Genomic Signatures Research Articles

Identification of SNPs in MITF associated with beak color of duck.

Introduction: Beak color-a pigment-related trait-is an important feature of duck breeds. Recently, little research has addressed genetic mechanism of the beak colors in poultry, whereas the process and the regulation factors of melanin deposition have been well described. Methods: To investigate the genetic mechanism of beak colors, we conducted an integrated analysis of genomic selection signatures to identify a candidate site associated with beak color. For this, we used black-billed (Yiyang I meat duck synthetic line H1, H2, H3&HF) and yellow-billed ducks (Cherry Valley ducks and white feather Putian black duck). Quantitative real-time PCR and genotyping approaches were used to verify the function of the candidate site. Results: We identified 3,895 windows containing 509 genes. After GO and KEGG enrichment analysis, nine genes were selected. Ultimately, MITF was selected by comparing the genomic differentiation (FST). After loci information selection, 41 extreme significantly different loci were selected, which are all located in intron regions of MITF and are in almost complete linkage disequilibrium. Subsequently, the site ASM874695v1:10:g.17814522T > A in MITF was selected as the marker site. Furthermore, we found that MITF expression is significantly higher in black-beaked ducks than in yellow-beaked ducks of the F2 generation (p < 0.01). After genotyping, most yellow-billed individuals are found with homozygous variant; at the same time, there are no birds with homozygous variant in black-billed populations, while the birds with homozygous and heterozygous variant share the same proportion. Conclusion: MITF plays a very critical role in the melanogenesis and melanin deposition of duck beaks, which can effectively affect the beak color. The MITF site, ASM874695v1:10:g.17814522T > A could be selected as a marker site for the duck beak color phenotype.

Genomic Scan for Runs of Homozygosity and Selective Signature Analysis to Identify Candidate Genes in Large White Pigs.

Large White pigs are extensively utilized in China for their remarkable characteristics of rapid growth and the high proportion of lean meat. The economic traits of pigs, comprising reproductive and meat quality traits, play a vital role in swine production. In this study, 2295 individuals, representing three different genetic backgrounds Large White pig populations were used: 500 from the Canadian line, 295 from the Danish line, and 1500 from the American line. The GeneSeek 50K GGP porcine HD array was employed to genotype the three pig populations. Firstly, genomic selective signature regions were identified using the pairwise fixation index (FST) and locus-specific branch length (LSBL). By applying a top 1% threshold for both parameters, a total of 888 candidate selective windows were identified, harbouring 1571 genes. Secondly, the investigation of regions of homozygosity (ROH) was performed utilizing the PLINK software. In total, 25 genomic regions exhibiting a high frequency of ROHs were detected, leading to the identification of 1216 genes. Finally, the identified potential functional genes from candidate genomic regions were annotated, and several important candidate genes associated with reproductive traits (ADCYAP1, U2, U6, CETN1, Thoc1, Usp14, GREB1L, FGF12) and meat quality traits (MiR-133, PLEKHO1, LPIN2, SHANK2, FLVCR1, MYL4, SFRP1, miR-486, MYH3, STYX) were identified. The findings of this study provide valuable insights into the genetic basis of economic traits in Large White pigs and may have potential use in future pig breeding programs.

IDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.

We present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic identifiers. iDeLUCS is scalable and user-friendly: its graphical user interface, with support for hardware acceleration, allows the practitioner to fine-tune the different hyper-parameters involved in the training process without requiring extensive knowledge of deep learning. The performance of iDeLUCS was evaluated on a diverse set of datasets: several real genomic datasets from organisms in kingdoms Animalia, Protista, Fungi, Bacteria, and Archaea, three datasets of viral genomes, a dataset of simulated metagenomic reads from microbial genomes, and multiple datasets of synthetic DNA sequences. The performance of iDeLUCS was compared to that of two classical clustering algorithms (k-means++ and GMM) and two clustering algorithms specialized in DNA sequences (MeShClust v3.0 and DeLUCS), using both intrinsic cluster evaluation metrics and external evaluation metrics. In terms of unsupervised clustering accuracy, iDeLUCS outperforms the two classical algorithms by an average of ∼20%, and the two specialized algorithms by an average of ∼12%, on the datasets of real DNA sequences analyzed. Overall, our results indicate that iDeLUCS is a robust clustering method suitable for the clustering of large and diverse datasets of unlabeled DNA sequences. iDeLUCS is available at https://github.com/Kari-Genomics-Lab/iDeLUCS under the terms of the MIT licence.

Genomic signatures and prognosis of advanced stage Chinese pediatric T cell lymphoblastic lymphoma by whole exome sequencing.

To investigate the genomic signatures and prognosis of advanced-stage T cell lymphoblastic lymphoma (T-LBL) and to examine the relationship between T-LBL and T cell acute lymphoblastic leukemia (T-ALL). 35 Chinese T-LBL children with stage III or IV disease were recruited for this study. They were treated with combination chemotherapy and whole exome sequencing. The relationship of the clinical features, prognosis and specific gene mutations was researched. Gene chips of T-LBL and T-ALL were downloaded from a database, and differential gene expression was analyzed. Germline causal gene mutations (CARS or MAP2K2) were detected in 2 patients; 3.06 ± 2.21 somatic causal gene mutations were identified in the 35 patients, and somatic mutations were observed in the NOTCH1, FBXW7, PHF6 and JAK3 genes. NOTCH1 mutations were significantly associated with FBXW7 mutations, and the age at diagnosis of patients with NOTCH1-FBXW7 mutations was less than that of patients without such mutations (P < 0.05). 32 patients achieved complete remission (CR), and 14 and 18 patients were classified into the intermediate risk (IR) group and high risk (HR) group. During a median follow-up of 44 months, 3 patients relapsed. Three-year prospective event free survival (pEFS) was 82.286%, and no significant differences of pEFS were found for different sexes, ages, or statuses of NOTCH1-FBXW7 mutations, (P > 0.05); however, the mean survival time of the IR group was longer than that of the HR group (P < 0.05). Differential expression of genes in the T-LBL and/or T-ALL datasets was analyzed using the R package limma, and 1/3 of the differentially expressed genes were found in both the T-ALL and T-LBL datasets. High expression of PI3K-Akt signal pathway genes and the USP34 gene was found in the T-LBL dataset. Although T-ALL and T-LBL both originate from precursor T-cells and are considered different manifestations of the same disease and the outcome of T-LBL is favorable when using T-ALL-based chemotherapy, there are differences in the gene distribution between T-LBL and T-ALL. It seems that the PI3K-Akt signaling pathway and the USP34 gene play important roles in T-LBL, but medicines targeting the USP34 gene or the PI3K-Akt pathway may be invalid.

Identification of ferroptosis-related molecular subtypes and a methylation-related ferroptosis gene prognostic signature in cervical squamous cell carcinoma.

We aimed to investigate the molecular characteristics of cervical squamous cell carcinoma (CESC) by analyzing ferroptosis-related gene (FRG) expression data to predict prognosis. Gene expression and clinicopathological data of patients with CESC were collected from the Cancer Genome Atlas and the Genotype-Tissue Expression databases. Using Cox regression analysis, we identified 21 FRGs associated with prognosis. Cluster analysis categorized patients into subgroups based on these genes and compared their clinicopathological, biological, and immune infiltration features. FRG methylation levels were examined, and a risk model based on such FRG methylation levels was constructed using LASSO and Cox regression analyses. The model's predictive capacity was validated, and the relationships between the risk score and immune infiltration, tumor microenvironment, and drug sensitivity were explored. FRG methylation in CESC tissues was validated by immunohistochemistry. We identified 21 FRGs associated with CESC prognosis. Patients were stratified into two subtypes based on these genes, they showed differences in prognosis, immune cell types, and immune checkpoint expression. A three-gene risk score (including AQP3, MGST1, and TFRC) was generated, and the low-risk group showed better overall survival. The high-risk and low-risk groups differed in terms of immune infiltration, gene mutations, and drug sensitivity. Experimental validation confirmed the upregulation of AQP3 and TFRC, whereas MGST1 expression was not significantly altered in CESC tissues compared with that in normal cervical tissues. This study highlights the potential role of FRG methylation in predicting CESC prognosis and provides a personalized assessment of immune responses in patients with CESC.

A novel telomere-related gene prognostic signature for survival and drug treatment efficiency prediction in lung adenocarcinoma.

Telomere-related genes (TRGs) play a critical role in various types of tumors. However, there is a lack of comprehensive exploration of their relevance in lung cancer. This research aimed to verify the relationship between TRGs gene expression and the prognosis of patients with lung adenocarcinoma (LUAD), as well as the prediction of drug treatment efficiency. A total of 2093 TRGs were acquired from TelNet. The clinical information including age, tumor stage, follow up and outcome (death/survival) and TRGs expression profile of LUAD were obtained from the patients in The Cancer Genome Atlas (TCGA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. The two databases were used to construct and verify a prognostic model based on the expression of hubTRGs. The tumor mutation burden, immune infiltration and subtypes, as well as IC50 prediction of multiple targeted drugs were also evaluated in TRGs-divided risk groups. A total of 335 TRGs were significantly differentially expressed in LUAD as compared with normal control. Among them, 9 TRGs (ABCC2, ABCC8, ALDH2, FOXP3, GNMT, JSRP1, MACF1, PLCD3, SULT4A1) were finally identified as hubGenes and used to construct a TRG risk score. The TRG risk score showed favorable performance in constructing a prognostic nomogram in predicting survival of LUAD, and the ROC curves at 1, 3 and 5 years were plotted and the AUROC values were 0.743, 0.754 and 0.735, respectively. Higher TRGs risk score correlated with worse immune subtypes and higher tumor mutation burden in LUAD tissues. In addition, the patients in TRG high risk group harbored a lower TIDE score which indicated potentially better response to immunotherapy. This study proposed a broad molecular signature of telomere-related genes that can be used in further functional and therapeutic investigations, and also represents an integrated modality for characterizing critical molecules when exploring novel targets for lung cancer immunotherapy.

Multi-locus genomic signatures of local adaptation to snow across the landscape in California populations of a willow leaf beetle.

Organisms living in mountains contend with extreme climatic conditions, including short growing seasons and long winters with extensive snow cover. Anthropogenic climate change is driving unprecedented, rapid warming of montane regions across the globe, resulting in reduced winter snowpack. Loss of snow as a thermal buffer may have serious consequences for animals overwintering in soil, yet little is known about how variability in snowpack acts as a selective agent in montane ecosystems. Here, we examine genomic variation in California populations of the leaf beetle Chrysomela aeneicollis, an emerging natural model system for understanding how organisms respond to climate change. We used a genotype-environment association approach to identify genomic signatures of local adaptation to microclimate in populations from three montane regions with variable snowpack and a coastal region with no snow. We found that both winter-associated environmental variation and geographical distance contribute to overall genomic variation across the landscape. We identified non-synonymous variation in novel candidate loci associated with cytoskeletal function, ion transport and membrane stability, cellular processes associated with cold tolerance in other insects. These findings provide intriguing evidence that variation in snowpack imposes selective gradients in montane ecosystems.

Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma.

Objective: Epithelial-mesenchymal transition (EMT) is linked to an unfavorable prognosis in oral squamous cell carcinoma (OSCC). Here, we aimed to develop an EMT gene signature for OSCC prognosis. Methods: In TCGA dataset, prognosis-related EMT genes with p < 0.05 were screened in OSCC. An EMT gene signature was then conducted with LASSO method. The efficacy of this signature in predicting prognosis was externally verified in the GSE41613 dataset. Correlations between this signature and stromal/immune scores and immune cell infiltration were assessed by ESTIMATE and CIBERSORT algorithms. GSEA was applied for exploring significant signaling pathways activated in high- and low-risk phenotypes. Expression of each gene was validated in 40 paired OSCC and normal tissues via RT-qPCR. Results: A prognostic 9-EMT gene signature was constructed in OSCC. High risk score predicted poorer clinical outcomes than low risk score. ROCs confirmed the well performance on predicting 1-, 3- and 5-year survival. Multivariate cox analysis revealed that this signature was independently predictive of OSCC prognosis. The well predictive efficacy was validated in the GSE41613 dataset. Furthermore, this signature was distinctly related to stromal/immune scores and immune cell infiltration in OSCC. Distinct pathways were activated in two subgroups. After validation, AREG, COL5A3, DKK1, GAS1, GPX7 and PLOD2 were distinctly upregulated and SFRP1 was downregulated in OSCC than normal tissues. Conclusion: Our data identified and verified a robust EMT gene signature in OSCC, which provided a novel clinical tool for predicting prognosis and several targets against OSCC therapy.

Exploration of pyroptosis-associated prognostic gene signature and lncRNA regulatory network in ovarian cancer

Ovarian cancer (OC), is a tumor that poses a serious threat to women's health due to its high mortality rate and bleak prognosis. Pyroptosis, a type of programmed cell death, is important for determining the prognosis of a patient's prognosis for cancer and may represent a novel target for treatment. However, research into how prognosis is impacted by pyroptosis-related genes (PRGs) is poorly understood. In this study, a prognostic model was created using bioinformatic analysis of PRGs in OC. In OC, we discovered 18 pyroptosis regulators that were either up- or down-regulated. By analyzing prognoses, we developed a 9-genes based prognostic model. Each OC patient received a risk score that could be used to categorize them into two subgroups: those with high risk and/or low chance of survival and those with low risk and/or high chance of survival. Functional enrichment and immunoinfiltration analysis indicated that low expression of immune pathways in high-risk group may account for the decrease of survival possibility. In Multivariable cox regression studies, age, clinical stage and the prognostic model were discovered to be independent factors impacting the prognosis for OC. To forecast OC patient survival, a predictive nomogram was developed. Furthermore, we found a correlation between predictive PRGs and clinical stage, indicating that AIM2, CASP3, ZBP1 and CASP8 may play a role in the growth of tumor in OC. After detailed and complete bioinformatics analysis, the lncRNA RP11-186B7.4/hsa-miR-449a/CASP8/AIM2/ZBP1 regulatory axis was identified in OC. Our study may provide a novel approach for prognostic biomarkers and therapeutic targets of OC.

Association between 21-gene-assay and detection of disseminated tumor cells in patients with early breast cancer: results from the IRMA trial

PurposeDisseminated tumor cells (DTCs) in the bone marrow (BM) are known to be of prognostic value for patients with early breast cancer (EBC). In addition to histopathological features, multigene expression assays, such as the commercially available 21-gene Breast Recurrence Score® assay, have been validated for evaluating prognosis and making decisions concerning adjuvant treatment in EBC. In a previous retrospective study from our group, the 21-gene assay was shown to be associated with DTC-detection. A secondary endpoint of the prospective IRMA trial was to evaluate the association between Recurrence Score® (RS) result and tumor cell dissemination in patients with EBC.MethodsDTC-status and RS result were assessed in patients with ER-positive/HER2-negative EBC with 0–3 pathologic lymph nodes who underwent primary surgical treatment at the Department for Women’s Health of Tuebingen University, Germany.ResultsPatients with a high RS result (≥ 26) were more frequently DTC-positive (22.6%) than patients with a low RS result (8.6%, p = 0.034). The odds for DTC-positivity increased with rising RS values (p = 0.047).ConclusionWe therefore confirm that a high genomic risk is associated with tumor cell dissemination into the BM. Further trials are needed to investigate whether therapeutic decisions could be further individualized by combining DTC-status and prognostic gene signature testing.

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.

Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Identification of a Predictive Genomic Biomarker for Prostate-directed Therapy in Synchronous Low-volume Metastatic Castration-sensitive Prostate Cancer

BackgroundStandard of care management for synchronous metastatic castration-sensitive prostate cancer (mCSPC) includes androgen deprivation therapy with a second-generation antiandrogen therapy and/or docetaxel. Recently, randomized data have demonstrated that prostate-directed therapy (PDT) is associated with an improvement in overall survival (OS) among patients with low-volume metastatic disease. Tumor genomics represents an additional dimension to define the clinical trajectory of patients with mCSPC. ObjectiveTo evaluate a high-risk (HiRi) genomic signature to predict the benefit from PDT. Design, setting, and participantsWe performed a single-institution retrospective review of men with synchronous low-volume mCSPC who underwent DNA panel sequencing of their tumor. Patients were classified according to the presence of HiRi mutation including pathogenic mutations in TP53, ATM, BRCA1, BRCA2, or Rb1. Outcome measurements and statistical analysisThe primary endpoint was to determine the effect of PDT on OS in patients with and without a HiRi mutation. A survival analysis was performed with the Kaplan-Meier method compared with log-rank test and multivariable Cox regression. The interaction between HiRi mutation and PDT was evaluated. Results and limitationsA total of 101 patients with synchronous low-volume CSPC were included with a median follow-up of 44 mo. Approximately half of patients were found to have a HiRi pathogenic mutation (49%). Patients with HiRi mutations demonstrated median OS of 73 versus 66.8 mo (p = 0.3) for no PDT versus PDT. Conversely, patients without a HiRi mutation demonstrated a significant improvement in OS of 60 versus 105.3 mo (p < 0.001) for no PDT versus PDT. The p value for interaction for OS between PDT and HiRi mutation was statistically significant (p < 0.001). Limitations include the retrospective nature of the study. ConclusionsHere, we have identified a HiRi genomic biomarker that appears predictive for the lack of benefit from PDT in men with synchronous low-volume mCSPC. Further work validating these results is warranted. Patient summaryIn this report, we evaluated a high-risk genomic biomarker to predict the benefit from prostate-directed therapy for men with synchronous low-volume metastatic castration-sensitive prostate cancer. We found that men without a high-risk mutation appear to experience a greater clinical benefit from prostate-directed therapy than those with a high-risk mutation.

Climate has contributed to population diversification of Daphnia galeata across Eurasia.

Climate is a fundamental abiotic factor that plays a key role in driving the evolution, distribution and population diversification of species. However, there have been few investigations of genomic signatures of adaptation to local climatic conditions in cladocerans. Here, we have provided the first high-quality chromosome-level genome assembly (~143 Mb, scaffold N50 12.6 Mb) of the waterflea, Daphnia galeata, and investigated genomic variation in 22 populations from Central Europe and Eastern China. Our ecological-niche models suggested that the historic distribution of D. galeata in Eurasia was significantly affected by Quaternary climate fluctuations. We detected pronounced genomic and morphometric divergences between European and Chinese D. galeata populations. Such divergences could be partly explained by genomic signatures of thermal adaptation to distinct climate regimes: a set of candidate single-nucleotide polymorphisms (SNPs) potentially associated with climate were detected. These SNPs were in genes significantly enriched in the Gene ontology terms "determination of adult lifespan" and "translation repressor activity", and especially, mthl5 and SOD1 involved in the IIS pathway, and EIF4EBP2 involved in the target of the rapamycin signalling pathway. Our study indicates that certain alleles might be associated with particular temperature regimes, playing a functional role in shaping the population structure of D. galeata at a large geographical scale. These results highlight the potential role of molecular variation in the response to climate variation, in the context of global climate change.

Genomic alterations as independent prognostic factors to predict the type of lung cancer recurrence

Introduction33–70% of lung cancer (LC) patients develop recurrence after radical treatment. Previous studies have shown the importance of clinical-pathological characteristics for the risk of recurrence. The role of molecular mechanisms remains unclear. The aim was analyzing genomic features in LC patients with local (LR) versus distant recurrence (DR) to predict the risk and type of recurrence. Materials and methodsPatients previously curatively treated with LC recurrences from 2015 to 2017 were retrospectively enrolled. Histological specimens collected at the time of LC diagnosis were sent for whole exome sequencing (WES). Genomic data was analyzed for single nucleotide polymorphisms (SNPs) and insertion-deletion mutations (INDELs). Results191 patients were included. 33% of patients had LR and 67% DR, with median recurrence-free survival (RFS) 15.4 versus 11.2 months (p = 0.20) and overall survival (OS) after recurrence 12.9 versus 8.5 months (p = 0.007), respectively. Of various laboratory parameters studied, lymphocytes were significantly decreased at recurrence (p < 0.0001) in the DR group.In genetic analysis, significantly enriched INDEL mutations were found in 38 and 98 genes and SNP mutations in 63 and 179 genes in DR and LR groups, respectively. DMXL2 and ABCC9 gene mutations caused by INDELs appeared exclusively in the DR group. Enrichment analysis detected genes, like KNTC1, CLASP1, CLASP2 and CENPE, responsible of microtubule disturbance in the DR group. Furthermore, genes related to cytosolic Ca2+ such as STIM1, ITPR3 and RYR3, were significantly enriched in DR group whereas in LR group enrichment of pathways related to endoplasmic/sarcoplasmic reticulum Ca2+ was observed. ConclusionOur findings indicate distinct genomic signatures in the LR and DR cohorts, with microtubule disturbance and calcium regulation playing a crucial role in invasiveness in DR of LC. Understanding molecular mechanisms of LC recurrence may lead to the discovery of novel drug targets that could potentially stop spread of cancer cells.

Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma: Association with Metabolic Tumor Burden Determined with FDG-PET/CT.

The detection of circulating tumor DNA (ctDNA) with next-generation sequencing (NGS) in venous blood is a promising tool for the genomic profiling of head and neck squamous cell carcinoma (HNSCC). The association between ctDNA findings and metabolic tumor burden detected with FDG-PET/CT imaging is of particular interest for developing prognostic and predictive algorithms in HNSCC. Twenty-six prospectively enrolled HNSCC patients were eligible for further analysis. All patients underwent tumor tissue and venous liquid biopsy sampling and FDG-PET/CT before definitive oncologic treatment. An NGS-based commercial panel was used for a genomic analysis of the samples. Maximum variant allele frequency (VAF) in blood correlated positively with whole-body (WB) metabolic tumor volume (MTV) and total lesion glycolysis (TLG) (r = 0.510, p = 0.008 and r = 0.584, p = 0.002, respectively). A positive liquid biopsy was associated with high WB-TLG using VAF ≥ 1.00% or ≥5.00% as a cut-off value (p = 0.006 or p = 0.003, respectively). Additionally, ctDNA detection was associated with WB-TLG when only concordant variants detected in both ctDNA and tissue samples were considered. A high metabolic tumor burden based on FDG imaging is associated with a positive liquid biopsy and high maximum VAF. Our findings suggest a complementary role of metabolic and genomic signatures in the pre-treatment evaluation of HNSCC.

Glioblastoma vascular plasticity limits effector T-cell infiltration and is blocked by cAMP activation.

Glioblastoma (GBM) is the deadliest form of brain cancer, characterized as a highly angiogenetic and immunosuppressive malignancy. Although immune checkpoint blockades have revolutionized cancer therapy, their therapeutic efficacy in GBM has been far less than expected or even ineffective. Here, we reveal that the genomic signature of glioma-derived endothelial cells (GdECs) correlates with an immunosuppressive state and poor prognosis of glioma patients. In vitro model of GdECs differentiation is established for drug screening, and we identify that cAMP activators could effectively block the GdECs formation by inducing oxidative stress. cAMP activator impairs the GdECs differentiation in vivo, normalizes the tumor vessels, and alters the immune profile, especially increasing the influx and function of CD8+ effector T cells. The dual blockade of GdECs and PD-1 induces tumor regression and establishes anti-tumor immune memory. Our study reveals that endothelial transdifferentiation of GBM shapes an endothelial immune cell barrier and supports the clinical development of combining GdECs blockade and immunotherapy for GBM.

Mutation-derived, genomic instability-associated lncRNAs are prognostic markers in gliomas.

Gliomas are the most commonly-detected malignant tumors of the brain. They contain abundant long non-coding RNAs (lncRNAs), which are valuable cancer biomarkers. LncRNAs may be involved in genomic instability; however, their specific role and mechanism in gliomas remains unclear. LncRNAs that are related to genomic instability have not been reported in gliomas. The transcriptome data from The Cancer Genome Atlas (TCGA) database were analyzed. The co-expression network of genomic instability-related lncRNAs and mRNA was established, and the model of genomic instability-related lncRNA was identified by univariate Cox regression and LASSO analyses. Based on the median risk score obtained in the training set, we divided the samples into high-risk and low-risk groups and proved the survival prediction ability of genomic instability-related lncRNA signatures. The results were verified in the external data set. Finally, a real-time quantitative polymerase chain reaction assay was performed to validate the signature. The signatures of 17 lncRNAs (LINC01579, AL022344.1, AC025171.5, LINC01116, MIR155HG, AC131097.3, LINC00906, CYTOR, AC015540.1, SLC25A21.AS1, H19, AL133415.1, SNHG18, FOXD3.AS1, LINC02593, AL354919.2 and CRNDE) related to genomic instability were identified. In the internal data set and Gene Expression Omnibus (GEO) external data set, the low-risk group showed better survival than the high-risk group (P < 0.001). In addition, this feature was identified as an independent risk factor, showing its independent prognostic value with different clinical stratifications. The majority of patients in the low-risk group had isocitrate dehydrogenase 1 (IDH1) mutations. The expression levels of these lncRNAs were significantly higher in glioblastoma cell lines than in normal cells. Our study shows that the signature of 17 lncRNAs related to genomic instability has prognostic value for gliomas and could provide a potential therapeutic method for glioblastoma.

Construction of a genomic instability-derived predictive prognostic signature for non-small cell lung cancer patients

BackgroundGenomic instability (GI) is an effective prognostic marker of cancer. Thus, in this work, we aimed to explore the impact of GI derived signature on prognosis in non-small cell lung cancer (NSCLC) patients using bioinformatics methods. MethodsThe data of NSCLC patients were collected from The Cancer Genome Atlas. Totally 1794 immune related genes were downloaded from immport database. The optimal prognosis related genes were identified by univariate and LASSO Cox analyses. The risk score model was built to predict the NSCLC patients’ prognosis. The immune cell infiltration was analyzed in CIBERSORT. ResultsThe 951 differentially expressed genes (DEGs) between the genomic stability (GS) and GI groups were enriched in 862 Gene ontology terms and 32 Kyoto Encyclopedia of Genes and Genomes pathways. Based on the 13 optimal genes, a prognostic risk score mode for NSCLC was established, and the high-risk patients exhibited worse overall survival. Moreover, the nomogram could reliably predict the clinical outcomes. The immune cell infiltration and checkpoints were significantly differential between the two groups (high-risk and low-risk). ConclusionThe GI related 13-gene signature (TMPRSS11E, TNNC2, HLF, FOXM1, PKMYT1, TCN1, RGS20, SYT8, CD1B, LY6K, MFSD4A, KLRG2 APCDD1L) could reliably predict the prognosis of NSCLC patients.

Updates on accelerated and blast phase myeloproliferative neoplasms: Are we making progress?

Management approaches for accelerated and blast phase myeloproliferative neoplasms remain challenging for clinicians and patients alike. Despite many therapeutic advances, outcomes for those patients who are not allogeneic haematopoietic cell transplant eligible remain, in general, very poor. Estimated survival rates for such blast phase patients is frequently reported as less than 6 months. No specific immunological, genomic or clinicopathological signature currently exists that accurately predicts the risk and timing of transformation, which frequently induces a high degree of anxiety among patients and clinicians alike. Within this review article, we provide an up-to-date summary of current understanding of the underlying pathogenesis of accelerated and blast phase disease and discuss current therapeutic approaches and realistic outcomes. Finally, we discuss how the horizon may look with the introduction of more novel agents into the clinical arena.

Ghost introgression facilitates genomic divergence of a sympatric cryptic lineage in Cycas revoluta.

A cryptic lineage is a genetically diverged but morphologically unrecognized variant of a known species. Clarifying cryptic lineage evolution is essential for quantifying species diversity. In sympatric cryptic lineage divergence compared with allopatric divergence, the forces of divergent selection and mating patterns override geographical isolation. Introgression, by supplying preadapted or neutral standing genetic variations, can promote sympatric cryptic lineage divergence via selection. However, most studies concentrated on extant species introgression, ignoring the genetic legacy of introgression from extinct or unsampled lineages ("ghost introgression"). Cycads are an ideal plant for studying the influence of ghost introgression because of their common interspecific gene flow and past high extinction rate. Here, we utilized reference-based ddRADseq to clarify the role of ghost introgression in the evolution of a previously identified sympatric cryptic lineage in Cycas revoluta. After re-evaluating the evolutionary independency of cryptic lineages, the group-wise diverged single-nucleotide polymorphisms among sympatric and allopatric lineages were compared and functionally annotated. Next, we employed an approximate Bayesian computation method for hypothesis testing to clarify the cryptic lineage evolution and ghost introgression effect. SNPs with the genomic signatures of ghost introgression were further annotated. Our results reconfirmed the evolutionary independency of cryptic lineage among C. revoluta and demonstrated that ghost introgression to the noncryptic lineage facilitated their divergence. Gene function related to heat stress and disease resistance implied ecological adaptation of the main extant populations of C. revoluta.