Identification and verification of the ferroptosis- and pyroptosis-associated prognostic signature for low-grade glioma

Accumulating evidence reveals that ferroptosis and pyroptosis play pivotal roles in tumorigenesis of low-grade glioma (LGG). In this research, we aimed to classify molecular subtypes and further identify and verify a novel multigene signature in LGG on the basis of ferroptosis and pyroptosis-related genes (FPRGs). Raw sequencing data and corresponding clinical data of LGG samples retrieved from the TCGA and CGGA databases were obtained for the training and validation datasets. Non-negative matrix factorization (NMF) clustering defined by FPRGs associated with prognosis was performed to classify molecular subtypes of LGG patients. LASSO-SVM-Random Forest analysis was carried out to develop an FPRG signature to predict the survival and benefit of immunotherapy of LGG patients. NMF clustering defined by FPRGs with prognostic values acted to categorize LGG patients into two molecular subtypes with different prognosis, clinical traits and immune microenvironments. A six-FPRG prognostic signature was constructed, accompanied by the optimal p-value. The AUC values of our signature exhibited great prognostic performances. Our signature was superior to other four well-recognized signatures in predicting the survival probability of LGG patients. Immune characteristics, tumor mutation profile, tumor stemness indices, MGMT methylation and immunotherapy response biomarkers showed significant differences between high- and low-risk populations. Finally, a nomogram was created for quantitative prediction of the survival probability of LGG patients, with the AUC values of the nomogram being 0.916, 0.888 and 0.836 for 1-, 3- and 5-year survival, sequentially. Overall, the FPRG signature may function as an effective indicator for the prognosis prediction and immunotherapy response of LGG patients

An English-Chinese Machine Translation and Evaluation Method for Geographical Names

In recent years, with increasing international communication and cooperation, the consensus of toponymic information among different countries has become increasingly important. A large number of English geographical names are in urgent need of translation into Chinese, but there are few studies on machine translation of geographical names at present. Therefore, this paper proposes a method of automatically translating English geographical names into Chinese. First, the lexical structure of the geographic names is analyzed to divide the whole name into two parts, the special name and the general name, in an approach based on the statistical template model that implements pointwise mutual information and a directed acyclic graph data structure on the extracted names from different categories of a geographical name corpus. Second, the two parts of the geographic names are translated. The general name can be directly translated via methods of free translation. For the transliteration of the special name, the phonetic symbols are generated based on the cyclic neural network, and then, the syllables are divided based on the minimum entropy and converted into Chinese characters. Finally, the two parts of Chinese characters are combined, and criteria are prepared to evaluate the translation reliability according to the translation process to realize automatic quality inspection and screening of geographical names. As the experimental results show, the method is effective in the translation process of English geographic names into Chinese. This method can be easily extended to other languages such as Arabic

Unraveling dynamic interactions between tumor-associated macrophages and consensus molecular subtypes in colorectal cancer: An integrative analysis of single-cell and bulk RNA transcriptome

Background: Accumulating research substantiated that tumor-associated macrophages (TAMs) have a significant impact on the tumorigenesis, progression, and distant metastasis, representing a novel target for various cancers. However, the underlying dynamic changes and interactions between TAMs and tumor cells remain largely elusive in colorectal cancer (CRC). Methods: We depicted the dynamic changes of macrophages using sing-cell RNA-seq data and extracted TAM differentiation-related genes. Next, we utilized the weighted gene co-expression network analysis (WGCNA) to acquire CMS-related modular genes using bulk RNA-seq data. Finally, we utilized univariate Cox and Lasso Cox regression analyses to identify TAM differentiation-related biomarkers and established a novel risk signature model. We employed quantitative real-time polymerase chain reaction (qRT-PCR) on CRC tissue samples and used immunohistochemistry (IHC) data frome the HPA database to validate the mRNA and protein expression of prognostic genes. The interaction of TAMs and each consensus molecular subtype (CMS) subpopulation was analyzed at the cellular level. Results: A total of 47,285 cells from single-cell dataset and 1197 CRC patients from bulk dataset were obtained. Among those, 6400 myeloid cells were re-clustered and annotated. RNASE1, F13A1, DAPK1, CLEC10A, RPN2, REG4 and RGS19 were identified as prognostic genes and the risk signature model was established based on the above genes. The qRT-PCR analysis indicated that the expression of RNASE1 and DAPK1 were significantly up-regulated in CRC tumor tissues. The cell-cell communication analysis demonstrated complex interactions between TAMs and CMS malignant cell subpopulations. Conclusion: This study presents an in-depth dissection of the dynamic features of TAMs in the tumor microenvironment and provides promising therapeutic targets for CRC

DataSheet_1_Comprehensive characterisation of immunogenic cell death in melanoma revealing the association with prognosis and tumor immune microenvironment.pdf

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.</p

DataSheet_2_Comprehensive characterisation of immunogenic cell death in melanoma revealing the association with prognosis and tumor immune microenvironment.pdf

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.</p

Relationship of Admission Serum Anion Gap and Prognosis of Critically Ill Patients: A Large Multicenter Cohort Study

Background. There were controversies over the relationship between Anion gap (AG) and mortality in critically ill patients. Therefore, a large multicenter cohort study was conducted to evaluate the association of AG and mortality in large-scale intensive care units (ICUs) patients. Methods. This retrospective cohort study included adult ICU patients enrolled from eICU Collaborative Research Database. According to initial serum AG upon ICU admission, patients were divided into three groups: AG16 mmol/L. Logistic regression models were built to investigate the association between serum AG and ICU and hospital mortalities. Serum AG was added into Acute Physiology and Chronic Health Evaluation (APACHE) IV score and the model discrimination was assessed by the area under the curve (AUC) of receiver operating characteristic curves. The relationship between serum AG and mortalities in patients with different acid-base status and serum lactate were also evaluated. An external validation was performed with the Critical care database comprising patients with infection at Zigong Fourth People’s Hospital. Results. A total of 8520 patients entered the final cohort. There are 42 patients with serum AG16 mmol/L. Serum AG>16 mmol/L is related with increased ICU mortality (odds ratio [OR], 1.530; 95% confidence interval [CI], 1.305–1.794) and hospital mortality (OR, 1.618; 95% CI, 1.415–1.849), compared with 8≤AG≤16 mmol/L. Adding Serum AG to APACHE IV score could statistically improve the prediction of ICU (0.770 [0.761–0.779] to 0.774 [0.765–0.783], P=0.001) and hospital mortalities (0.756 [0.747–0.765] to 0.761 [0.751–0.770], P=0.012). The associations between serum AG and mortalities remain robust in patients with different acid-base statuses and serum lactate. The findings are validated in the external cohort. Conclusions. Initial serum AG>16 mmol/L after ICU admission is associated with increased mortality in critically ill patients

Association of platelet count with mortality in patients with infectious diseases in intensive care unit: a multicenter retrospective cohort study

Platelets play important roles in thrombosis, hemostasis, inflammation, and infection. We aimed to evaluate the association between platelet count and its variation trend and prognosis of patient with infectious diseases in intensive care units (ICUs). This retrospective cohort study extracted 4,251 critically ill adult patients with infectious diseases from the eICU Collaborative Research Database, whose platelet counts were measured daily during the first 7 days after admission. In the survivors, platelet counts decreased in the first days after admission, reached a nadir on day 3, and then returned and continued to rise above the admission value. In non-survivors, the platelet counts decreased after admission, without a subsequent upturn. We defined three subgroups according to the nadir platelet counts within 7 days: ≤50, 50–130, and ≥130 × 109/L, corresponding to high, intermediate, and low ICU mortality. A decreased platelet count was associated with increased ICU mortality (intermediate vs. low: 1.676 [1.285–2.187]; high vs. low: 3.632 [2.611–5.052]). In conclusion, during the first 7 days, platelet counts decreased after ICU admission, while increased subsequently in the survivors but not in the non-survivors. ICU mortality risk increased as nadir platelet count decreased below 130 × 109/L, and further boosted when it reached below 50 × 109/L

MicroRNA-150 Inhibits the Activation of Cardiac Fibroblasts by Regulating c-Myb

Background/Aims: Cardiac fibrosis is the primary cause of deteriorated cardiac function in various cardiovascular diseases. Numerous studies have demonstrated that microRNAs (miRNAs) are critical regulators of myocardial fibrosis. Specifically, many studies have reported that miR-150 is downregulated in cardiovascular diseases, such as acute myocardial infarction (AMI), myocardial hypertrophy and myocardial fibrosis. However, the exact role of miR-150 in these pathological processes remains unknown. Methods: We used the transverse aortic constriction (TAC) mouse model to study the role of miR-150 in cardiac fibrosis induced by pressure overload. After the TAC operation, qRT-PCR was used to measure the expression profiles of miR-150 in left ventricle tissues and populations of primary heart cell types. Then, we used both miR-150 knockout mice and wild type (WT) mice in the TAC model. Changes in cardiac function and pathology were measured using transthoracic echocardiography and pathological analysis, respectively. Furthermore, we predicted the target of miR-150 in cardiac fibroblasts (CFs) and completed in vitro CF transfection experiments using miR-150 analogs and siRNA corresponding to the predicted target. Results: We observed decreased expression levels of miR-150 in hearts suffering pressure overload, and these levels decreased more sharply in CFs than in cardiomyocytes. In addition, the degrees of cardiac function deterioration and cardiac fibrosis in miR-150-/- mice were more severe than were those in WT mice. By transfecting CFs with an miR-150 analog in vitro, we observed that miR-150 inhibited cardiac fibroblast activation. We predicted that the transcription factor c-Myb was the target of miR-150 in CFs. Transfecting CFs with c-Myb siRNA eliminated the effects of an miR-150 inhibitor, which promoted CF activation. Conclusion: These findings reveal that miR-150 acts as a pivotal regulator of pressure overload-induced cardiac fibrosis by regulating c-Myb