Poor nutritional status is associated with incomplete functional recovery in elderly patients with mild traumatic brain injury

BackgroundThe geriatric nutritional risk index (GNRI) is a simple index for evaluating the nutrition status of elderly patients. Many investigations have demonstrated that this index is associated with the prognosis of several diseases. This study aims to identify the relationship between the GNRI and recovery in elderly mild traumatic brain injury (mTBI) patients.MethodsA total of 228 mTBI patients older than 65 years were included in this study. mTBI was defined as an injury to the brain with a loss of consciousness of 30 min or less, a duration of posttraumatic amnesia of <24 h, and an admission Glasgow Coma Scale (GCS) score of 13–15. The Glasgow Outcome Scale Extended (GOSE), an outcome scale assessing functional independence, work, social activities, and personal relationships, was applied to assess the recovery of the patients. The clinical outcome was divided into complete recovery (GOSE = 8) and incomplete recovery (GOSE ≤ 7) at 6 months after the injury. Multivariate logistic regression was applied to evaluate the association between the GNRI and recovery of elderly mTBI patients, with adjustment for age, sex, hypertension, diabetes, and other important factors.ResultsThe receiver operating curve (ROC) analysis demonstrated that the cutoff value of GNRI was 97.85, and the area under the curve (AUC) was 0.860. Compared to the patients with a high GNRI, the patients with a low GNRI were older, had a higher prevalence of anemia, acute subdural hematoma, and subarachnoid hemorrhage, had a higher age-adjusted Charlson Comorbidity Index value, and had lower levels of albumin, lymphocytes, and hemoglobin. Multivariable analysis showed that high GNRI was associated with a lower risk of 6-month incomplete recovery (OR, 0.770, 95% CI: 0.709–0.837, p < 0.001).ConclusionThe GNRI has utility as part of the objective risk assessment of incomplete 6-month functional recovery in elderly patients with mTBI

Comprehensive genomic analysis of Oesophageal Squamous Cell Carcinoma reveals clinical relevance

Abstract Oesophageal carcinoma is the fourth leading cause of cancer-related death in China, and more than 90% of these tumours are oesophageal squamous cell carcinoma (ESCC). Although several ESCC genomic sequencing studies have identified mutated somatic genes, the number of samples in each study was relatively small, and the molecular basis of ESCC has not been fully elucidated. Here, we performed an integrated analysis of 490 tumours by combining the genomic data from 7 previous ESCC projects. We identified 18 significantly mutated genes (SMGs). PTEN, DCDC1 and CUL3 were first reported as SMGs in ESCC. Notably, the AJUBA mutations and mutational signature4 were significantly correlated with a poorer survival in patients with ESCC. Hierarchical clustering analysis of the copy number alteration (CNA) of cancer gene census (CGC) genes in ESCC patients revealed three subtypes, and subtype3 exhibited more CNAs and marked for worse prognosis compared with subtype2. Moreover, database annotation suggested that two significantly differential CNA genes (PIK3CA and FBXW7) between subtype3 and subtype2 may serve as therapeutic drug targets. This study has extended our knowledge of the genetic basis of ESCC and shed some light into the clinical relevance, which would help improve the therapy and prognosis of ESCC patients

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Seizure after chronic subdural hematoma evacuation: associated factors and effect on clinical outcome

ObjectiveChronic subdural hematoma (CSDH) is a common disease in neurosurgery, which usually occurs in the elderly. Seizure is one of the postoperative complications in CSDH patients and can affect patient outcomes. There is currently no consensus on whether antiepileptic drugs should be prescribed prophylactically. The aim of this study was to evaluate independent risk factors for postoperative seizures and unfavorable outcomes in CSDH patients.MethodsWe reviewed 1,244 CSDH patients who had undergone burr-hole craniotomy in this study. Patient clinical data, CT scan results, recurrence and outcome data were collected. We divided the patients into two groups based on whether they had a postoperative seizure. Percentages and χ2 tests were applied for categorical variables. Standard deviations and two-sided unpaired t-tests were applied for continuous variables. Stepwise logistic regression analyses were performed to identify the independent factors of postoperative seizures and unfavorable outcomes.ResultsThe incidence of seizures after CSDH surgery was 4.2% in this study. There was no significant difference in recurrence rate between seizure and non-seizure patients (p = 0.948), and the outcome of seizure patients was significantly poor (p &lt; 0.001). There are more postoperative complications in seizure patients (p &lt; 0.001). Logistic regression analysis showed that the independent risk factors for postoperative seizures included drinking history (p = 0.031), cardiac disease (p = 0.037), brain infarction (p = 0.001) and trabecular hematoma (p &lt; 0.001). The use of urokinase is a protective factor for postoperative seizures (p = 0.028). Hypertension is an independent risk factor for unfavorable outcome in seizure patients (p = 0.038).ConclusionSeizures after CSDH surgery were associated with postoperative complications, higher mortality and poorer clinical outcomes at follow-up. We believe that alcohol consumption, cardiac disease, brain infarction and trabecular hematoma are independent risk factors for seizures. The use of urokinase is a protective factor against seizures. Patients with postoperative seizures need more stringent management of their blood pressure. A prospective randomized study is necessary to determine which subgroups of CSDH patients would benefit from antiepileptic drugs prophylaxis

Mutant TP53 G245C and R273H promote cellular malignancy in esophageal squamous cell carcinoma

Abstract Background TP53 gene mutations occur in more than 50% of human cancers and the vast majority of these mutations in human cancers are missense mutations, which broadly occur in DNA binding domain (DBD) (Amino acids 102–292) and mainly reside in six “hotspot” residues. TP53 G245C and R273H point mutations are two of the most frequent mutations in tumors and have been verified in several different cancers. In the previous study of the whole genome sequencing (WGS), we found some mutations of TP53 DBD in esophageal squamous cell carcinoma (ESCC) clinical samples. We focused on two high-frequent mutations TP53 p.G245C and TP53 p.R273H and investigated their oncogenic roles in ESCC cell lines, p53-defective cell lines H1299 and HCT116 p53−/−. Results MTS and colony formation assays showed that mutant TP53 G245C and R273H increased cell vitality and proliferation. Flow cytometry results revealed inhibition of ultraviolet radiation (UV)- and ionizing radiation (IR)- induced apoptosis and disruption of TP53-mediated cell cycle arrest after UV, IR and Nocodazole treatment. Transwell assays indicated that mutant TP53 G245C and R273H enhanced cell migration and invasion abilities. Moreover, western blot revealed that they were able to suppress the expression of TP53 downstream genes in the process of apoptosis and cell cycle arrest induced by UV, which suggests that these two mutations can influence apoptosis and growth arrest might be due, at least in part, to down-regulate the expression of P21, GADD45α and PARP. Conclusions These results indicate that mutant TP53 G245C and R273H can lead to more aggressive phenotypes and enhance cancer cell malignancy, which further uncover TP53 function in carcinogenesis and might be useful in clinical diagnosis and therapy of TP53 mutant cancers

Image_2_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Image_1_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Table_1_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.xlsx

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p