Retroarc Jurassic burial and exhumation of Barrovian metamorphic rocks dated by monazite petrochronology, Funeral Mountains, California

ABSTRACT In this study, we determined the timing of burial and subsequent exhumation of Barrovian metamorphic rocks from the Chloride Cliff area of the Funeral Mountains in southeastern California by constraining the ages of different portions of a pressure-temperature (P-T) path. Using a split-stream laser-ablation inductively coupled plasma–mass spectrometry (ICP-MS) system, we analyzed 192 domains from 35 grains of monazite within five samples with a spot size of 8 µm to determine U-Pb ages and trace-element abundances from the same samples (same polished sections) that were analyzed to produce the P-T paths. Changes that took place within individual monazite grains reflect localized equilibrium and captured the changes in heavy rare earth element (HREE) abundances in the matrix reservoir that occurred as garnet grew, resorbed, and then regrew, thus constraining ages on different portions of the P-T path. The results show that garnet began growing ca. 168 Ma, began resorbing ca. 160 Ma, began retrograde regrowth ca. 157 Ma, and continued to regrow at least through ca. 143 Ma. The early garnet growth corresponds to a period of pressure increase along the P-T path. The subsequent partial resorption corresponds to the prograde crossing of a garnet-consuming reaction during decompression, and the retrograde garnet regrowth occurred when this same reaction was recrossed in the retrograde sense during further decompression. These results are consistent with previously determined ages, which include a Lu-Hf garnet age of 167.3 ± 0.72 Ma for the early pressure-increase portion of the P-T path, and 40Ar/39Ar muscovite cooling ages of 153 and 146 Ma in the lower-grade Indian Pass area 10 km southeast of Chloride Cliff. The 40Ar/39Ar muscovite ages document cooling at the same time as retrograde garnet regrowth was taking place at Chloride Cliff. The oldest monazite age obtained in this study, 176 ± 5 Ma, suggests that southeast-directed thrusting within the Jurassic retroarc was ongoing by this time along the California portion of the western North American plate margin, as a consequence of east-dipping subduction and/or arc collision. The Funeral Mountains were likely located on the east side of the northern Sierra Nevada range in the Jurassic, taking into account dextral strike-slip displacement along the Cretaceous Mojave–Snow Lake fault. The Late Jurassic timing of burial in the Funeral Mountains and its Jurassic location suggest burial was associated with the East Sierran thrust system. The timing of prograde garnet resorption during exhumation (160–157 Ma) corresponds to a change from regional dextral transpression to sinistral transtension along the Jurassic plate margin inferred to have occurred ca. 157 Ma. The recorded exhumation was concurrent with intrusion of the 148 Ma Independence dike swarm in the eastern Sierra Nevada and Mojave regions, which developed within a regime of northeast-southwest extension.

Prognostic Significance and Gene Co-Expression Network of PLAU and PLAUR in Gliomas

The urokinase-type plasminogen activator(PLAU) and its receptor PLAUR participate in a series of cell physiological activities on the extracellular surface. Abnormal expression of PLAU and PLAUR is associated with tumorigenesis. This study aims to evaluate the prognostic value of PLAU/PLAUR transcription expression in glioma and to explore how they affect the generation and progression of glioma. In this study, online databases are applied, such as Oncomine, GEPIA, CGGA, cBioPortal, and LinkedOmics. Overexpression of PLAU/PLAUR was found to be significantly associated with clinical variables including age, tumor type, WHO grade, histology, IDH-1 mutation, and 1p19q status. PLAU and PLAUR had a high correlation in transcriptional expression levels. High expression of PLAU and PLAUR predicted a poor prognosis in primary glioma and recurrent glioma patients, especially in lower grade gliomas. Cox regression analysis indicated that high expression of PLAU and PLAUR were independent prognostic factors for shorter overall survival in glioma patients. In gene co-expression network analysis PLAU and PLAUR and their co-expression genes were found to be involved in inflammatory activities and tumor-related signaling pathways. In conclusion, PLAU and PLAUR could be promising prognostic biomarkers and potential therapeutic targets of glioma patients.

Pleomorphic xanthoastrocytoma is a heterogeneous entity with pTERT mutations prognosticating shorter survival

Pleomorphic xanthoastrocytoma (PXA) in its classic manifestation exhibits distinct morphological features and is assigned to CNS WHO grade 2 or grade 3. Distinction from glioblastoma variants and lower grade glial and glioneuronal tumors is a common diagnostic challenge. We compared a morphologically defined set of PXA (histPXA) with an independent set, defined by DNA methylation analysis (mcPXA). HistPXA encompassed 144 tumors all subjected to DNA methylation array analysis. Sixty-two histPXA matched to the methylation class mcPXA. These were combined with the cases that showed the mcPXA signature but had received a histopathological diagnosis other than PXA. This cohort constituted a set of 220 mcPXA. Molecular and clinical parameters were analyzed in these groups. Morphological parameters were analyzed in a subset of tumors with FFPE tissue available. HistPXA revealed considerable heterogeneity in regard to methylation classes, with methylation classes glioblastoma and ganglioglioma being the most frequent mismatches. Similarly, the mcPXA cohort contained tumors of diverse histological diagnoses, with glioblastoma constituting the most frequent mismatch. Subsequent analyses demonstrated the presence of canonical pTERT mutations to be associated with unfavorable prognosis among mcPXA. Based on these data, we consider the tumor type PXA to be histologically more varied than previously assumed. Histological approach to diagnosis will predominantly identify cases with the established archetypical morphology. DNA methylation analysis includes additional tumors in the tumor class PXA that share similar DNA methylation profile but lack the typical morphology of a PXA. DNA methylation analysis also assist in separating other tumor types with morphologic overlap to PXA. Our data suggest the presence of canonical pTERT mutations as a robust indicator for poor prognosis in methylation class PXA.

WHO Grade Loses Its Prognostic Value in Molecularly Defined Diffuse Lower-Grade Gliomas

BackgroundWhile molecular insights to diffuse lower-grade glioma (dLGG) have improved the basis for prognostication, most established clinical prognostic factors come from the pre-molecular era. For instance, WHO grade as a predictor for survival in dLGG with isocitrate dehydrogenase (IDH) mutation has recently been questioned. We studied the prognostic role of WHO grade in molecularly defined subgroups and evaluated earlier used prognostic factors in the current molecular setting.Material and MethodsA total of 253 adults with morphological dLGG, consecutively included between 2007 and 2018, were assessed. IDH mutations, codeletion of chromosomal arms 1p/19q, and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletions were analyzed.ResultsThere was no survival benefit for patients with WHO grade 2 over grade 3 IDH-mut dLGG after exclusion of tumors with known CDKN2A/B homozygous deletion (n=157) (log-rank p=0.97). This was true also after stratification for oncological postoperative treatment and when astrocytomas and oligodendrogliomas were analyzed separately. In IDH-mut astrocytomas, residual tumor volume after surgery was an independent prognostic factor for survival (HR 1.02; 95% CI 1.01–1.03; p=0.003), but not in oligodendrogliomas (HR 1.02; 95% CI 1.00–1.03; p=0.15). Preoperative tumor size was an independent predictor in both astrocytomas (HR 1.03; 95% CI 1.00–1.05; p=0.02) and oligodendrogliomas (HR 1.05; 95% CI 1.01–1.09; p=0.01). Age was not a significant prognostic factor in multivariable analyses (astrocytomas p=0.64, oligodendrogliomas p=0.08).ConclusionOur findings suggest that WHO grade is not a robust prognostic factor in molecularly well-defined dLGG. Preoperative tumor size remained a prognostic factor in both IDH-mut astrocytomas and oligodendrogliomas in our cohort, whereas residual tumor volume predicted prognosis in IDH-mut astrocytomas only. The age cutoffs for determining high risk in patients with IDH-mut dLGG from the pre-molecular era are not supported by our results.

Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis

Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

SERPINH1 is a Potential Prognostic Biomarker and Correlated With Immune Infiltration: A Pan-Cancer Analysis

Background: Serpin peptidase inhibitor clade H, member 1 (SERPINH1) is a gene encoding a member of the serpin superfamily of serine proteinase inhibitors. The upregulated of SERPINH1 was associated with poor prognosis in breast cancer, stomach adenocarcinoma, and esophageal carcinoma. However, the role of SERPINH1 in pan-cancer is largely unexplored.Methods: SERPINH1 expression and the correlation with prognosis in human pan-cancer were analyzed by the Cancer Genome Atlas and the Genotype-Tissue Expression dataset. Pearson correlation analysis was applied to evaluate the role of SERPINH1 expression in tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methyltransferase, and common immunoregulators. Spearman’s correlation test was used to analysis SERPINH1 expression in tumor immune infiltration and infiltrating immune cells via the Tumor Immune Evaluation Resource database. Furtherly, immunohistochemistry staining of SERPINH1 was acquired from the Human Protein Atlas database for validation.Results: SERPINH1 was abnormally expressed in fourteen cancers. The high expression of SERPINH1 significantly reduced the overall survival (OS), disease-specific survival, and progression free interval in eleven cancers. Moreover, SERPINH1 expression was correlated with MMR, MSI, TMB, and DNA methylation in multiple types of cancer. Also, SERPINH1 expression showed strong association with immunoregulators and immune checkpoint markers in testicular germ cell tumors, brain lower grade glioma (LGG), pheochromocytoma and paraganglioma. In addition, SERPINH1 expression was related to immune cell infiltration in multiple cancers, particularly in breast invasive carcinoma, LGG, and liver hepatocellular carcinoma. The result of immunohistochemistry verification shown that SERPINH1 staining was higher in tumor samples than in normal tissue in colon adenocarcinoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma and cervical squamous cell carcinoma, which was consistent with the result of OS.Conclusion: Overall, these results indicate that SERPINH1 may serve as an important prognostic biomarker and correlate with tumor immunity in human pan-cancer.

Development and validation of a prognostic nomogram for lower-grade glioma based on an autophagy-related lncRNA signature

IntroductionGliomas account for 75% of the primary malignant brain tumors. The prognosis and treatment planning vary in lower-grade gliomas (LGG) due to their heterogeneous clinical behaviors. The dysregulation of autophagy-related (ATG) lncRNAs plays a crucial role in LGG. We aimed to develop and validate an ATG lncRNA risk signature, and a survival nomogram with integration of novel prognostic for LGG patients.Material and methodsDifferentially expressed ATG lncRNAs were screened out based on TCGA and GTEx RNA-seq databases. ATG lncRNA prognostic signature was then established by Kaplan–Meier, univariate Cox proportional hazards regression, Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. Kaplan–Meier, univariate Cox regression and multivariate Cox proportional hazards regression were used to screen out clinical and molecular variables. A nomogram was developed and internally validated by ROC and calibration plots.ResultsAn ATG lncRNA risk signature was constructed with six differentially expressed lncRNAs (LINC00599, LINC02609, AC021739.2, AL118505.1, AL354892.2, and AL590666.2). Based on the risk signature, a nomogram was developed by addition of the significant prognostic clinical variables (age and grade) and molecular variables (IDH status and MGMT status).ConclusionsWe identified an ATG lncRNA risk signature and develop a nomogram for individualized survival prediction in LGG patients. A user-friendly free online calculator to facilitate the use of this nomogram among clinicians is also provided: https://linstu2009.shinyapps.io/LGGPRODICTORapp/?_ga=2.3154800.1506830296.1588641469-159983587.1588641469.

The Impact of Sickle Cell Disease on Academic Performance among Affected Students

Background: Sickle cell disease (SCD) is a genetic disease that is highly prevalent in Jazan Province, Saudi Arabia, and is mostly characterized by many complications such as vaso-occlusive crises (VOC), acute chest syndrome (ACS) and well-documented neurological complications. These complications may affect patients’ academic performance. Methods: An observational, cross-sectional, retrospective study was conducted in Jazan Province. General and demographic data were collected and questions about academic performance of students with SCD were answered. Both t-tests and chi-square tests, along with multiple logistic regression, were used for analysis. Results: 982 participants were selected for this study with a mean age of 23 years (SD: 7). Most of the participants were female (64%). The number of participants with SCD was 339 (36%), of whom 42% were male. Students with SCD recorded lower grade point averages (GPA) and more absences compared to healthy participants. Further, about 60% of students with SCD thought they performed better than 40% of the participants without SCD during the COVID-19 pandemic when most of the educational activities were online. Conclusion: As has been previously reported, this study suggested that the academic performance of students with SCD is negatively affected compared to healthy individuals, and this is mostly due to complications associated with the disease. Further, students with SCD acknowledged better performance with online education, an option that should be considered to improve their academic performance. National studies on a larger population are required by health and education officials, and supportive online educational programs are warranted to enhance the academic performance of this population.