Lateral Habenula Neurons Signal Cold Aversion and Participate in Cold Aversion.

The aversion to cold is a fundamental motivated behavior that contributes to the body temperature homeostasis. However, the involvement of the lateral habenula (LHb) as a regulatory hub for negative emotions in this physiological process remains uninvestigated. In this study, we demonstrate an elevation in the population activity of LHb neurons following exposure to cold stimuli. Additionally, we establish the necessity of Vglut2-expressing neurons within the LHb for the encoding of cold aversion behaviors. Furthermore, we have elucidated a neural circuit from excitatory neurons of the dorsomedial hypothalamus (DMH) to LHb that plays a crucial role in this progress. Manipulation of the DMH-LHb circuit has a significant impact on cold aversion behavior in mice. It is worth noting that this circuit does not exhibit any noticeable effects on autonomic thermoregulation or depression-like behavior. The identification of these neural mechanisms involved in behavioral thermoregulation provides a promising avenue for future research.

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors.

Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.

Genome Instability-Associated Long Non-Coding RNAs Reveal Biomarkers for Glioma Immunotherapy and Prognosis.

Genome instability is a hallmark of tumors and is involved in proliferation, invasion, migration, and treatment resistance of many tumors. However, the relationship of genome instability with gliomas remains unclear. Here, we constructed genome instability-derived long non-coding RNA (lncRNA)-based gene signatures (GILncSig) using genome instability-related lncRNAs derived from somatic mutations. Multiple platforms were used to confirm that the GILncSig were closely related to patient prognosis and clinical characteristics. We found that GILncSig, the glioma microenvironment, and glioma cell DNA methylation-based stemness index (mDNAsi) interacted with each other to form a complex regulatory network. In summary, this study confirmed that GILncSig was an independent prognostic indicator for patients, distinguished high-risk and low-risk groups, and affected immune-cell infiltration and tumor-cell stemness indicators (mDNAsi) in the tumor microenvironment, resulting in tumor heterogeneity and immunotherapy resistance. GILncSig are expected to provide new molecular targets for the clinical treatment of patients with gliomas.

Irregular shape as an independent predictor of prognosis in patients with primary intracerebral hemorrhage.

The utility of noncontrast computed tomography markers in the prognosis of spontaneous intracerebral hemorrhage has been studied. This study aimed to investigate the predictive value of the computed tomography (CT) irregularity shape for poor functional outcomes in patients with spontaneous intracerebral hemorrhage. We retrospectively reviewed all 782 patients with intracranial hemorrhage in our stroke emergency center from January 2018 to September 2019. Laboratory examination and CT examination were performed within 24 h of admission. After three months, the patient's functional outcome was assessed using the modified Rankin Scale. Multinomial logistic regression analyses were applied to identify independent predictors of functional outcome in patients with intracerebral hemorrhage. Out of the 627 patients included in this study, those with irregular shapes on CT imaging had a higher proportion of poor outcomes and mortality 90 days after discharge (P < 0.001). Irregular shapes were found to be significant independent predictors of poor outcome and mortality on multiple logistic regression analysis. In addition, the increase in plasma D-dimer was associated with the occurrence of irregular shapes (P = 0.0387). Patients with irregular shapes showed worse functional outcomes after intracerebral hemorrhage. The elevated expression level of plasma D-dimers may be directly related to the formation of irregular shapes.

TMEM2 Combined with IDH and 1p19q in Refining Molecular Subtypes for Predicting Survival of Patients with Glioma.

Gliomas are common intracranial tumors with high morbidity and mortality in adults. Transmembrane protein 2 (TMEM2) is involved in the malignant behavior of solid tumors. TMEM2 regulates cell adhesion and metastasis as well as intercellular communication by degrading nonprotein components of the extracellular matrix. This study aimed to evaluate the relationship between TMEM2 expression levels and glioma subtypes or patient prognosis. Our findings revealed that TMEM2 expression was abnormally upregulated in high-grade glioma. Moreover, combining TMEM2, the status of isocitrate dehydrogenase (IDH) and 1p19q, we subdivided molecular subtypes with significant differences in survival. Patients in the MT-codel-low subgroup had better prognosis than those in the WT-no-codel-high subgroup, who fared the worst. Additionally, correlation analysis of TMEM2 and immune cell infiltration indicated an altered tumor microenvironment (TME) and cell redistribution in the TMEM2 high-expression subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that focal adhesion and PI3K-Akt signaling pathways were enriched in the TMEM2-expressing group. In conclusion, aberrant TMEM2 expression can be used as an independent prognostic marker for refining glioma molecular subtyping and accurate prognosis. These findings will improve rational decision making to provide individualized therapy for patients with glioma.

Long noncoding RNA SNHG12 is a potential diagnostic and prognostic biomarker in various tumors.

BACKGROUND: Tumors are the second most common cause of death in humans worldwide, second only to cardiovascular and cerebrovascular diseases. Although methods and techniques for the treatment of tumors continue to improve, the effect is not satisfactory. These may lack effective therapeutic targets. This study aimed to evaluate the value of SNHG12 as a biomarker in the prognosis and clinical characteristics of various cancer patients. METHODS: We analyzed SNHG12 expression and plotted the survival curves of all cancer samples in the TCGA database using the GEPIA tool. Then, we searched for eligible papers up to April 1, 2019, in databases. Next, the data were extracted from studies examining SNHG12 expression, overall survival and clinicopathological features in patients with malignant tumors. We used Review Manager 5.3 and Stata 15 software to analyze the statistical data. RESULTS: In the TCGA database, abnormally high expression of SNHG12 in tumor samples indicates that the patient has a poor prognosis. Results of meta-analysis is that SNHG12 high expression is related to low overall survival (HR = 2.72, 95% CI = 1.95-3.8, P < 0.00001), high tumor stage (OR = 3.94, 95% CI = 2.80-5.53, P < 0.00001), high grade (OR = 2.04, 95% CI = 1.18-3.51, P = 0.01), distant metastasis (OR = 2.20, 95% CI = 1.40-3.46, P = 0.0006), tumor size (OR = 2.79, 95% CI = 1.89-4.14, P < 0.00001), and lymph node metastasis (OR = 2.66, 95% CI = 1.65-4.29, P < 0.0001). CONCLUSIONS: Our study confirmed that the high expression level of SNHG12 is closely related to the clinicopathological characteristics and prognosis of patients and is a new predictive biomarker for various cancer patients.

The increasing expression of GPX7 related to the malignant clinical features leading to poor prognosis of glioma patients.

BACKGROUND: Glioma is the most common malignant brain tumor in adults. The standard treatment scheme of glioma is surgical resection combined alternative radio- and chemotherapy. However, the outcome of glioma patients was unsatisfied. Here, we aimed to explore the molecular and biological function characteristics of GPX7 in glioma. METHODS: The multidimensional data of glioma samples were downloaded from Chinese Glioma Genome Atlas (CGGA). RT-qPCR method was used to identify the expression status of GPX7. Kaplan-Meier curves and Cox regression analysis were used to explore the prognostic value of GPX7. Gene Set Enrichment Analysis (GSEA) was applied to investigate the GPX7-related functions in glioma. RESULTS: The results indicated that the expression of GPX7 in glioma was higher compared to that in normal brain tissue. Univariate and multivariate Cox regression analyses confirmed that the expression value of GPX7 was an independent prognostic factor in glioma. The GSEA analysis showed that GPX7 was significantly enriched in the cell cycle pathway, ECM pathway, focal adhesion pathway, and toll-like receptor pathway. CONCLUSIONS: The GPX7 was recommended as an independent risk factor for patients diagnosed with glioma for the first time and GPX7 could be potentially used as the therapy target in future. Furthermore, we attempted to explore a potential biomarker for improving the diagnosis and prognosis of patients with glioma.

A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme.

Glioblastoma multiforme (GBM) is the most aggressive primary tumor of the central nervous system. As biomedicine advances, the researcher has found the development of GBM is closely related to immunity. In this study, we evaluated the GBM tumor immunoreactivity and defined the Immune-High (IH) and Immune-Low (IL) immunophenotypes using transcriptome data from 144 tumors profiled by The Cancer Genome Atlas (TCGA) project based on the single-sample gene set enrichment analysis (ssGSEA) of five immune expression signatures (IFN-γ response, macrophages, lymphocyte infiltration, TGF-ß response, and wound healing). Next, we identified six immunophenotype-related long non-coding RNA biomarkers (im-lncRNAs, USP30-AS1, HCP5, PSMB8-AS1, AL133264.2, LINC01684, and LINC01506) by employing a machine learning computational framework combining minimum redundancy maximum relevance algorithm (mRMR) and random forest model. Moreover, the expression level of identified im-lncRNAs was converted into an im-lncScore using the normalized principal component analysis. The im-lncScore showed a promising performance for distinguishing the GBM immunophenotypes with an area under the curve (AUC) of 0.928. Furthermore, the im-lncRNAs were also closely associated with the levels of tumor immune cell infiltration in GBM. In summary, the im-lncRNA signature had important clinical implications for tumor immunophenotyping and guiding immunotherapy in glioblastoma patients in future.

An Immune Gene-Related Five-lncRNA Signature for to Predict Glioma Prognosis.

BACKGROUND: The tumor immune microenvironment is closely related to the malignant progression and treatment resistance of glioma. Long non-coding RNA (lncRNA) plays a regulatory role in this process. We investigated the pathological mechanisms within the glioma microenvironment and potential immunotherapy resistance related to lncRNAs. METHOD: We downloaded datasets derived from glioma patients and analyzed them by hierarchical clustering. Next, we analyzed the immune microenvironment of glioma, related gene expression, and patient survival. Coexpressed lncRNAs were analyzed to generate a model of lncRNAs and immune-related genes. We analyzed the model using survival and Cox regression. Then, univariate, multivariate, receiver operating characteristic (ROC), and principle component analysis (PCA) methods were used to verify the accuracy of the model. Finally, GSEA was used to evaluate which functions and pathways were associated with the differential genes. RESULTS: Normal brain tissue maintains a low-medium immune state, and gliomas are clearly divided into three groups (low to high immunity). The stromal, immune, and estimate scores increased along with immunity, while tumor purity decreased. Further, human leukocyte antigen (HLA), programmed cell death-1 (PDL1), T cell immunoglobulin and mucin domain 3 (TIM-3), B7-H3, and cytotoxic T lymphocyte-associated antigen-4 (CTLA4) expression increases concomitantly with immune state, and the patient prognosis worsens. Five immune gene-related lncRNAs (AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515) were screened to construct risk models. We found that risk scores are related to patient prognosis and clinical characteristics, and are positively correlated with PDL1, TIM-3, and B7-H3 expression. These lncRNAs may regulate the tumor immune microenvironment through cytokine-cytokine receptor interactions, complement, and coagulation cascades, and may promote CD8 + T cell, regulatory T cell, M1 macrophage, and infiltrating neutrophils activity in the high-immunity group. In vitro, the abnormal expression of immune-related lncRNAs and the relationship between risk scores and immune-related indicators (PDL1, CTLA4, CD3, CD8, iNOS) were verified by q-PCR and immunohistochemistry (IHC). CONCLUSION: For the first time, we constructed immune gene-related lncRNA risk models. The risk score may be a new biomarker for tumor immune subtypes and provide molecular targets for glioma immunotherapy.