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Introduction: Following acute enterovirus (EV) infection, outcomes vary based on factors like the immune response, viral cell entry receptor expression levels, tissue tropism, and genetic factors of both the host and virus. While most individuals exhibit mild, self-limited symptoms, others may suffer severe complications or prolonged infections that can lead to autoimmune disorders. Methods: To elucidate host responses to EV infection, we performed whole exome sequencing on blood samples from both infected and uninfected individuals. Our initial focus was on genes encoding EV entry receptors-PSGL-1, SCARB2, and ANAXA2 for EV-A71, and CD155 for poliovirus-and on host genes ACBD3 and PI4KΒ, crucial for EV replication. Results: Although no specific genetic variants directly associated with EV infection were identified, we discovered 118 variants across 116 genes enriched in East Asian populations through multi-layered variant filtering. These variants were further analyzed for their potential impacts on organs, biological processes, and molecular pathways. Phenome-wide association studies were conducted to refine our understanding of their contributions to EV infection susceptibility. Discussion: Our findings aim to develop a predictive panel based on these 118 variants, which could help susceptible individuals during EV outbreaks, guiding targeted clinical interventions and preventative strategies.

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Understanding the genetic characteristics of indigenous goat breeds is crucial for their conservation and breeding efforts. Hainan black goats, as a native breed of south China's tropical island province of Hainan, possess distinctive traits such as black hair, a moderate growth rate, good meat quality, and small body size. However, they exhibit exceptional resilience to rough feeding conditions, possess high-quality meat, and show remarkable resistance to stress and heat. In this study, we resequenced the whole genome of Hainan black goats to study the economic traits and genetic basis of these goats, we leveraged whole-genome sequencing data from 33 Hainan black goats to analyze single nucleotide polymorphism (SNP) density, Runs of homozygosity (ROH), Integrated Haplotype Score (iHS), effective population size (Ne), Nucleotide diversity Analysis (Pi) and selection characteristics. Our findings revealed that Hainan black goats harbor a substantial degree of genetic variation, with a total of 23 608 983 SNPs identified. Analysis of ROHs identified 53 710 segments, predominantly composed of short fragments, with inbreeding events mainly occurring in ancient ancestors, the estimates of inbreeding based on ROH in Hainan black goats typically exhibit moderate values ranging from 0.107 to 0.186. This is primarily attributed to significant declines in the effective population size over recent generations. Moreover, we identified 921 candidate genes within the intersection candidate region of ROH and iHS. Several of these genes are associated with crucial traits such as immunity (PTPRC, HYAL1, HYAL2, HYAL3, CENPE and PKN1), heat tolerance (GNG2, MAPK8, CAPN2, SLC1A1 and LEPR), meat quality (ACOX1, SSTR1, CAMK2B, PPP2CA and PGM1), cashmere production (AKT4, CHRM2, OXTR, AKT3, HMCN1 and CDK19), and stress resistance (TLR2, IFI44, ENPP1, STK3 and NFATC1). The presence of these genes may be attributed to the genetic adaptation of Hainan black goats to local climate conditions. The insights gained from this study provide valuable references and a solid foundation for the preservation, breeding, and utilization of Hainan black goats and their valuable genetic resources.

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The detection of genomic sequences and their alterations is crucial for basic research and clinical diagnostics. However, current methodologies are costly and time-consuming and require outsourcing sample preparation, processing, and analysis to genomic companies. Here, we establish One-pot DTECT, a platform that expedites the detection of genetic signatures, only requiring a short incubation of a PCR product in an optimized one-pot mixture. One-pot DTECT enables qualitative, quantitative, and visual detection of biologically relevant variants, such as cancer mutations, and nucleotide changes introduced by prime editing and base editing into cancer cells and human primary T cells. Notably, One-pot DTECT achieves quantification accuracy for targeted genetic signatures comparable with Sanger and next-generation sequencing. Furthermore, its effectiveness as a diagnostic platform is demonstrated by successfully detecting sickle cell variants in blood and saliva samples. Altogether, One-pot DTECT offers an efficient, versatile, adaptable, and cost-effective alternative to traditional methods for detecting genomic signatures.

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BACKGROUND: Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment. METHODS: We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H's (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels. RESULTS: We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression. CONCLUSIONS: The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

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BACKGROUND: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and is the leading cause of cancer death worldwide. Its progression is characterized by genomic instability. In turn, the level of genomic instability affects the prognosis and immune status of patients with LUAD. However, the impact of molecular features associated with genomic instability on the tumor microenvironment (TME) has not been well characterized. In addition, the effect of the genes related to genomic instability in LUAD on individualized treatment of LUAD is unknown. METHODS: The RNA-Sequencing, somatic mutation, and clinical data of LUAD patients were downloaded from publicly available databases. A genetic signature associated with genomic instability (GSAGI) was constructed by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Bioinformatics analysis investigated the differences in prognosis, immune characteristics, and the most appropriate treatment strategy among different subtypes of LUAD patients. CCK-8 and colony formation verified the various effects of Etoposide on different subtypes of LUAD cell lines. Cell-to-cell communication analysis was performed using the "CellChat" R package. The expression of the risk factors in the GSAGI was verified using real-time quantitative PCR (qRT-PCR) and Immunohistochemistry (IHC). RESULTS: We constructed and validated the GSAGI, consisting of five genes: ANLN, RHOV, KRT6A, SIGLEC6, and KLRG2. The GSAGI was an independent prognostic factor for LUAD patients. Patients in the high-risk group distinguished by the GSAGI are more suitable for chemotherapy. More immune cells are infiltrating the tumor microenvironment of patients in the low-risk group, especially B cells. Low-risk group patients are more suitable for receiving immunotherapy. The single-cell level analysis confirmed the influence of the GSAGI on TME and revealed the Mode of action between tumor cells and other types of cells. qRT-PCR and IHC showed increased ANLN, RHOV, and KRT6A expression in the LUAD cells and tumor tissues. CONCLUSION: This study confirms that genes related to genomic instability can affect the prognosis and immune status of LUAD patients. The GSAGI we identified has the potential to guide clinicians in predicting clinical outcomes, assessing immunological status, and even developing personalized treatment plans for LUAD patients.

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Despite their generally favorable prognosis, luminal A tumors paradoxically pose the highest ten-year recurrence risk among breast cancers. From those that relapse, a quarter of them do it within five years after diagnosis. Identifying such patients is crucial, as long-term relapsers could benefit from extended hormone therapy, whereas early relapsers may require aggressive treatment. In this study, we demonstrate that NCAPH plays a role in the pathogenesis of luminal A breast cancer, contributing to its adverse progression in vitro and in vivo. Furthermore, we reveal that a signature of intratumoral gene expression, associated with elevated levels of NCAPH, serves as a potential marker to identify patients facing unfavorable progression of luminal A breast cancer. Indeed, transgenic mice overexpressing NCAPH generated breast tumors with long latency, and in MMTV-NCAPH/ErbB2+ double-transgenic mice, the luminal tumors formed were more aggressive. In addition, high intratumoral levels of Ncaph were associated with worse breast cancer evolution and poor response to chemotherapy in a cohort of genetically heterogeneous transgenic mice generated by backcrossing. In this cohort of mice, we identified a series of transcripts associated with elevated intratumoral levels of NCAPH, which were linked to adverse progression of breast cancer in both mice and humans. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate regression analysis on this series of transcripts, we derived a ten-gene risk score. This score is defined by a gene signature (termed Gene Signature for Luminal A 10 or GSLA10) that correlates with unfavorable progression of luminal A breast cancer. The GSLA10 signature surpassed the Oncotype DX signature in discerning tumors with unfavorable outcomes (previously categorized as Luminal A by PAM50) across three independent human cohorts. This GSLA10 signature aids in identifying patients with Luminal A tumors displaying adverse prognosis, who could potentially benefit from personalized treatment strategies.

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Introduction: Papillary thyroid carcinoma is a type of thyroid cancer that exhibits significant variability in prognosis. Extensive research indicates that the impaired signaling of 1,25(OH)2D3-VDR may be a crucial factor in the development and progression of PTC. Methods: To investigate this further, Integrated analysis mRNA expression information from The Cancer Genome Atlas and GEO, we compared gene expression in cancer and normal tissues and identified differentially expressed genes (DEGs). Through this analysis, we identified DEGs and calculated risk estimates for seven genetic markers. Results: Subsequently, we constructed predictive models using LASSO-Cox regression to test the predictive value of these markers. Our results revealed that 64 calcium metabolism-related genes showed significant differences between tumor and normal tissues. Ten of the identified DEGs were significantly associated with overall survival, indicating their potential role in disease progression. Using the average risk score for the seven genetic markers, we divided patients into high- and low-risk groups. We found that patients in the low-risk group had significantly better overall survival than those in the high-risk group, highlighting the importance of these genetic markers in predicting prognosis. Further analysis using Cox regression demonstrated that the risk levels had independent predictive power. Additionally, we conducted functional analysis of the identified genetic markers, which showed significant differences in immune status between the two patient groups. We also investigated the effect of these calcium metabolism-related genes on thyroid cancer biological functions, immune microenvironment, and drug resistance. Discussion: Our findings provide evidence of a novel genetic signature associated with calcium metabolism, which can predict prognosis in patients with PTC. These results may have significant implications for the development of new diagnostic and therapeutic approaches to improve outcomes for PTC patients.

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BACKGROUND: Glioblastoma poses an inevitable threat to patients despite aggressive therapy regimes. It displays a great level of molecular heterogeneity and numerous substitutions in several genes have been documented. Next-generation sequencing techniques have identified various molecular signatures that have led to a better understanding of the molecular pathogenesis of glioblastoma. In this limited study, we sought to identify genetic variants in a small number of rare patients with aggressive glioblastoma. METHODS: Five tumor tissue samples were isolated from four patients with rapidly growing glioblastoma. Genomic DNA was isolated and whole exome sequencing was used to study protein-coding regions. Generated FASTQ files were analyzed and variants were called for each sample. Variants were prioritized with different approaches and functional annotation was applied for the detrimental variants. RESULTS: A total of 49,780 somatic variants were identified in the five glioblastoma samples studied, with the majority as missense substitutions. The top ten genes with the highest number of substitutions were MUC3A, MUC4, MUC6, OR4C5, PDE4DIP, AHNAK2, OR4C3, ZNF806, TTN, and RP1L1. Notably, variant prioritization after annotation indicated that the MTCH2 (Chr11: 47647265 A>G) gene sequence change was putative deleterious in all of the aggressive tumor samples. CONCLUSION: The MTCH2 (Chr11: 47647265 A>G) gene substitution was identified as putative deleterious in highly aggressive glioblastomas, which merits further investigation. Moreover, a high tumor mutation burden was observed, with a signature of the highest substitutions in MUC3A, MUC4, MUC6, OR4C5, PDE4DIP, AHNAK2, OR4C3, ZNF806, TTN, and RP1L1 genes. The findings provide critical, initial data for the further rational design of genetic screening and diagnostic approaches against aggressive glioblastoma.

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Tibetan goats, Taihang goats, Jining grey goats, and Meigu goats are the representative indigenous goats in China, found in Qinghai-Tibet Plateau, Western pastoral area, Northern and Southern agricultural regions. Very few studies have conducted a comprehensive analysis of the genomic diversity and selection of these breeds. We genotyped 96 unrelated individuals, using goat 53 K Illumina BeadChip array, of the following goat breeds: Tibetan (TG), Taihang (THG), Jining grey (JGG), and Meigu (MGG). A total of 45 951 single nucleotide polymorphisms were filtered to estimate the genetic diversity and selection signatures. All breeds had a high proportion (over 95%) of polymorphic loci. The observed and excepted heterozygosity ranged from 0.338 (MGG) to 0.402 (JGG) and 0.339 (MGG) to 0.395 (JGG), respectively. Clustering analysis displayed a genetically distinct lineage for each breed, and their Fst were greater than 0.25, indicating that they had a higher genetic differentiation between groups. Furthermore, effective population size reduced in all four populations, indicating a loss of genetic diversity. In addition, runs of homozygosity were mainly distributed in 5-10 Mb. Lastly, we identified signature genes, which were closely related to high-altitude adaptation (ADIRF) and prolificity (CNTROB, SMC3, and PTEN). This study provides a valuable resource for future studies on genome-wide perspectives on the diversity and selection signatures of Chinese indigenous goats.

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Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients' survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

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Aging plays an essential role in the development for chronic obstructive pulmonary disease (COPD). The aim of this study was to identify and validate the potential aging-related genes of COPD through bioinformatics analysis and experimental validation. Firstly, we compared the gene expression profiles of aged and young COPD patients using two datasets (GSE76925 and GSE47460) from Gene Expression Omnibus (GEO), and identified 244 aging-related different expressed genes (DEGs), with 132 up-regulated and 112 down-regulated. Then, by analyzing the data for cigarette smoke-induced COPD mouse model (GSE125521), a total of 783 DEGs were identified between aged and young COPD mice, with 402 genes increased and 381 genes decreased. Additionally, functional enrichment analysis revealed that these DEGs were actively involved in COPD-related biological processes and function pathways. Meanwhile, six genes were identified as the core aging-related genes in COPD after combining the human DEGs and mouse DEGs. Eventually, five out of six core genes were validated to be up-regulated in the lung tissues collected from aged COPD patients than young COPD patients, namely NKG7, CKLF, LRP4, GDPD3 and CXCL9. Thereinto, the expressions of NKG7 and CKLF were negatively associated with lung function. These results may expand the understanding for aging in COPD.

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BACKGROUND: To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection. METHODS: We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them. RESULTS: The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities. CONCLUSIONS: Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

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Background: Aging is a strong risk factor and an independent prognostic factor in idiopathic pulmonary fibrosis (IPF). In this study, we aimed to conduct a comprehensive analysis based on gene expression profiles for the role of aging in pulmonary fibrosis. Method: Four datasets (GSE21411, GSE24206, GSE47460, and GSE101286) for patients with clinical IPF and one dataset for bleomycin (BLM)-induced pulmonary fibrosis (BIPF) mouse model (GSE123293) were obtained from Gene Expression Omnibus (GEO). According to different age ranges, both patients with IPF and BIPF mice were divided into young and aged groups. The differently expressed genes (DEGs) were systemically analyzed using Gene Ontology (GO) functional, Kyoto Encyclopedia of Genes and Genomes (KEGG), and hub genes analysis. Finally, we verified the role of age and core genes associated with age in vivo. Results: Via the expression profile comparisons of aged and young patients with IPF, we identified 108 aging-associated DEGs, with 21 upregulated and 87 downregulated. The DEGs were associated with "response to glucocorticoid," "response to corticosteroid," and "rhythmic process" in GO biological process (BP). For KEGG analysis, the top three significantly enriched KEGG pathways of the DEGs included "IL-17 signaling pathway," "Mineral absorption," and "HIF-1-signaling pathway." Through the comparisons of aged and young BIPF mice, a total number of 778 aging-associated DEGs were identified, with 453 genes increased and 325 genes decreased. For GO and KEGG analysis, the DEGs were enriched in extracellular matrix (ECM) and collagen metabolism. The common DEGs of patients with IPF and BIPF mice were enriched in the BP category, including "induction of bacterial agglutination," "hyaluronan biosynthetic process," and "positive regulation of heterotypic cell-cell adhesion." We confirmed that aged BIPF mice developed more serious pulmonary fibrosis. Finally, the four aging-associated core genes (Slc2a3, Fga, Hp, and Thbs1) were verified in vivo. Conclusion: This study provides new insights into the impact of aging on pulmonary fibrosis. We also identified four aging-associated core genes (Slc2a3, Fga, Hp, and Thbs1) related to the development of pulmonary fibrosis.

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BACKGROUND: The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. In addition to The Cancer Genome Atlas (TCGA), simpler tools have been developed, such as the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE). We attempted to determine a genetic signature to build a recurrence risk score in patients diagnosed with low- and intermediate-risk endometrial cancer. METHODS: A case-control study was conducted. The eligible patients were women diagnosed with recurrence low- and intermediate-risk endometrial cancer between January 2009 and December 2014 at a single institution; the recurrence patients were matched to two nonrecurrence patients with the same diagnosis by age and surgical staging. Following RNA isolation of 51 cases, 17 recurrence and 34 nonrecurrence patients, the expression profile was determined using the nCounter® PanCancer Pathways Panel, which contains 770 genes. RESULTS: The expression profile was successfully characterized in 49/51 (96.1%) cases. We identified 12 genes differentially expressed between the recurrence and nonrecurrence groups. The ROC curve for each gene was generated, and all had AUCs higher than 0.7. After backward stepwise logistic regression, four genes were highlighted: FN1, DUSP4, LEF1, and SMAD9. The recurrence risk score was calculated, leading to a ROC curve of the 4-gene model with an AUC of 0.93, sensitivity of 100%, and specificity of 72.7%. CONCLUSION: We identified a four-gene signature that may be associated with recurrence in patients with low- and intermediate-risk endometrial cancer. This finding suggests a new prognostic factor in this poorly explored group of patients with endometrial cancer.

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B. cereus is a human pathogen associated with food poisoning leading to gastrointestinal disorders, as well as local and severe systemic infections. The pathogenic spectrum of B. cereus ranges from strains used as probiotics in humans to lethal highly toxic strains. In this study, we gathered a collection of 100 strains representative of the pathological diversity of B. cereus in humans, and characterized these strains for their cytotoxic potential towards human cells. We analyzed the correlation between cytotoxicity to epithelial and macrophage cells and the combination of 10 genes suspected to play a role during B. cereus virulence. We highlight genetic differences among isolates and studied correlations between genetic signature, cytotoxicity and strain pathological status. We hope that our findings will improve our understanding of the pathogenicity of B. cereus, thereby making it possible to improve both clinical diagnosis and food safety.

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We aimed to isolate circulating tumor cells (CTCs) using a microfluidic technique with a novel lateral magnetophoretic microseparator. Prostate cancer-specific gene expressions were evaluated using mRNA from the isolated CTCs. A CTC-based multigene model was then developed for identifying advanced prostate cancer. Peripheral blood samples were obtained from five healthy donors and patients with localized prostate cancer (26 cases), metastatic hormone-sensitive prostate cancer (mHSPC, 10 cases), and metastatic castration-resistant prostate cancer (mCRPC, 28 cases). CTC recovery rate and purity (enriched CTCs/total cells) were evaluated according to cancer stage. The areas under the curves of the six gene expressions were used to evaluate whether multigene models could identify mHSPC or mCRPC. The number of CTCs and their purity increased at more advanced cancer stages. In mHSPC/mCRPC cases, the specimens had an average of 27.5 CTCs/mL blood, which was 4.2 × higher than the isolation rate for localized disease. The CTC purity increased from 2.1% for localized disease to 3.8% for mHSPC and 6.7% for mCRPC, with increased CTC expression of the genes encoding prostate-specific antigen (PSA), prostate-specific membrane antigen (PSMA), and cytokeratin 19 (KRT19). All disease stages exhibited expression of the genes encoding androgen receptor (AR) and epithelial cell adhesion molecule (EpCAM), although expression of the AR-V7 variant was relatively rare. Relative to each gene alone, the multigene model had better accuracy for predicting advanced prostate cancer. Our lateral magnetophoretic microseparator can be used for identifying prostate cancer biomarkers. In addition, CTC-based genetic signatures may guide the early diagnosis of advanced prostate cancer.

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Establishing a prognostic genetic signature closely related to the tumor immune microenvironment (TIME) to predict clinical outcomes is necessary. Using the Gene Expression Omnibus (GEO) database of a non­small cell lung cancer (NSCLC) cohort and the immune score derived from the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, we applied the least absolute shrinkage and selection operator (LASSO) Cox regression model to screen a 10­gene signature among the 448 differentially expressed genes and found that the risk prediction models constructed by 10 genes could be more sensitive to prognosis than TNM (Tumor, Lymph node and Metastasis) stage (P=0.006). The CIBERSORT method was applied to quantify the relative levels of different immune cell types. It was found that the ratio of eosinophils, mast cells (MCs) resting and CD4 T cells memory activated in the low­risk group was higher than that in the high­risk group, and the difference was statistically significant (P=0.003, P=0.014 and P=0.018, respectively). Inconsistently, the ratio of resting natural killer (NK) cells and activated plasma cells in the low­risk group was significantly lower than that in the high­risk group (P=0.05 and P=0.009, respectively). Kaplan­Meier survival results showed that patients of the high­risk group had significantly shorter overall survival (OS) than those of the low­risk group in the training set (P<0.001). Furthermore, Kaplan­Meier survival showed that patients of the high­risk group had significantly shorter OS than those of the low­risk group (P=0.0025 and P=0.0157, respectively) in the validation set [GSE31210 and TCGA (The Cancer Genome Atlas)]. The 10­gene signature was found to be an independent risk factor for prognosis in univariate and multivariate Cox proportional hazard regression analyses (P<0.001). In addition, it was found that the risk model constructed by the 10­gene signature was related to the clinical related factors in logistic regression analysis. The genetic signature closely related to the immune microenvironment was found to be able to predict differences in the proportion of immune cells (eosinophils, resting MCs, memory activated CD4 T cells, resting NK cells and plasma cells) in the risk model. Our findings suggest that the genetic signature closely related to TIME could predict the prognosis of NSCLC patients, and provide some reference for immunotherapy.

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OBJECTIVES: Impaired vascular pathophysiology and increased cardiovascular (CV) mortality are associated with rheumatoid arthritis (RA). To date, no genomic analysis of RA- and RA treatment-related vascular pathophysiology has been published. In this pilot study, we performed gene expression profiling in association with vascular pathophysiology in RA patients. METHODS: Sixteen and 19 biologic-naïve RA patients were included in study 1 and study 2, respectively. In study 1, genetic signatures determined by microarray were related to flow-mediated vasodilation (FMD), pulse-wave velocity (PWV), and common carotid intima-media thickness (IMT) of patients. In study 2, clinical response (cR) vs non-response (cNR) to 1-year etanercept (ETN) or certolizumab pegol (CZP) treatment, as well as "vascular" response (vR) vs non-response (vNR) to biologics, were also associated with genomic profiles. Multiple testing could not be performed due to the relatively small number of patients; therefore, our pilot study may lack power. RESULTS: In study 1, multiple genes were up- or downregulated in patients with abnormal vs normal FMD, IMT, and PWV. In study 2, there were 13 cR and 6 cNR anti-tumor necrosis factor (TNF)-treated patients. In addition, 10, 9, and 8 patients were FMD-20%, IMT-20%, and PWV-20% responders. Again, vascular responder status was associated with changes of the expression of various genes. The highest number of genes showing significant enrichment were involved in positive regulation of immune effector process, regulation of glucose transport, and Golgi vesicle budding. CONCLUSION: Differential expression of multiple genetic profiles may be associated with vascular pathophysiology associated with RA. Moreover, distinct genetic signatures may also be associated with clinical and vascular responses to 1-year anti-TNF treatment.

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Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all cancers of the thyroid. It is also one of the few cancers with a rapidly increasing incidence. PTC is usually contained within the thyroid gland and generally biologically indolent. Prognosis of the cancer is excellent, with less than 2% mortality at 5 years. However, more than 25% of patients with PTC developed a recurrence during a long term follow-up. The present article provides an updated condensed overview of PTC, which focuses mainly on the molecular alterations involved and recent biomarker investigations.

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Background: The etiology of schizophrenia is extensively debated, and multiple factors have been contended to be involved. A panoramic view of the contributing factors in a genome-wide study can be an effective strategy to provide a comprehensive understanding of its causality. Materials and Methods: GSE53987 dataset downloaded from GEO-database, which comprised mRNA expression data of post-mortem brain tissue across three regions from control (C) and age-matched subjects (T) of schizophrenia (N = Hippocampus [HIP]: C-15, T-18, Prefrontal cortex [PFC]: C-15, T-19, Associative striatum [STR]: C-18, T-18). Bio-conductor-affy-package used to compute mRNA expression, and further t-test applied to investigate differential gene expression. The analysis of the derived genes performed using the PANTHER Classification System and NCBI database. Further, a protein interactome analysis of the derived gene set was performed using STRING v10 database (https://string-db.org/) Results: A set of 40 genes showed significantly altered (p < 0.01) expression across all three brain regions. The analyses unraveled genes implicated in biological processes and events, and molecular pathways relating basic neuronal functions. Conclusions: The aberrant expression of genes maintaining basic cell machinery explains compromised neuronal processing in SCZ.