Hypoxia-associated autophagy flux dysregulation in human cancers.

A general feature of cancer is hypoxia, determined as low oxygen levels. Low oxygen levels may cause cells to alter in ways that contribute to tumor growth and resistance to treatment. Hypoxia leads to variations in cancer cell metabolism, angiogenesis and metastasis. Furthermore, a hypoxic tumor microenvironment might induce immunosuppression. Moreover, hypoxia has the potential to impact cellular processes, such as autophagy. Autophagy refers to the catabolic process by which damaged organelles and toxic macromolecules are broken down. The abnormal activation of autophagy has been extensively recorded in human tumors and it serves as a regulator of cell growth, spread to other parts of the body, and resistance to treatment. There is a correlation between hypoxia and autophagy in human malignancies. Hypoxia can regulate the activity of AMPK, mTOR, Beclin-1, and ATGs to govern autophagy in human malignancies. Furthermore, HIF-1α, serving as an indicator of low oxygen levels, controls the process of autophagy. Hypoxia-induced autophagy has a crucial role in regulating the growth, spread, and resistance to treatment in human malignancies. Hypoxia-induced regulation of autophagy can impact other mechanisms of cell death, such as apoptosis. Chemoresistance and radioresistance have become significant challenges in recent years. Hypoxia-mediated autophagy plays a crucial role in determining the response to these therapeutic treatments.

A novel metabolic-related gene signature for predicting clinical prognosis and immune microenvironment in head and neck squamous cell carcinoma.

OBJECTIVES: Metabolic reprogramming is not only an essential hallmark in the progression of head and neck squamous cell carcinoma (HNSCC), but also an important regulator of cancer cell adaptation to tumor microenvironment (TME). However, the potential mechanism of metabolic reprogramming in TME of HNSCC is still unknown. METHODS: The head and neck squamous cell carcinoma with survival information were obtained the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The metabolic-related genes were identified by differential analysis and survival analysis. Univariate and multivariate Cox regression analyses were applied to determine an overall estimate of metabolic-related risk signature and related clinical parameters. The sensitivity and specificity of the risk signature were evaluated by time-dependent receiver operation characteristic (ROC) curves. TME immune cell infiltration mediated by metabolic-related genes was explored by gene set enrichment analysis (GSEA) and correlation analysis. RESULTS: Seven metabolic-related genes (SMS, MTHFD2, HPRT1, DNMT1, PYGL, ADA, and P4HA1) were identified to develop a metabolic-related risk signature. The low-risk group had a better overall survival compared to that of the high-risk group in the TCGA and GSE65858 cohorts. The AUCs for 1-, 3-, and 5-year overall survival were 0.646 vs. 0.673, 0.694 vs. 0.639, and 0.673 vs. 0.573, respectively. The AUC vale of risk score was 0.727 vs. 0.673. The low-risk group was associated with immune cell infiltration in the TME. CONCLUSIONS: The metabolic-related risk signature were constructed and validated, which could involve in regulating the immune cell infiltration in the TME and act as an independent biomarker that predicted the prognosis of HNSCC.

Novel RNA N6-methyladenosine regulator related signature for predicting clinical and immunological characteristics in breast cancer.

BACKGROUND: Epigenetic mechanismshave been reported to involve in shaping tumor immune microenvironment (TME). However, the role of RNA N6-methyladenosine (m6A) modification in breast cancerhas not been fully explored. METHODS: Based on m6A modification and TME infiltration characteristics of 2249 breast cancer patients, we comprehensively correlated m6A modification with immune landscapeby screeningcandidate genes, function analysis and constructing m6Asignatures. Principal component analysis was used to establish the m6Ascore. Both LASSO and Cox regression analyses were used to evaluate its prognostic value.Functional assays and immunohistochemistry were used to evaluate the expression of m6A regulators and immune cell infiltration. RESULTS: Based on the dysregulated expression of m6A, three distinct clusters were identified that displayed diverse types of tumour-associated TME cell infiltration in breast cancer.Gene signatures, stromal activity, and clinical prognosis were assessed by the m6Ascore. m6Ascore could function as a biomarker for predicting the therapeutic response to targeted therapy and immunotherapy.The dysregulated expression of m6Aregulators mediated the immune cell infiltration in the TME. CONCLUSION: Basedonthestudy,weidentified the signature and potential mechanism of m6AmodificationsthatmodifyTME cell infiltration. Thus, targeting m6A regulators may provide a promisingmethodoftreatingBRCA.

Novel cellular senescence-related risk model identified as the prognostic biomarkers for lung squamous cell carcinoma.

Background: Lung cancer is one of the top causes of cancer-related death worldwide. Cellular senescence is a characteristic of cell cycle arrest that plays a role in carcinogenesis and immune microenvironment modulation. Despite this, the clinical and immune cell infiltration features of senescence in lung squamous cell carcinoma (LUSC) are unknown. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to get RNA-seq data and clinical information for LUSC. The least absolute shrinkage and selection operator (LASSO)-Cox regression, receiver operating characteristic (ROC), and Kaplan-Meier analysis were used to evaluate a risk model for predicting overall survival based on six differentially expressed genes. The tumor microenvironment (TME) and immunotherapy response were also studied. Results: To discriminate LUSC into high- and low-risk subgroups, a risk model comprised of six cellular senescence-related genes (CDKN1A, CEBPB, MDH1, SIX1, SNAI1, and SOX5) was developed. The model could stratify patients into high-risk and low-risk groups, according to ROC and Kaplan-Meier analysis. In the TCGA-LUSC and GSE73403 cohorts, the high-risk group had a worse prognosis (P<0.05), and was associated with immune cell inactivation and being insensitive to immunotherapy in IMvigor210. Conclusions: We discovered a new LUSC classification based on six cellular senescence-related genes, which will aid in identifying patients who will benefit from anti-PD-1 treatment. Targeting senescence-related genes appears to be another option for improving clinical therapy for LUSC.

CYP1B1: A Novel Molecular Biomarker Predicts Molecular Subtype, Tumor Microenvironment, and Immune Response in 33 Cancers.

BACKGROUND: Cytochrome P450 Family 1 Subfamily B Member 1 (CYP1B1) is a critical metabolic enzyme of melatonin. Although melatonin has been identified to exhibit tumor suppressing activity, the role and mechanism of the clinical and immunological characteristics of CYP1B1 in cancer remain unclear. METHODS: In this study, RNA expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) across 33 solid tumors. The expression, survival, immune subtype, molecular subtype, tumor mutation burden (TMB), microsatellite instability (MSI), biological pathways, and function in vitro and vivo were evaluated. The predictive value of CYP1B1 in immune cohorts was further explored. RESULTS: We found the dysregulated expression of CYP1B1 was associated with the clinical stage and tumor grade. Immunological correlation analysis showed CYP1B1 was positively correlated with the infiltration of lymphocyte, immunomodulator, chemokine, receptor, and cancer-associated fibroblasts (CAFs) in most cancer. Meanwhile, CYP1B1 was involved in immune subtype and molecular subtype, and was connected with TMB, MSI, neoantigen, the activation of multiple melatonergic and immune-related pathways, and therapeutic resistance. CONCLUSIONS: Together, this study comprehensively revealed the role and mechanism of CYP1B1 and explored the significant association between CYP1B1 expression and immune activity. These findings provide a promising predictor and molecular target for clinical immune treatment.

Identification of necroptosis-related signature and tumor microenvironment infiltration characteristics in lung adenocarcinoma.

OBJECTIVES: Lung cancer remains the most common cancer and the leading cause of cancer deaths. However, the potential roles of necroptosis-related signature and tumor microenvironment (TME) in the lung adenocarcinoma (LUAD) still unknown. MATERIALS AND METHODS: Expression data and clinical information were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. In the TCGA dataset, necroptosis phenotype-related differentially expressed genes (DEGs) were identified. A necroticscore score was developed and validated by integrating GEO-meta datasets. The clinical value of the risk score was further evaluated using Kaplan-Meier and immunotherapeutic cohort (IMvigor210 cohort). RESULTS: Three necroptosis-related patterns and distinct necroptosis-related gene cluster were identified based on the abnormal expression of 14 necroptosis regulators. The necroptosis genomic phenotypes were obtained based on 117 necroptosis phenotype-related DEGs. A necroticscore were constructed to evaluate necroptosis pattern of each patient. Low necroticscore was linked with decreased immune check-point expression, enhanced immune check-point inhibitor response, and better clinical benefits. CONCLUSION: This study suggested that the crucial roles of necroptosis-related regulators in modeling the heterogeneity of TME characteristics. Thus, assessing necroptosis patterns provided us with a deeper understanding of TME and might guide the clinical immunotherapy treatment of lung cancer.

Molecular Characterization Clinical and Immunotherapeutic Characteristics of m5C Regulator NOP2 Across 33 Cancer Types.

Background: Recent studies have identified that RNA 5-methylcytosine (m5C) is a wide-spread epigenetic modification in tumorigenesis. However, the clinical and immunotherapeutic values of m5C regulator NOP2 in 33 cancers remain unclear. Methods: The mRNA expression data and clinical data of 33 cancers were downloaded from The Cancer Genome Atlas (TCGA) database. The immunotherapy data including GSE67501, GSE78220, GSE35640, and IMvigor210 were downloaded from the Gene Expression Omnibus (GEO) database and the website based on the Creative Commons 3.0 license (http://research-pub.Gene.com/imvigor210corebiologies). The expression, survival, clinical parameters, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor microenvironment (TME) were evaluated. Finally, the relationship between NOP2 and immunotherapy response was further explored. Results: NOP2 was significantly upregulated in most cancers, and high NOP2 expression was associated with poor prognosis. TMB, MSI, and NOP2 activities were involved in the dysregulation of NOP2. NOP2 was closely associated with immune cell infiltration, immune modulators, and immunotherapeutic inactivation. Conclusions: We comprehensively explored the clinical and immunotherapeutic values of NOP2 in cancers, providing evidence regarding the function of NOP2 and its role in clinical treatment.

Identification of Clinical and Tumor Microenvironment Characteristics of Hypoxia-Related Risk Signature in Lung Adenocarcinoma.

Background: Lung cancer is the leading cause of cancer-related death globally. Hypoxia can suppress the activation of the tumor microenvironment (TME), which contributes to distant metastasis. However, the role of hypoxia-mediated TME in predicting the diagnosis and prognosis of lung adenocarcinoma (LUAD) patients remains unclear. Methods: Both RNA and clinical data from the LUAD cohort were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Both univariate and multivariate Cox regression analyses were used to further screen prognosis-related hypoxia gene clusters. Time-dependent receiver operation characteristic (ROC) curves were established to evaluate the predictive sensitivity and specificity of the hypoxia-related risk signature. The characterization of gene set enrichment analysis (GSEA) and TME immune cell infiltration were further explored to identify hypoxia-related immune infiltration. Results: Eight hypoxia-related genes (LDHA, DCN, PGK1, PFKP, FBP1, LOX, ENO3, and CXCR4) were identified and established to construct a hypoxia-related risk signature. The high-risk group showed a poor overall survival compared to that of the low-risk group in the TCGA and GSE68465 cohorts (p < 0.0001). The AUCs for 1-, 3-, and 5-year overall survival were 0.736 vs. 0.741, 0.656 vs. 0.737, and 0.628 vs. 0.649, respectively. The high-risk group was associated with immunosuppression in the TME. Conclusion: The hypoxia-related risk signature may represent an independent biomarker that can differentiate the characteristics of TME immune cell infiltration and predict the prognosis of LUAD.

Molecular Characterization of the Clinical and Tumor Immune Microenvironment Signature of 5-methylcytosine-Related Regulators in non-small Cell Lung Cancer.

Background: DNA methylation is an important epigenetic modification, among which 5-methylcytosine methylation (5mC) is generally associated with tumorigenesis. Nonetheless, the potential roles of 5mC regulators in the tumor microenvironment (TME) remain unclear. Methods: The 5mC modification patterns of 1,374 lung adenocarcinoma samples were analyzed systematically. The correlation between the 5mC modification and tumor microenvironment cell infiltration was further assessed. The 5mCscore was developed to evaluate tumor mutation burden, immune check-point inhibitor response, and the clinical prognosis of individual tumors. Results: Three 5mC modification patterns were established based on the clinical characteristics of 21 5mC regulators. According to the differential expression of 5mC regulators, three distinct 5mC gene cluster were also identified, which showed distinct TME immune cell infiltration patterns and clinical prognoses. The 5mCscore was constructed to evaluate the tumor mutation burden, immune check-point inhibitor response, and prognosis characteristics. We found that patients with a low 5mCscore had significant immune cell infiltration and increased clinical benefit. Conclusion: This study indicated that the 5mC modification is involved in regulating TME infiltration remodeling. Targeting 5mC modification regulators might be a novel strategy to treat lung cancer.

Report on the 18th International Symposium on Geo-disaster Reduction and the 4th Gu Dezhen Lecture, 20-22 November 2020, Beijing, China.

The 18th International Symposium on Geo-disaster Reduction (ISGdR) was held on 20-22 November in Beijing, China, focusing on the theme of "Improving the Relationship between Geoenvironment and Society". In this symposium, a high-level Gu Dezhen Lecture and a number of keynote and invited lectures provided a platform for scientists, industrial professionals and students to share their researches and exchange novel ideas on geo-disaster reduction in a hybrid way of offline and online.

SPH-based numerical simulations of flow slides in municipal solid waste landfills.

Most municipal solid waste (MSW) is disposed of in landfills. Over the past few decades, catastrophic flow slides have occurred in MSW landfills around the world, causing substantial economic damage and occasionally resulting in human victims. It is therefore important to predict the run-out, velocity and depth of such slides in order to provide adequate mitigation and protection measures. To overcome the limitations of traditional numerical methods for modelling flow slides, a mesh-free particle method entitled smoothed particle hydrodynamics (SPH) is introduced in this paper. The Navier-Stokes equations were adopted as the governing equations and a Bingham model was adopted to analyse the relationship between material stress rates and particle motion velocity. The accuracy of the model is assessed using a series of verifications, and then flow slides that occurred in landfills located in Sarajevo and Bandung were simulated to extend its applications. The simulated results match the field data well and highlight the capability of the proposed SPH modelling method to simulate such complex phenomena as flow slides in MSW landfills.