Associations between hemispheric asymmetry and schizophrenia-related risk genes in people with schizophrenia and people at a genetic high risk of schizophrenia

Background Schizophrenia is considered a polygenic disorder. People with schizophrenia and those with genetic high risk of schizophrenia (GHR) have presented with similar neurodevelopmental deficits in hemispheric asymmetry. The potential associations between neurodevelopmental abnormalities and schizophrenia-related risk genes in both schizophrenia and those with GHR remains unclear. Aims To investigate the shared and specific alternations to the structural network in people with schizophrenia and those with GHR. And to identify an association between vulnerable structural network alternation and schizophrenia-related risk genes. Method A total of 97 participants with schizophrenia, 79 participants with GHR and 192 healthy controls, underwent diffusion tensor imaging (DTI) scans at a single site. We used graph theory to characterise hemispheric and whole-brain structural network topological metrics. For 26 people in the schizophrenia group and 48 in the GHR group with DTI scans we also calculated their schizophrenia-related polygenic risk scores (SZ-PRSs). The correlations between alterations to the structural network and SZ-PRSs were calculated. Based on the identified genetic–neural association, bioinformatics enrichment was explored. Results There were significant hemispheric asymmetric deficits of nodal efficiency, global and local efficiency in the schizophrenia and GHR groups. Hemispheric asymmetric deficit of local efficiency was significantly positively correlated with SZ-PRSs in the schizophrenia and GHR groups. Bioinformatics enrichment analysis showed that these risk genes may be linked to signal transduction, neural development and neuron structure. The schizophrenia group showed a significant decrease in the whole-brain structural network. Conclusions The shared asymmetric deficits in people with schizophrenia and those with GHR, and the association between anomalous asymmetry and SZ-PRSs suggested a vulnerability imaging marker regulated by schizophrenia-related risk genes. Our findings provide new insights into asymmetry regulated by risk genes and provides a better understanding of the genetic–neural pathological underpinnings of schizophrenia.

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From BRCA1 to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

Breast Care ◽

10.1159/000515319 ◽

2021 ◽

pp. 1-12

Author(s):

Emma R. Woodward ◽

Elke M. van Veen ◽

D. Gareth Evans

Keyword(s):

Breast Cancer ◽

High Risk ◽

Reproductive Factors ◽

Risk Scores ◽

Clinical Indication ◽

Nucleotide Polymorphisms ◽

Moderate Risk ◽

Risk Genes ◽

Increased Risk ◽

Gene Panels

Background: There has been huge progress over the last 30 years in identifying the familial component of breast cancer. Summary: Currently around 20% is explained by the high-risk genes BRCA1 and BRCA2, a further 2% by other high-penetrance genes, and around 5% by the moderate risk genes ATM and CHEK2. In contrast, the more than 300 low-penetrance single-nucleotide polymorphisms (SNP) now account for around 28% and they are predicted to account for most of the remaining 45% yet to be found. Even for high-risk genes which confer a 40–90% risk of breast cancer, these SNP can substantially affect the level of breast cancer risk. Indeed, the strength of family history and hormonal and reproductive factors is very important in assessing risk even for a BRCA carrier. The risks of contralateral breast cancer are also affected by SNP as well as by the presence of high or moderate risk genes. Genetic testing using gene panels is now commonplace. Key-Messages: There is a need for a more parsimonious approach to panels only testing those genes with a definite 2-fold increased risk and only testing those genes with challenging management implications, such as CDH1 and TP53, when there is strong clinical indication to do so. Testing of SNP alongside genes is likely to provide a more accurate risk assessment.

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A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6894278 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Ruixiang Luo ◽

Mengjun Huang ◽

Yinhuai Wang

Keyword(s):

Prostate Cancer ◽

Transcription Factors ◽

High Risk ◽

Risk Score ◽

Drug Sensitivity ◽

Risk Model ◽

Low Risk ◽

Risk Scores ◽

Risk Genes ◽

Therapeutic Drugs

Background. Prostate cancer (PC) is one of the most critical cancers affecting men’s health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs. Materials and Methods. In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package “pRRophetic” was applied to drug sensitivity (IC50) value prediction. Results. We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified. Conclusions. Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

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A Filtering Approach to Ergonomics Surveillance

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181397041001158 ◽

1997 ◽

Vol 41 (1) ◽

pp. 722-724

Author(s):

J. Murray Gibson ◽

David C. Alexander

Keyword(s):

Risk Factors ◽

High Risk ◽

Employee Turnover ◽

Low Risk ◽

Risk Scores ◽

Moderate Risk ◽

Risk Levels ◽

Related Risk ◽

Filtering Approach ◽

Ergonomic Risk Factors

There is a need in industry for practical surveillance methods to identify ergonomics problems. Most conventional surveillance methods have the following characteristics: • Require completion of a multi-page checklist for every job in the facility. • Identify, in a “single-pass” survey, all jobs presenting a moderate to low level of ergonomic-related risk, resulting in an “unmanageable” list of problems. • Provide job risk scores used to prioritize every ergonomics problem in the facility. The author presents an alternative surveillance methodology which identifies and prioritizes high, moderate, and low risk jobs using a “filtering approach”. This filtering approach actually consists of three separate checklists, each identifying (or filtering) for jobs of different risk levels: High Risk Survey, Moderate Risk Survey, and Low Risk Survey. Each checklist utilizes data from three sources: ergonomic risk factors, loss information, and employee turnover/complaints.

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White Matter Microstructure in Individuals at Clinical High Risk of Psychosis: A Whole-Brain Diffusion Tensor Imaging Study

Schizophrenia Bulletin ◽

10.1093/schbul/sbt079 ◽

2013 ◽

Vol 40 (4) ◽

pp. 895-903 ◽

Cited By ~ 65

Author(s):

Christian Clemm von Hohenberg ◽

Ofer Pasternak ◽

Marek Kubicki ◽

Thomas Ballinger ◽

Mai-Anh Vu ◽

...

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

High Risk ◽

Diffusion Tensor ◽

Imaging Study ◽

Clinical High Risk ◽

Whole Brain ◽

White Matter Microstructure ◽

Diffusion Tensor Imaging Study

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A TTP-incorporated scoring model for predicting mortality of solid tumor patients with bloodstream infection caused by Escherichia coli

Supportive Care in Cancer ◽

10.1007/s00520-021-06442-z ◽

2021 ◽

Author(s):

Qing Zhang ◽

Hao-Yang Gao ◽

Ding Li ◽

Chang-Sen Bai ◽

Zheng Li ◽

...

Keyword(s):

Escherichia Coli ◽

High Risk ◽

Bloodstream Infection ◽

Solid Tumor ◽

Prognostic Scores ◽

Risk Scores ◽

Youden Index ◽

Tumor Patients ◽

Scoring Model ◽

Significant Difference

Abstract Background Few mortality-scoring models are available for solid tumor patients who are predisposed to develop Escherichia coli–caused bloodstream infection (ECBSI). We aimed to develop a mortality-scoring model by using information from blood culture time to positivity (TTP) and other clinical variables. Methods A cohort of solid tumor patients who were admitted to hospital with ECBSI and received empirical antimicrobial therapy was enrolled. Survivors and non-survivors were compared to identify the risk factors of in-hospital mortality. Univariable and multivariable regression analyses were adopted to identify the mortality-associated predictors. Risk scores were assigned by weighting the regression coefficients with corresponding natural logarithm of the odds ratio for each predictor. Results Solid tumor patients with ECBSI were distributed in the development and validation groups, respectively. Six mortality-associated predictors were identified and included in the scoring model: acute respiratory distress (ARDS), TTP ≤ 8 h, inappropriate antibiotic therapy, blood transfusion, fever ≥ 39 °C, and metastasis. Prognostic scores were categorized into three groups that predicted mortality: low risk (< 10% mortality, 0–1 points), medium risk (10–20% mortality, 2 points), and high risk (> 20% mortality, ≥ 3 points). The TTP-incorporated scoring model showed excellent discrimination and calibration for both groups, with AUC being 0.833 vs 0.844, respectively, and no significant difference in the Hosmer–Lemeshow test (6.709, P = 0.48) and the chi-square test (6.993, P = 0.46). Youden index showed the best cutoff value of ≥ 3 with 76.11% sensitivity and 79.29% specificity. TTP-incorporated scoring model had higher AUC than no TTP-incorporated model (0.837 vs 0.817, P < 0.01). Conclusions Our TTP-incorporated scoring model was associated with improving capability in predicting ECBSI-related mortality. It can be a practical tool for clinicians to identify and manage bacteremic solid tumor patients with high risk of mortality.

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Assessing the clinical utility of genetic risk scores for targeted cancer screening

Journal of Translational Medicine ◽

10.1186/s12967-020-02699-w ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Carly A. Conran ◽

Zhuqing Shi ◽

William Kyle Resurreccion ◽

Rong Na ◽

Brian T. Helfand ◽

...

Keyword(s):

Colorectal Cancer ◽

Primary Care ◽

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Clinical Utility ◽

Low Risk ◽

Risk Scores ◽

Average Risk ◽

Genetic Risk Scores

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.

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Retrospective analysis of hemispheric structural network change as a function of location and size of glioma

Brain Communications ◽

10.1093/braincomms/fcaa216 ◽

2020 ◽

Author(s):

Shawn D’Souza ◽

Lisa Hirt ◽

David R Ormond ◽

John A Thompson

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

Temporal Lobe ◽

Diffusion Tensor ◽

Brain Regions ◽

Structural Network ◽

Tumour Location ◽

The Impact ◽

Tractography Data ◽

Diffusion Tensor Imaging Tractography

Abstract Gliomas are neoplasms that arise from glial cell origin and represent the largest fraction of primary malignant brain tumours (77%). These highly infiltrative malignant cell clusters modify brain structure and function through expansion, invasion and intratumoral modification. Depending on the growth rate of the tumour, location and degree of expansion, functional reorganization may not lead to overt changes in behaviour despite significant cerebral adaptation. Studies in simulated lesion models and in patients with stroke reveal both local and distal functional disturbances, using measures of anatomical brain networks. Investigations over the last two decades have sought to use diffusion tensor imaging tractography data in the context of intracranial tumours to improve surgical planning, intraoperative functional localization, and post-operative interpretation of functional change. In this study, we used diffusion tensor imaging tractography to assess the impact of tumour location on the white matter structural network. To better understand how various lobe localized gliomas impact the topology underlying efficiency of information transfer between brain regions, we identified the major alterations in brain network connectivity patterns between the ipsilesional versus contralesional hemispheres in patients with gliomas localized to the frontal, parietal or temporal lobe. Results were indicative of altered network efficiency and the role of specific brain regions unique to different lobe localized gliomas. This work draws attention to connections and brain regions which have shared structural susceptibility in frontal, parietal and temporal lobe glioma cases. This study also provides a preliminary anatomical basis for understanding which affected white matter pathways may contribute to preoperative patient symptomology.

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A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

Scientific Reports ◽

10.1038/s41598-021-85246-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nan Ma ◽

Lu Si ◽

Meiling Yang ◽

Meihua Li ◽

Zhiyi He

Keyword(s):

High Risk ◽

Roc Curve ◽

Expression Profiles ◽

Low Risk ◽

Stage I ◽

Risk Scores ◽

Training Set ◽

Cox Regression Analysis ◽

Nsclc Patients ◽

Rna Signature

AbstractThere is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

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Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy

Scientific Reports ◽

10.1038/s41598-020-80751-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bastian David ◽

Jasmine Eberle ◽

Daniel Delev ◽

Jennifer Gaubatz ◽

Conrad C. Prillwitz ◽

...

Keyword(s):

Visual Field ◽

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Diffusion Tensor ◽

Global Network ◽

Visual Field Defects ◽

Whole Brain ◽

Network Analyses ◽

Postoperative Mri ◽

Selective Amygdalohippocampectomy

AbstractSelective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyses from voxel- to network-level. Twenty-eight patients with temporal lobe epilepsy underwent pre- and postoperative MRI (T1-MPRAGE and Diffusion Tensor Imaging) as well as kinetic perimetry according to Goldmann standard. We probed for whole-brain gray matter (GM) and white matter (WM) correlates of VFD using voxel-based morphometry and tract-based spatial statistics, respectively. We furthermore reconstructed individual structural connectomes and conducted local and global network analyses. Two clusters in the bihemispheric middle temporal gyri indicated a postsurgical GM volume decrease with increasing VFD severity (FWE-corrected p < 0.05). A single WM cluster showed a fractional anisotropy decrease with increasing severity of VFD in the ipsilesional optic radiation (FWE-corrected p < 0.05). Furthermore, patients with (vs. without) VFD showed a higher number of postoperative local connectivity changes. Neither in the GM, WM, nor in network metrics we found preoperative correlates of VFD severity. Still, in an explorative analysis, an artificial neural network meta-classifier could predict the occurrence of VFD based on presurgical connectomes above chance level.

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Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Healthcare ◽

10.3390/healthcare9060778 ◽

2021 ◽

Vol 9 (6) ◽

pp. 778

Author(s):

Ann-Rong Yan ◽

Indira Samarawickrema ◽

Mark Naunton ◽

Gregory M. Peterson ◽

Desmond Yip ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Venous Thromboembolism ◽

High Risk ◽

Prediction Models ◽

Poor Performance ◽

Risk Models ◽

Related Risk ◽

Wide Range ◽

Ambulatory Patients

Venous thromboembolism (VTE) is a significant cause of mortality in patients with lung cancer. Despite the availability of a wide range of anticoagulants to help prevent thrombosis, thromboprophylaxis in ambulatory patients is a challenge due to its associated risk of haemorrhage. As a result, anticoagulation is only recommended in patients with a relatively high risk of VTE. Efforts have been made to develop predictive models for VTE risk assessment in cancer patients, but the availability of a reliable predictive model for ambulate patients with lung cancer is unclear. We have analysed the latest information on this topic, with a focus on the lung cancer-related risk factors for VTE, and risk prediction models developed and validated in this group of patients. The existing risk models, such as the Khorana score, the PROTECHT score and the CONKO score, have shown poor performance in external validations, failing to identify many high-risk individuals. Some of the newly developed and updated models may be promising, but their further validation is needed.

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