Early Detection of Cancer/methods

• Colorectal cancer screening
• Diagnostic model
• Immune status
• Machine learning
• Serum protein biomarkers

• Humans
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/blood
• Biomarkers, Tumor/blood
• Biomarkers, Tumor/genetics
• Machine Learning
• Gene Expression Regulation, Neoplastic
• Up-Regulation
• Female
• Male
• Gene Expression Profiling/methods
• Prognosis
• Gene Regulatory Networks
• Middle Aged

• Hispanics
• blood-based biomarkers
• colorectal cancer
• diagnostic accuracy

• U54- MD007600 Research Centers in Minority Institutions
• R25MD007607 National Institutes of Health (NIH)/NIMHD Hispanic Clinical and Translational Research Education and Career Development
• R25GM061838 NIH/National Center for Research Resources
• U54CA096297 NCI NIH HHS
• R21CA198963 NCI NIH HHS

• CLEC4D
• NF-κB signaling pathways
• gastric cancer
• migration

• Tianjin Key Medical Discipline (Specialty) Construction Project
• National Key R&D Program of China
• the Distinguished professor of Tianjin

• Chronic diseases
• Glycolipid metabolism
• Oxidative stress
• Targeted therapy
• Vanin1

• Humans
• Cysteamine/metabolism
• Oxidative Stress
• Pantothenic Acid/metabolism
• Chronic Disease
• Disease Progression
• Amidohydrolases/metabolism
• GPI-Linked Proteins/metabolism

• lung cancer
• epidemiology
• cancer epidemiology
• diagnostic markers
• prognostic markers

• Humans
• Lung Neoplasms/diagnosis
• Lung Neoplasms/genetics
• Transcriptome
• RNA
• Biomarkers, Tumor/genetics
• CD4-Positive T-Lymphocytes

• UiT The Arctic University of Norway (incl University Hospital of North Norway)

• Colorectal cancer
• PRRG4
• circ_0056618
• miR-411-5p

• colorectal cancer
• dynein cytoplasmic 1 light intermediate chain 1
• inosine monophosphate dehydrogenase 1
• phospholipase A and acyltransferase 2
• prognostic and predictive values
• stool specimens
• ubiquitin-conjugating enzyme E2 N

• human
• medicine

• biomarkers
• colorectal cancer
• gut microbiota
• inflammation
• patients’ stratification

• Accuracy
• CCRAT
• Colorectal cancer occurrence
• Model calibration
• Model discrimination
• Polygenic risk score (PRS)

• Colorectal cancer
• Genetic risk score
• Meta-analysis
• Polygenic
• Prediction models
• Single nucleotide polymorphisms

• Adult
• Area Under Curve
• Asian People/genetics
• Case-Control Studies
• Clinical Decision Rules
• Colorectal Neoplasms/ethnology
• Colorectal Neoplasms/genetics
• Female
• Genetic Predisposition to Disease/ethnology
• Genetic Predisposition to Disease/genetics
• Genome-Wide Association Study
• Humans
• Male
• Middle Aged
• Multifactorial Inheritance/genetics
• Polymorphism, Single Nucleotide/genetics
• Risk Assessment
• Risk Factors
• White People/genetics

Colorectal cancer (CRC) is one of the most common malignancies.An early diagnosis and an accurate prognosis are major focuses of CRC research. Tumor microenvironment cells and the extent of infiltrating immune and stromal cells contribute significantly to the tumor prognosis.

Immune and stromal scores were calculated based on the ESTIMATE algorithm using the sample expression profile of the The Cancer Genome Atlas (TCGA) database. GSE102479 was used as the validation database. Differentially expressed genes whose expression was significantly associated with the prognosis of CRC patients were identified based on the immune matrix score. Survival analysis was conducted on the union of the differentially expressed genes. A protein–protein interaction (PPI) network was constructed using the STRING database to identify the closely connected modules. To conduct functional enrichment analysis of the relevant genes, GO and KEGG pathway analyses were performed with Cluster Profiler. Pivot analysis of the ncRNAs and TFs was performed by using the RAID2.0 database and TRRUST v2 database. TF-mRNA regulatory relationships were analyzed in the TRRUST V2 database. Hubgene targeting relationships were screened in the TargetScan, miRTarBase and miRDB databases. The SNV data of the hub genes were analyzed by using the R maftools package. A ROC curve was drawn based on the TCGA database. The proportion of immune cells was estimated using CIBERSORT and the LM22 feature matrix.

The results showed that the matrix score was significantly correlated with colorectal cancer stage T. A total of 789 differentially expressed genes and 121 survival-related prognostic genes were identified. The PPI network showed that 22 core genes were related to the CRC prognosis. Furthermore, four ncRNAs that regulated the core prognosis genes, 11 TFs with regulatory effects on the core prognosis genes, and two drugs, quercetin and pseudoephedrine, that have regulatory effects on colorectal cancer were also identified.

We obtained a list of tumor microenvironment-related genes for CRC patients. These genes could be useful for determining the prognosis of CRC patients. To confirm the function of these genes, additional experiments are necessary.

• Colorectal cancer
• Immune scores
• Prognosis
• Tumor micro-environment

• Annexin A3
• cancer
• pathway
• role

• annexin A3
• biomarkers
• drug resistance
• neoplasm
• signal transduction
• tumorigenesis

• Biomarkers
• Clinical practice
• Colorectal cancer
• Early detection
• Lower gastrointestinal cancers
• Primary care

• Biomarkers
• Early Detection of Cancer
• Gastrointestinal Neoplasms/diagnosis
• Gastrointestinal Neoplasms/epidemiology
• Humans
• Prevalence
• Sensitivity and Specificity

• C8640/A23385 Cancer Research UK

• angiogenesis
• biomarkers
• circulating mRNA
• digital PCR
• liquid biopsy
• metastatic colon cancer

• PI19/01478 Instituto de Salud Carlos III

The immune system is crucial in regulating colorectal cancer (CRC) tumorigenesis. Identification of immune-related transcriptomic signatures derived from the peripheral blood of patients with CRC would provide insights into CRC pathogenesis, and suggest novel clues to potential immunotherapy strategies for the disease. The present study collected blood samples from 59 patients with CRC and 62 healthy control patients and performed whole blood gene expression profiling using microarray hybridization. Immune-related gene expression signatures for CRC were identified from immune gene datasets, and an algorithmic predictive model was constructed for distinguishing CRC from controls. Model performance was characterized using an area under the receiver operating characteristic curve (ROC AUC). Functional categories for CRC-specific gene expression signatures were determined using gene set enrichment analyses. A Kaplan-Meier plotter survival analysis was also performed for CRC-specific immune genes in order to characterize the association between gene expression and CRC prognosis. The present study identified five CRC-specific immune genes [protein phosphatase 3 regulatory subunit Bα (PPP3R1), amyloid β precursor protein, cathepsin H, proteasome activator subunit 4 and DEAD-Box Helicase 3 X-Linked]. A predictive model based on this five-gene panel showed good discriminatory power (independent test set sensitivity, 83.3%; specificity, 94.7%, accuracy, 89.2%; ROC AUC, 0.96). The candidate genes were involved in pathways associated with ‘adaptive immune responses’, ‘innate immune responses’ and ‘cytokine signaling’. The survival analysis found that a high level of PPP3R1 expression was associated with a poor CRC prognosis. The present study identified five CRC-specific immune genes that were potential diagnostic biomarkers for CRC. The biological function analysis indicated a close association between CRC pathogenesis and the immune system, and may reveal more information about the immunogenic and pathogenic mechanisms driving CRC in the future. Overall, the association between PPP3R1 expression and survival of patients with CRC revealed potential new targets for CRC immunotherapy.

• adaptive immune response
• colorectal cancer
• immune analysis
• peripheral blood
• transcriptome

Histiocytic sarcoma is an aggressive hematopoietic malignancy of mature tissue histiocytes with a poorly understood etiology in humans. A histologically and clinically similar counterpart affects flat-coated retrievers (FCRs) at unusually high frequency, with 20% developing the lethal disease. The similar clinical presentation combined with the closed population structure of dogs, leading to high genetic homogeneity, makes dogs an excellent model for genetic studies of cancer susceptibility. To determine the genetic risk factors underlying histiocytic sarcoma in FCRs, we conducted multiple genome-wide association studies (GWASs), identifying two loci that confer significant risk on canine chromosomes (CFA) 5 (P_wald = 4.83x10^-9) and 19 (P_wald = 2.25x10^-7). We subsequently undertook a multi-omics approach that has been largely unexplored in the canine model to interrogate these regions, generating whole genome, transcriptome, and chromatin immunoprecipitation sequencing. These data highlight the PI3K pathway gene PIK3R6 on CFA5, and proximal candidate regulatory variants that are strongly associated with histiocytic sarcoma and predicted to impact transcription factor binding. The CFA5 association colocalizes with susceptibility loci for two hematopoietic malignancies, hemangiosarcoma and B-cell lymphoma, in the closely related golden retriever breed, revealing the risk contribution this single locus makes to multiple hematological cancers. By comparison, the CFA19 locus is unique to the FCR and harbors risk alleles associated with upregulation of TNFAIP6, which itself affects cell migration and metastasis. Together, these loci explain ~35% of disease risk, an exceptionally high value that demonstrates the advantages of domestic dogs for complex trait mapping and genetic studies of cancer susceptibility.

We have identified two regions of the canine genome that explain a striking 35% of risk for developing histiocytic sarcoma in FCRs. The disease is uniformly lethal, affects 20% of FCRs, and parallels a cancer of the same name in humans. Both regions harbor genes involved in cell migration and cancer-related pathways. The first includes variants in regulatory regions at the tumor suppressor PIK3R6 locus that are strongly associated with histiocytic sarcoma and likely confer risk for other hematopoietic cancers. FCRs with risk alleles at the second locus demonstrate increased expression of TNFAIP6, which correlates with poor prognosis in multiple human cancers. In identifying genomic differences between affected and unaffected dogs, we advance our understanding of both canine and human health biology and set the stage for the development of diagnostic and therapeutic strategies.

• Alleles
• Animals
• Binding Sites
• Cell Adhesion Molecules/genetics
• Chromatin Immunoprecipitation Sequencing
• Dog Diseases/genetics
• Dogs/classification
• Dogs/genetics
• Genetic Predisposition to Disease
• Genome/genetics
• Genome-Wide Association Study/veterinary
• Genomics
• Genotype
• Germ-Line Mutation/genetics
• Hematologic Neoplasms/genetics
• Hematologic Neoplasms/veterinary
• Histiocytic Sarcoma/genetics
• Histiocytic Sarcoma/veterinary
• Phosphatidylinositol 3-Kinase/genetics
• Principal Component Analysis
• RNA-Seq
• Transcription Factors/metabolism

• FI2 GM133344 NIGMS NIH HHS
• National Human Genome Research Institute
• UK Flatcoated Retriever Society
• National Institute of General Medical Sciences
• Flint Animal Cancer Center
• European Commission

Background: The fecal immunochemical test (FIT) is a widely used strategy for colorectal cancer (CRC) screening with moderate sensitivity. To further increase the sensitivity of FIT in identifying colorectal neoplasia, in this study, we established a classifier model by combining FIT result and other demographic and clinical features.

Methods: A total of 4,477 participants were examined with FIT and those who tested positive (over 100 ng/ml) were followed up by a colonoscopy examination. Demographic and clinical information of participants including four domains (basic information, clinical history, diet habits and life styles) that consist of 15 features were retrieved from questionnaire surveys. A mean decrease accuracy (MDA) score was used to select features that are mostly related to CRC. Five different algorithms including logistic regression (LR), classification and regression tree (CART), support vector machine (SVM), artificial neural network (ANN) and random forest (RF) were used to generate a classifier model, through a 10X cross validation process. Area under curve (AUC) and normalized mean squared error (NMSE) were used in the evaluation of the performance of the model.

Results: The top six features that are mostly related to CRC include age, gender, history of intestinal adenoma or polyposis, smoking history, gastrointestinal discomfort symptom and fruit eating habit were selected. LR algorithm was used in the generation of the model. An AUC score of 0.92 and an NMSE score of 0.076 were obtained by the final classifier model in separating normal individuals from participants with colorectal neoplasia.

Conclusion: Our results provide a new “Funnel” strategy in colorectal neoplasia screening via adding a classifier model filtering step between FIT and colonoscopy examination. This strategy minimizes the need of colonoscopy examination while increases the sensitivity of FIT-based CRC screening.

• classifier model
• colorectal neoplasia screening
• fecal immunochemical test
• funnel strategy
• logistic regression model

• colonoscopy
• ctDNA
• extracellular vesicles
• fecal immunochemical test (FIT)
• flexible sigmoidoscopy
• liquid biopsy
• mRNA
• microRNA
• proteins

Colorectal cancer (CRC) is one of the most common types of malignancies, and radiochemotherapy (RCT) followed by surgery is the recommended approach for CRC treatment. However, some cases do not respond to first-line conventional chemotherapy or even progress further after treatment. Moreover, there is a risk of severe side effects, such as radiodermatitis. Therefore, identifying predictors for RCT sensitivity is an essential step toward predicting and eventually overcoming resistance.

We used integrative bioinformatics analysis and experimental validation to show that regenerating family member 4 (REG4) may be a potential biomarker for RCT sensitivity in CRC.

REG4, whose expression is upregulated in some CRC tissues and downregulated in RCT-sensitive CRC cells, was identified as a potential genetic marker for RCT sensitivity in CRC. Immunohistochemistry-based tissue microarray of human CRC was used to experimentally validate REG4 data obtained from the bioinformatics analysis.

Collectively, these results indicate that REG4 may be a potential biomarker for RCT sensitivity in CRC.

• colon cancer transcriptome
• immunohistochemistry microarray
• radiochemotherapy resistance
• regenerating family member 4

Vanin-1 plays a pivotal role in oxidative stress and the inflammatory response. However, its relationship with traumatic sepsis remains unknown. The aim of our study was to evaluate whether plasma vanin-1 could be used for the early prediction of traumatic sepsis.

In this three-stage prospective cohort study, severe trauma patients admitted from January 2015 to October 2018 at two hospitals were enrolled. Plasma vanin-1 levels were measured by enzyme-linked immunosorbent assay (ELISA). The associations among variables and traumatic sepsis were identified by logistic regression models and the receiver operating characteristic (ROC) curve was analyzed to evaluate the diagnostic efficiency.

A total of 426 trauma patients (22 in the discovery cohort, 283 in the internal test cohort, and 121 in the external validation cohort) and 16 healthy volunteers were recruited. The plasma vanin-1 of trauma patients was significantly higher than that of healthy volunteers (P < 0.05). Patients with sepsis had higher plasma vanin-1 than patients without sepsis in the discovery trauma cohort (P < 0.05). In the internal test cohort, plasma vanin-1 at day 1 after trauma was significantly associated with the incidence of sepsis (OR = 3.92, 95% CI 2.68–5.72, P = 1.62 × 10⁻¹²). As a predictive biomarker, vanin-1 afforded a better area under the curve (AUC) (0.82, 95% CI 0.77–0.87) than C-reaction protein (CRP) (0.62, 95% CI 0.56–0.68, P < 0.0001), procalcitonin (PCT) (0.66, 95% CI 0.60–0.71, P < 0.0001), and Acute Physiology and Chronic Health Evaluation II (APACHE II) (0.71, 95% CI 0.65–0.76, P = 6.70 × 10⁻³). The relevance was further validated in the external validation cohort (OR = 4.26, 95% CI 2.22–8.17, P = 1.28 × 10⁻⁵), with an AUC of 0.83 (95% CI 0.75–0.89). Vanin-1 could also improve the diagnostic efficiency of APACHE II (AUC = 0.85).

Our study demonstrated that plasma vanin-1 increased among trauma patients and was independently associated with the risk of sepsis. Vanin-1 might be a potential biomarker for the early prediction of traumatic sepsis.

Clinicaltrials.gov Identifier, NCT01713205.

The online version contains supplementary material available at 10.1007/s40121-021-00414-w.

• Biomarker
• Sepsis
• Trauma
• Vanin-1

• FFPE
• VANGL1
• colorectal cancer
• gene signature
• mRNA expression

The importance of coenzyme A (CoA) as a carrier of acyl residues in cell metabolism is well understood. Coenzyme A participates in more than 100 different catabolic and anabolic reactions, including those involved in the metabolism of lipids, carbohydrates, proteins, ethanol, bile acids, and xenobiotics. However, much less is known about the importance of the concentration of this cofactor in various cell compartments and the role of altered CoA concentration in various pathologies. Despite continuous research on these issues, the molecular mechanisms in the regulation of the intracellular level of CoA under pathological conditions are still not well understood. This review summarizes the current knowledge of (a) CoA subcellular concentrations; (b) the roles of CoA synthesis and degradation processes; and (c) protein modification by reversible CoA binding to proteins (CoAlation). Particular attention is paid to (a) the roles of changes in the level of CoA under pathological conditions, such as in neurodegenerative diseases, cancer, myopathies, and infectious diseases; and (b) the beneficial effect of CoA and pantethine (which like CoA is finally converted to Pan and cysteamine), used at pharmacological doses for the treatment of hyperlipidemia.

• CoA
• CoAlation
• cancer
• coenzyme A
• neurodegenerative diseases

• Animals
• Biosynthetic Pathways
• Coenzyme A/metabolism
• Humans
• Mammals/metabolism
• Polymorphism, Single Nucleotide/genetics
• Protein Processing, Post-Translational
• Substrate Specificity

• diagnosis and prognosis
• dynamic network biomarker
• edge-based biomarker
• hepatitis B virus
• hepatocellular carcinoma

• biomarkers
• cervical cancer
• peripheral blood
• squamous intraepithelial lesion
• transcriptome

Peripheral blood transcriptome profiling is a potentially important tool for disease detection. We utilize this technique in a case-control study to identify candidate transcriptomic biomarkers able to differentiate women with breast lesions from normal controls.

Whole blood samples were collected from 50 women with high-risk breast lesions, 57 with breast cancers and 44 controls (151 samples). Blood gene expression profiling was carried out using microarray hybridization. We identified blood gene expression signatures using AdaBoost, and constructed a predictive model differentiating breast lesions from controls. Model performance was then characterized by AUC sensitivity, specificity and accuracy. Biomarker biological processes and functions were analyzed for clues to the pathogenesis of breast lesions.

Ten gene biomarkers were identified (YWHAQ, BCLAF1, WSB1, PBX2, DDIT4, LUC7L3, FKBP1A, APP, HERC2P2, FAM126B). A ten-gene panel predictive model showed discriminatory power in the test set (sensitivity: 100%, specificity: 84.2%, accuracy: 93.5%, AUC: 0.99). These biomarkers were involved in apoptosis, TGF-beta signaling, adaptive immune system regulation, gene transcription and post-transcriptional protein modification.

A promising method for the detection of breast lesions is reported. This study also sheds light on breast cancer/immune system interactions, providing clues to new targets for breast cancer immune therapy.

• Adult
• Aged
• Area Under Curve
• Biomarkers, Tumor/genetics
• Breast Neoplasms/blood
• Breast Neoplasms/diagnosis
• Breast Neoplasms/genetics
• Case-Control Studies
• Data Accuracy
• Early Detection of Cancer/methods
• Female
• Humans
• Middle Aged
• Models, Genetic
• Retrospective Studies
• Sensitivity and Specificity
• Transcriptome
• Young Adult

• NDRG4
• PBMC
• TFPI2
• colorectal cancer
• quantitative methylation analysis

• Johne’s disease
• Mycobacterium avium subsp. paratuberculosis
• RNAseq
• biomarker discovery
• bovine
• combinatorial discriminant analysis

• PON01_01841 Ministero dell'Istruzione, dell'Università e della Ricerca

• CD133
• Colorectal cancer
• Extracellular vesicle
• Non-invasive biomarker
• VEGF
• mRNA

• National Research Foundation of Korea

• colorectal cancer
• diagnostic biomarkers
• early detection
• proximity extension assay
• screening
• sensitivity and specificity

• collaborative cross
• gastric cancer
• mouse model
• tumor susceptibility

• Animals
• Carcinogenesis/genetics
• Carcinogenesis/pathology
• Collaborative Cross Mice
• Disease Models, Animal
• Female
• Genetic Predisposition to Disease/etiology
• Male
• Mice
• Stomach Neoplasms/etiology
• Stomach Neoplasms/pathology

• P42 ES027704 NIEHS NIH HHS
• T32 OD011083 NIH HHS
• National Natural Science Foundation of China
• Natural Science Foundation from the Department of Science & Technology of Jiangsu Province
• Lawrence Berkeley National Laboratory
• Office of Naval Research
• National Institutes of Health

• Epigenetics
• Inflammation
• Neighborhood disadvantage
• Racial disparities
• Telomere length
• Triple-negative breast cancer

• Breast Neoplasms/epidemiology
• Breast Neoplasms/ethnology
• Female
• Health Status Disparities
• Humans
• Inflammation/epidemiology
• Inflammation/ethnology
• Racial Groups
• Residence Characteristics
• Stress, Psychological/epidemiology
• Stress, Psychological/ethnology

• R01 CA169127 NCI NIH HHS
• R01 CA239120 NCI NIH HHS
• U01 CA179671 NCI NIH HHS

• Animals
• Carcinogenesis/genetics
• Carcinogenesis/pathology
• Cell Line, Tumor
• Chromatin/metabolism
• Enhancer of Zeste Homolog 2 Protein/metabolism
• Epigenesis, Genetic
• Epithelial Cells/metabolism
• Epithelial Cells/pathology
• Epithelial-Mesenchymal Transition/genetics
• Female
• Gene Expression Regulation, Neoplastic
• Haploinsufficiency/genetics
• Humans
• Lamin Type B/metabolism
• Lung Neoplasms/genetics
• Lung Neoplasms/pathology
• Mice, Inbred BALB C
• Mice, Inbred C57BL
• Neoplasm Grading
• Neoplasm Metastasis
• Phenotype
• Polycomb Repressive Complex 2/metabolism
• Proto-Oncogene Mas
• Proto-Oncogene Proteins c-ret/metabolism
• Signal Transduction
• Up-Regulation/genetics

• Transregional Collaborative Research Centre 81
• Cardio Pulmonary Institute
• German Center for Lung Research

Liver metastasis has been found to affect outcome in prostate, pancreatic and colorectal cancers, but its role in lung cancer is unclear. The 5 year survival rate remains extensively low owing to intrinsic resistance to conventional therapy which can be attributed to the genetic modulators involved in the pathogenesis of the disease. Thus, this study aims to generate a model for early diagnosis and timely treatment of liver metastasis in lung cancer patients.

mRNA expression of 15 genes was quantified by real time PCR on lung cancer specimens with (n = 32) and without (n = 30) liver metastasis and their normal counterparts. Principal Component analysis, linear discriminant analysis and hierarchical clustering were conducted to obtain a predictive model. The accuracy of the models was tested by performing Receiver Operating Curve analysis.

The expression profile of all the 15 genes were subjected to PCA and LDA analysis and 5 models were generated. ROC curve analysis was performed for all the models and the individual genes. It was observed that out of the 15 genes only 8 genes showed significant sensitivity and specificity. Another model consisting of the selected eight genes was generated showing a specificity and sensitivity of 90.0 and 96.87 respectively (p <0.0001). Moreover, hierarchical clustering showed that tumors with a greater fold change lead to poor prognosis.

Our study led to the generation of a concise, biologically relevant multi-gene panel that significantly and non-invasively predicts liver metastasis in lung cancer patients.

• Adult
• Aged
• Aged, 80 and over
• Algorithms
• Biomarkers, Tumor/genetics
• Cell Line, Tumor
• Early Detection of Cancer/methods
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Liver Neoplasms/diagnosis
• Liver Neoplasms/genetics
• Liver Neoplasms/mortality
• Liver Neoplasms/secondary
• Lung Neoplasms/diagnosis
• Lung Neoplasms/genetics
• Lung Neoplasms/mortality
• Lung Neoplasms/pathology
• Male
• Middle Aged
• Oligonucleotide Array Sequence Analysis
• Predictive Value of Tests
• Prognosis
• Sensitivity and Specificity
• Survival Analysis