Unveiling causal relationships between addiction phenotypes and inflammatory cytokines: insights from bidirectional mendelian randomization and bibliometric analysis.

Observational studies have suggested associations between multiple inflammatory factors and tobacco and alcohol use, but establishing causation is challenging in epidemiological investigations. We employed genetic association data about the circulating levels of 41 cytokines obtained from the genome-wide association study (GWAS), which contained 8293 Finnish participants. Genetic data on 5 substance use phenotypes were obtained from the GWAS dataset containing 1.2 million European subjects. Then, we conducted a bidirectional mendelian randomization (MR) study. The forward results indicated that smoking cessation was positively correlated with hepatocyte growth factor (HGF), interleukin-10 (IL-10), and stem cell factor (SCF); cigarettes per day was a risk factor associated with high expression in stromal cell-derived factor 1α (SDF-1 A), interferon-γ (IFN-G), IL-4, and granulocyte colony-stimulating factor (G-CSF); drinks per week and smoking initiation were risk factors respectively correlated with reduced HGF and IL-2RA levels. During inverse MR analysis, the findings revealed that both IL-16 and IL-18 increased the risk of cigarettes per day; macrophage inflammatory protein-1ß (MIP-1B) and tumor necrosis factor-ß (TNF-B) inhibited and promoted smoking cessation, respectively; macrophage colony-stimulating factor (M-CSF) elevated the risk of drinks per week, while interferon inducible protein 10 (IP-10) had a contrary role; IL-7 and M-CSF respectively prolonged and shortened age of initiation of regular smoking. This study provides genetic proof supporting a causal relationship between various inflammatory factors and addiction phenotypes. Further comprehensive investigations are required to uncover underlying biological mechanisms. In addition, bibliometric studies have shown that oxidative stress is one of the most important orientations in alcohol and tobacco addiction research, where an in-depth investigation of its pro-inflammatory mechanisms would facilitate the development of potential therapeutic biological targets and drugs.

Hereditary and cortical morphological biomarker of sensitivity to reward in short-term withdrawal methamphetamine abusers.

Higher sensitivity to reward (SR) and weaker sensitivity to punishment (SP) construct the fundamental craving characteristics of methamphetamine abuse. However, few studies have appraised relationships between SR/SP (SR or SP) and cortical morphological alterations in methamphetamine abusers and whether hereditary factors take effects on SR/SP is unclear. Based on surface-based morphometric analysis, cortical discrepancy was investigated between 38 methamphetamine abusers and 37 healthy controls. Within methamphetamine abusers, correlation profiling was performed to discover associations among aberrant neuroimaging substrates, SR, SP, and craving. According to nine single nucleotide polymorphism sites of dopamine-related genes, we conducted univariate general linear model to find different effects of genotypes on cortical alterations and SR/SP/craving (SR, SP, or craving). Ultimately, mediation analyses were conducted among single nucleotide polymorphism sites, SR/SP/craving, and cortical morphological alterations to discover their association pathways. Compared to healthy controls, thinner cortices in inferior temporal gyrus, lateral orbitofrontal cortex, medial orbitofrontal cortex, inferior parietal lobule, and lateral occipital cortex in the left hemisphere were found in methamphetamine abusers (P < 0.05, family-wise error corrected). Cortical thickness in the inferior temporal gyrus was negatively correlated with SR scores. We found that rs1800497 A-containing genotypes had lower cortical thickness in the left inferior parietal lobule than the GG genotype. The rs5751876 had effects on SR scores. This study would provide convincing biomarkers for SR in methamphetamine abusers and offer potential genetic targets for personalizing relapse prevention.

Causal relationship between gut microbiota and glioblastoma: a two-sample Mendelian randomization study.

Background: Observational research and medical trials have suggested a connection between gut microbiota and glioblastoma, but it remains unclear if the relationship is causal. Method: A two-sample Mendelian randomization (MR) study was conducted by employing data from the MiBioGen consortium's largest genome-wide association study (n=18340) and the FinnGen consortium R8 release information (162 cases and 256,583 controls). Inverse variance weighted (IVW), weighted median estimator (WME), weighted model, MR-Egger, simple mode, and MR-PRESSO were used to determine the causal relationship between gut microbiota and glioblastoma. Reverse MR analysis was also performed on bacteria identified as causally related to glioblastoma. Results: Seven causal relationships were identified between genetic liability in the gut microbiota and glioblastoma, involving various bacterial families and genera. No significant causal effect was found on gut microbiota from glioblastoma, and no significant heterogeneity of instrumental variables (IVs) or horizontal pleiotropy was observed. Conclusion: A two-sample MR analysis reveals a causal association between the gut microbiota and glioblastoma, highlighting the need for more investigation to comprehend the processes behind this association.

Altered orientation dispersion index of white matter in individuals with insomnia during the COVID-19 pandemic: A study combining neuroimaging technique and Mendelian randomization.

STUDY OBJECTIVES: Coronavirus disease 2019 (COVID-19) can lead to insomnia. However, associations between COVID-19-caused insomnia and white matter (WM) changes are unclear. METHODS: All subjects had ever been infected with COVID-19. We investigated 89 insomniacs (29 chronic insomniacs, 33 new-onset insomniacs, 27 aggravated insomniacs) and 44 matched non-insomnia participants. Neurite orientation dispersion and density imaging (NODDI) was performed to identify micro-structural alterations of WM, and twelve scales related to sleeping status, memory, attention, learning, emotional status, and executive functions were used. Then, correlations between insomnia/cognitive-behavioral functions and diffusion metrics were tested. To eliminate influence of pre-COVID-19 factors on insomnia, causal relationships between COVID-19 and WM changes were validated by Mendelian randomization (MR) analysis. The significant brain regions of COVID-19-caused insomnia were intersected results of tract-based spatial statistics (TBSS) and MR analyses. RESULTS: Compared to non-insomnia group, insomnia group and its subgroups including post-COVID-19 aggravated or unchanged chronic insomnia group had higher orientation dispersion index (ODI) in extensive brain regions. The left superior longitudinal fasciculus (SLF), left posterior thalamic radiation (PTR), and left cingulate gyrus (CG) were specific brain regions in COVID-19-induced insomnia aggravation. After Bonferroni correction, partial correlation analyses within insomnia group showed that ODI in left SLF was positively correlated with Pittsburgh sleep quality index (PSQI), insomnia severity index (ISI), and self-rating anxiety scale (SAS) scores; ODI in the left PTR was positively correlated with PSQI and ISI scores. CONCLUSIONS: This study is a continuation of our previous research, which provided potential biomarkers for COVID-19-induced insomnia.

The genetic susceptibility analysis of TAAR1 rs8192620 to methamphetamine and heroin abuse and its role in impulsivity.

Abnormal genetic polymorphism of trace amine-associated receptor 1 (TAAR1) rs8192620 site has been confirmed to induce methamphetamine (MA) use and drug craving. However, the genetic susceptibility difference between MA addicts and heroin addicts is unknown. This study evaluated genetic heterogeneity of TAAR1 rs8192620 between MA and heroin addicts and elucidated whether rs8192620 genotypes associated with discrepancy in emotional impulsivity, which would help to instruct individualized treatment in addiction via acting on TAAR1 and evaluate risk of varied drug addiction. Participants consisting of gender-matched 63 MA and 71 heroin abusers were enrolled in the study. Due to mixed drug usage in some MA addicts, MA users were further subdivided into 41 only-MA (only MA taking) and 22 mixed-drug (Magu composed of about 20% MA and 70% caffeine) abusers. Via inter-individual single nucleotide polymorphism (SNP) analysis and two-sample t tests, respectively, the genotypic and Barratt Impulsiveness Scale-11 (BIS-11) scores differences between groups were completed. With following genotypic stratification, the differences in BIS-11 scores between groups were analyzed through two-sample t test. Individual SNP analysis showed significant differences in alleles distribution of rs8192620 between MA and heroin subjects (p = 0.019), even after Bonferroni correction. The TT homozygotes of rs8192620 dominated in MA participants, while C-containing genotypes in heroin (p = 0.026). There was no association of genotypes of TAAR1 rs8192620 with addicts' impulsivity. Our research indicates that the TAAR1 gene polymorphism might mediate the susceptibility discrepancy between MA and heroin abuse.

Identifying Minimal Hepatic Encephalopathy: A New Perspective from Magnetic Resonance Imaging.

Type C hepatic encephalopathy (HE) is a condition characterized by brain dysfunction caused by liver insufficiency and/or portal-systemic blood shunting, which manifests as a broad spectrum of neurological or psychiatric abnormalities, ranging from minimal HE (MHE), detectable only by neuropsychological or neurophysiological assessment, to coma. Though MHE is the subclinical phase of HE, it is highly prevalent in cirrhotic patients and strongly associated with poor quality of life, high risk of overt HE, and mortality. It is, therefore, critical to identify MHE at the earliest and timely intervene, thereby minimizing the subsequent complications and costs. However, proper and sensitive diagnosis of MHE is hampered by its unnoticeable symptoms and the absence of standard diagnostic criteria. A variety of neuropsychological or neurophysiological tests have been performed to diagnose MHE. However, these tests are nonspecific and susceptible to multiple factors (eg, aging, education), thereby limiting their application in clinical practice. Thus, developing an objective, effective, and noninvasive method is imperative to help detect MHE. Magnetic resonance imaging (MRI), a noninvasive technique which can produce many objective biomarkers by different imaging sequences (eg, Magnetic resonance spectroscopy, DWI, rs-MRI, and arterial spin labeling), has recently shown the ability to screen MHE from NHE (non-HE) patients accurately. As advanced MRI techniques continue to emerge, more minor changes in the brain could be captured, providing new means for early diagnosis and quantitative assessment of MHE. In addition, the advancement of artificial intelligence in medical imaging also presents the potential to mine more effective diagnostic biomarkers and further improves the predictive efficiency of MHE. Taken together, advanced MRI techniques may provide a new perspective for us to identify MHE in the future. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Gray Matter Thickness and Subcortical Nuclear Volume in Men After SARS-CoV-2 Omicron Infection.

Importance: The clinical manifestations and effects on the brain of the SARS-CoV-2 Omicron variant in the acute postinfection phase remain unclear. Objective: To investigate the pathophysiological mechanisms underlying clinical symptoms and changes to gray matter and subcortical nuclei among male patients after Omicron infection and to provide an imaging basis for early detection and intervention. Design, Setting, and Participants: In this cohort study, a total of 207 men underwent health screening magnetic resonance imaging scans between August 28 and September 18, 2022; among them, 98 provided complete imaging and neuropsychiatric data. Sixty-one participants with Omicron infection were reevaluated after infection (January 6 to 14, 2023). Neuropsychiatric data, clinical symptoms, and magnetic resonance imaging data were collected in the acute post-Omicron period, and their clinical symptoms were followed up after 3 months. Gray matter indexes and subcortical nuclear volumes were analyzed. Associations between changes in gray matter and neuropsychiatric data were evaluated with correlation analyses. Exposures: Gray matter thickness and subcortical nuclear volume change data were compared before and after Omicron infection. Main Outcomes and Measures: The gray matter indexes and subcutaneous nuclear volume were generated from the 3-dimensional magnetization-prepared rapid acquisition gradient echo and were calculated with imaging software. Results: Ninety-eight men underwent complete baseline data collection; of these, 61 (mean [SD] age, 43.1 [9.9] years) voluntarily enrolled in post-Omicron follow-up and 17 (mean [SD] age, 43.5 [10.0] years) voluntarily enrolled in 3-month follow-up. Compared with pre-Omicron measures, Beck Anxiety Inventory scores were significantly increased (median, 4.50 [IQR, 1.00-7.00] to 4.00 [IQR, 2.00-9.75]; P = .006) and depressive distress scores were significantly decreased (median, 18.00 [IQR, 16.00-20.22] to 16.00 [IQR, 15.00-19.00]; P = .003) at the acute post-Omicron follow-up. Fever, headache, fatigue, myalgia, cough, and dyspnea were the main symptoms during the post-Omicron follow-up; among the participants in the 3-month follow-up, fever (11 [64.7%] vs 2 [11.8%]; P = .01), myalgia (10 [58.8%] vs 3 (17.6%]; P = .04), and cough (12 [70.6%] vs 4 [23.5%]; P = .02) were significantly improved. The gray matter thickness in the left precuneus (mean [SD], 2.7 [0.3] to 2.6 [0.2] mm; P < .001) and right lateral occipital region (mean [SD], 2.8 [0.2] to 2.7 [0.2] and 2.5 [0.2] to 2.5 [0.2] mm; P < .001 for both) and the ratio of the right hippocampus volume to the total intracranial volume (mean [SD]. 0.003 [0.0003] to 0.003 [0.0002]; P = .04) were significantly reduced in the post-Omicron follow-up. The febrile group had reduced sulcus depth of the right inferior parietal region compared with the nonfebrile group (mean [SD], 3.9 [2.3] to 4.8 [1.1]; P = .048. In the post-Omicron period, the thickness of the left precuneus was negatively correlated with the Beck Anxiety Inventory scores (r = -0.39; P = .002; false discovery rate P = .02), and the ratio of the right hippocampus to the total intracranial volume was positively correlated with the Word Fluency Test scores (r = 0.34; P = .007). Conclusions and Relevance: In this cohort study of male patients infected with the Omicron variant, the duration of symptoms in multiple systems after infection was short. Changes in gray matter thickness and subcortical nuclear volume injury were observed in the post-Omicron period. These findings provide new insights into the emotional and cognitive mechanisms of an Omicron infection, demonstrate its association with alterations to the nervous system, and verify an imaging basis for early detection and intervention of neurological sequelae.

Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis.

Background: The performance in evaluating thyroid nodules on ultrasound varies across different risk stratification systems, leading to inconsistency and uncertainty regarding diagnostic sensitivity, specificity, and accuracy. Objective: Comparing diagnostic performance of detecting thyroid cancer among distinct ultrasound risk stratification systems proposed in the last five years. Evidence acquisition: Systematic search was conducted on PubMed, EMBASE, and Web of Science databases to find relevant research up to December 8, 2022, whose study contents contained elucidation of diagnostic performance of any one of the above ultrasound risk stratification systems (European Thyroid Imaging Reporting and Data System[Eu-TIRADS]; American College of Radiology TIRADS [ACR TIRADS]; Chinese version of TIRADS [C-TIRADS]; Computer-aided diagnosis system based on deep learning [S-Detect]). Based on golden diagnostic standard in histopathology and cytology, single meta-analysis was performed to obtain the optimal cut-off value for each system, and then network meta-analysis was conducted on the best risk stratification category in each system. Evidence synthesis: This network meta-analysis included 88 studies with a total of 59,304 nodules. The most accurate risk category thresholds were TR5 for Eu-TIRADS, TR5 for ACR TIRADS, TR4b and above for C-TIRADS, and possible malignancy for S-Detect. At the best thresholds, sensitivity of these systems ranged from 68% to 82% and specificity ranged from 71% to 81%. It identified the highest sensitivity for C-TIRADS TR4b and the highest specificity for ACR TIRADS TR5. However, sensitivity for ACR TIRADS TR5 was the lowest. The diagnostic odds ratio (DOR) and area under curve (AUC) were ranked first in C-TIRADS. Conclusion: Among four ultrasound risk stratification options, this systemic review preliminarily proved that C-TIRADS possessed favorable diagnostic performance for thyroid nodules. Systematic review registration: https://www.crd.york.ac.uk/prospero, CRD42022382818.

Identification of NOX4 as a New Biomarker in Hepatocellular Carcinoma and Its Effect on Sorafenib Therapy.

To improve the survival of patients with hepatocellular carcinoma (HCC), new biomarkers and therapeutic targets are urgently needed. In this study, the GEO and TCGA dataset were used to explore the differential co-expressed genes and their prognostic correlation between HCC and normal samples. The mRNA levels of these genes were validated by qRT-PCR in 20 paired fresh HCC samples. The results demonstrated that the eight-gene model was effective in predicting the prognosis of HCC patients in the validation cohorts. Based on qRT-PCR results, NOX4 was selected to further explore biological functions within the model and 150 cases of paraffin-embedded HCC tissues were scored for NOX4 immunohistochemical staining. We found that the NOX4 expression was significantly upregulated in HCC and was associated with poor survival. In terms of function, the knockdown of NOX4 markedly inhibited the progression of HCC in vivo and in vitro. Mechanistic studies suggested that NOX4 promotes HCC progression through the activation of the epithelial-mesenchymal transition. In addition, the sensitivity of HCC cells to sorafenib treatment was obviously decreased after NOX4 overexpression. Taken together, this study reveals NOX4 as a potential therapeutic target for HCC and a biomarker for predicting the sorafenib treatment response.

Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study.

Disruption of brain resting-state networks (RSNs) is known to be related to stroke exposure, but determining causality can be difficult in epidemiological studies. We used data on genetic variants associated with the levels of functional (FC) and structural connectivity (SC) within 7 RSNs identified from a genome-wide association study (GWAS) meta-analysis among 24,336 European ancestries. The data for stroke and its subtypes were obtained from the MEGASTROKE consortium, including up to 520,000 participants. We conducted a two-sample bidirectional Mendelian randomization (MR) study to investigate the causality relationship between FC and SC within 7 RSNs and stroke and its subtypes. The results showed that lower global mean FC and limbic network FC were associated with a higher risk of any ischemic stroke and small vessel stroke separately. Moreover, ventral attention network FC and default mode network SC have a positive causal relationship with the risk of small vessel stroke and large artery stroke, respectively. In the inverse MR analysis, any stroke and large artery stroke were causally related to dorsal attention network FC and somatomotor FC, respectively. The present study provides genetic support that levels of FC or SC within different RSNs have contrasting causal effects on stroke and its subtypes. Moreover, there is a combination of injury and compensatory physiological processes in brain RSNs following a stroke. Further studies are necessary to validate our results and explain the physiological mechanisms.

Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction.

Drug abuse is a serious problem worldwide. Owing to intermittent intake of certain substances and the early inconspicuous clinical symptoms, this brings huge challenges for timely diagnosing addiction status and preventing substance use disorders (SUDs). As a non-invasive technique, neuroimaging can capture neurobiological signatures of abnormality in multiple brain regions caused by drug consumption in each clinical stage, like parenchymal morphology alteration as well as aberrant functional activity and connectivity of cerebral areas, making it realizable to diagnosis, prediction and even preemptive therapy of addiction. Machine learning (ML) algorithms primarily used for classification have been extensively applied in analysing medical imaging datasets. Significant neurobiological characteristics employed and revealed by classifiers were used to diagnose addictive states and predict initiation and vulnerability to drug usage, treatment abstinence, relapse and resilience of addicts and the risk of SUD. In this review, we summarize application of ML methods in neuroimaging focusing on addicts' diagnosis of clinical status and risk prediction and elucidate the discriminative neurobiological features from brain electrophysiological, morphological and functional perspectives that contribute most to the classifier, finally highlighting the auxiliary role of ML in addiction treatment.

Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders.

Background: Individual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders (SUD). Aims: This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling (CPM). Methods: One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging (fMRI) scans at baseline. The Heroin Craving Questionnaire (HCQ) was used to assess craving levels at baseline and at the 8-month follow-up of abstinence. CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ (HCQfollow-up-HCQbaseline). Then, the predictive ability of identified networks was tested in a separate, heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD. Results: CPM could predict craving changes induced by long-term abstinence, as shown by a significant correlation between predicted and actual HCQfollow-up (r=0.417, p<0.001) and changes in HCQ (negative: r=0.334, p=0.002；positive: r=0.233, p=0.038). Identified craving-related prediction networks included the somato-motor network (SMN), salience network (SALN), default mode network (DMN), medial frontal network, visual network and auditory network. In addition, decreased connectivity of frontal-parietal network (FPN)-SMN, FPN-DMN and FPN-SALN and increased connectivity of subcortical network (SCN)-DMN, SCN-SALN and SCN-SMN were positively correlated with craving levels. Conclusions: These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence, as well as the generalisation ability; the identified brain networks might be the focus of innovative therapies in the future.

Unraveling the molecular mechanisms between inflammation and tumor angiogenesis.

Inflammatory mediators in tumor microenvironment influence cancer occurrence, growth and metastasis through complex signaling networks. Excessive inflammation is closely associated with elevated cancer risk and mortality, in part through inflammation-induced angiogenesis. Mechanistically, multiple tumor-associated inflammatory cells increase the release and accumulation of various inflammatory products in cancerous sites. These products in turn activate tumor associated signaling cascades such as STAT3, NF-κB, PI3K/Akt and p38 MAPK, which mediate the recruitment of inflammatory cells and secretion of pro-inflammatory factors. More importantly, these events promote the secretion of various pro-angiogenesis factors from endothelial, tumor and inflammatory cells, which then drive malignancy in endothelial cells in a paracrine and/or autocrine manner. Its ultimate effect is to promote endothelial cell proliferation, migration, survival and tube formation, and to hence the formation of blood vessels in tumors. This review describes the signaling network that connects the interaction between inflammation and cancer, especially those involved in inflammation-induced angiogenesis. This will reveal potential targets for the design of anti-inflammatory treatments and drugs that inhibites tumor growth and angiogenesis.

Posttranslational Modifications of Smurfs: Emerging Regulation in Cancer.

Smad ubiquitination regulatory factors (Smurfs) belong to the Nedd4 subfamily of HECT-type E3 ubiquitin ligases. Under normal situations, Smurfs are exactly managed by upstream regulators, and thereby strictly control tumor biological processes, including cell growth, differentiation, apoptosis, polarization, epithelial mesenchymal transition (EMT), and invasion. Disruption of Smurf activity has been implicated in cancer progression, and Smurf activity is controlled by a series of posttranslational modifications (PTMs), including phosphorylation, ubiquitination, neddylation, sumoylation, and methylation. The effect and function of Smurfs depend on PTMs and regulate biological processes. Specifically, these modifications regulate the functional expression of Smurfs by affecting protein degradation and protein interactions. In this review, we summarize the complexity and diversity of Smurf PTMs from biochemical and biological perspectives and highlight the understanding of their roles in cancer.

The Clearance of High-Risk Human Papillomavirus is Sooner After Thin Loop Electrosurgical Excision Procedure (t-LEEP).

Purpose/aim of the study: To investigate high-risk human papillomavirus (HPV) infection clearance following thin loop electrosurgical excision procedure (t-LEEP) among patients with cervical benign lesion. Materials and Methods: This retrospective study analyzed clinical data from patients with cervical benign lesion and HPV infection, who had undergone t-LEEP (T-Group), compared with patients with HPV infection undergone no treatment (NT-Group). Both groups attended regular follow-up between January 2008 and January 2012. Kaplan-Meier analysis was used to compare the HPV clearance time. Results: The average clearance time was 7.7 months (M) (95% confidence interval [CI]: 6.5-8.9 M) in T-Group, and 10.4 M (95%CI: 9.4-11.3 M) in NT-Group, with significant difference between groups (p = 0.003). Among patients with low viral load, the HPV clearance times were 7.6 M (95%CI: 6.3-9.0 M) in T-Group and 9.7 M (95%CI: 8.6-10.8 M) in NT-Group (p = 0.042). Among patients with high viral load, the HPV clearance times were 8.0 M (95%CI: 5.3-10.6 M) in T-Group and 11.4 M (95%CI: 9.7-13.1 M) in NT-Group (p = 0.041). The average time of HPV clearance in T-Group was shorter than NT-Group in all age groups, with significant differences in ≤29Y-group (p = 0.008) and 30-39Y-group (p = 0.005). The accumulated clearance rate of HPV infection at sixth month and 12th month were 24.5% and 67.9% in T-Group, 7.8% and 43.1% in NT-Group, with significant differences (p = 0.001 at 6th month, p = 0.032 at 12th month). Conclusions: T-LEEP accelerates the clearance of high-risk HPV infection and make the HPV infection rates dropped rapidly in the first year.

Identification of a microRNA signature associated with survivability in cervical squamous cell carcinoma.

BACKGROUND: The aim of this study is to find the potential miRNA expression signature capable of predicting survival time for cervical squamous cell carcinoma (CSCC) patients. METHODS: The expression of 332 miRNAs was measured in 131 (Training cohort) and 130 (Validation cohort) patients with CSCC in the Cancer Genome Atlas (TCGA) data portal. The miRNA expression signature was identified by Cox Proportion Hazard regression model to the Training data set, and subsequently validated in an independent Validation set. Kaplan-Meier curves and the receiver operating characteristic analyses of 5 years were used to access the overall survival of miRNA signature. MiRNA signature-gene target analysis was performed, followed by the construction of the regulatory network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used to explore the function of target genes of miRNA signature. RESULTS: A 2-miRNA expression signature of hsa-mir-642a and hsa-mir-378c associated with survivability was identified in CSCC. Both of them had a significant diagnostic and prognostic value of patients with CSCC. A total of 345 miRNA signature-target pairs were obtained in the miRNA signature-gene target regulatory network, in which 316 genes were targets of has-mir-378c and has-mir-642a. Functional analysis of target genes showed that MAPK signaling pathway, VEGF signaling pathway and endocytosis were the significantly enriched signal pathways that covered most genes. CONCLUSIONS: The 2-miRNA signature adds to the prognostic value of CSCC. In-depth interrogation of the 2-miRNAs will provide important biological insights that finding and developing novel molecularly prediction to improve prognosis for CSCC patients.

AKIP1 promotes angiogenesis and tumor growth by upregulating CXC-chemokines in cervical cancer cells.

Upregulation of A-kinase-interacting protein 1 (AKIP1) has been observed in breast and esophageal cancers, indicating that AKIP1 may be a potent oncogenic protein. However, the role of AKIP1 in cervical cancer still remains unknown. This study aimed to explore the role of AKIP1 in cervical cancer and to investigate the underlying mechanism of AKIP1 in tumor growth. Expression of AKIP1 in cervical cancer cells was determined by qRT-PCR and western blotting. Cell-Light EdU and colony formation assays were used to determine cell proliferation. CXCL1 and CXCL8 proteins were quantified by ELISA kits. Western blotting and qRT-PCR were used to examine the alterations in signaling-related proteins and mRNA, respectively. Endothelial cell tube formation assay was performed to evaluate the effect of AKIP1 on angiogenesis. A BALB/c nude mouse xenograft model was used to evaluate the role of AKIP1 in vivo. Cancer cell proliferation was inhibited and tumor growth and angiogenesis restrained in BALB/c nude mice by suppressing AKIP1 expression in cervical cancer cell lines. In addition, overexpression of AKIP1 in cervical cancer cells elevated the levels of CXCL1, CXCL2, and CXCL8. These three chemokines were not only involved in endothelial tube formation by binding to the endothelial receptor CXCR2, but also in cervical cancer cell proliferation and clone formation, which were induced by overexpression of AKIP1. Furthermore, we found that AKIP1-induced chemokine expression was decreased by an inhibitor of nuclear factor kappa-B kinase subunit ß. These results show that AKIP1 is crucial in cervical cancer angiogenesis and growth by elevating the levels of the NF-κB-dependent chemokines CXCL1, CXCL2, and CXCL8.

Molecular mechanisms underlying endometriosis pathogenesis revealed by bioinformatics analysis of microarray data.

OBJECTIVE: To identify differentially expressed genes (DEGs) in endometriosis and further analyze molecular mechanisms implicated in disease pathogenesis. MATERIALS AND METHODS: Gene expression data (ID: GSE7846) of human endometrial endothelial cells (HEECs) collected from eutopic endometria tissue of patients with and without endometriosis were downloaded from Gene Expression Omnibus. DEGs were screened using Limma package, followed by enrichment analysis using clusterProfiler package in R. Thereafter, protein-protein interactions (PPIs) were analyzed using STRING (Search Tool for the Retrieval of Interacting Genes) database and visualized by Cytoscape software. Meanwhile, transcription factors were screened from the DEGs based on TRANSFA database, followed by construction of regulatory network using Cytoscape. RESULTS: A total of 2255 up- and 408 down-regulated genes were identified in endometriosis patients as compared with control patients. Those DEGs were predominantly enriched in focal adhesion (e.g., FN1, EGF, FYN, EGFR, RAC1, CCND1 and JUN), regulation of actin cytoskeleton (e.g., FN1, EGF, EGFR, RAC1 and JUN) and MAPK signaling pathway (e.g., EGF, EGFR, RAC1, JUN, TGFB1 and MYC). Importantly, EGF, EGFR, JUN, FN1, RAC1, TGFB1, CCND1 and FYN were hub nodes in the PPI network. Additionally, TGFB1, SMAD1 and SMAD4 showed up-regulation in TGFB signaling pathway. Transcription factor MYC had a regulatory effect on the most DEGs, including TGFB1, RAC1 and CCND1. CONCLUSIONS: Focal adhesion, regulation of actin cytoskeleton, MAPK and TGFB/SMAD signaling pathway may be important molecular mechanism underlying the pathogenesis of endometriosis.

Mir-155 promotes cervical cancer cell proliferation through suppression of its target gene LKB1.

MicroRNAs (miRNAs) are important regulators of many physiological and pathological processes, including cell proliferation, apoptosis, and cell cycle arrest. In this study, we aimed to investigate the biological role of miR-155 in cervical cancer and the underlying molecular mechanism involved in tumorigenesis. The expression of miR-155 in human cervical cancer tissues was detected by real-time PCR. MTT assay and BrdU incorporation assay were used to measure the proliferation of cervical cancer cells. Apoptosis cells and cell cycle distribution were analyzed by flow cytometry. We found that the expression of miR-155 was upregulated in cervical cancer tissues compared to the adjacent non-cancer tissues. Overexpression of miR-155 promoted the proliferation of Hela and SiHa cells. By contrast, downregulation of miR-155 inhibited the growth of cervical cancer cells. Flow cytometry analysis showed that low expression of miR-155 promoted apoptosis and induced cell cycle arrest in Hela and SiHa cells. Moreover, the mRNA and protein expression of LKB1 was significantly reduced in cervical cancer tissues. Luciferase reporter assay demonstrated that LKB1 was a target gene of miR-155, suggesting that miRNA-155 promoted the proliferation of cervical cancer cells by regulating LKB1 expression.