Air pollution and skin diseases: A comprehensive evaluation of the associated mechanism.

Air pollutants deteriorate the survival environment and endanger human health around the world. A large number of studies have confirmed that air pollution jeopardizes multiple organs, such as the cardiovascular, respiratory, and central nervous systems. Skin is the largest organ and the first barrier that protects us from the outside world. Air pollutants such as particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs) will affect the structure and function of the skin and bring about the development of inflammatory skin diseases (atopic dermatitis (AD), psoriasis), skin accessory diseases (acne, alopecia), auto-immune skin diseases (cutaneous lupus erythematosus(CLE) scleroderma), and even skin tumors (melanoma, basal cell carcinoma (BCC), squamous-cell carcinoma (SCC)). Oxidative stress, skin barrier damage, microbiome dysbiosis, and skin inflammation are the pathogenesis of air pollution stimulation. In this review, we summarize the current evidence on the effects of air pollution on skin diseases and possible mechanisms to provide strategies for future research.

F. Nucleatum enhances oral squamous cell carcinoma proliferation via E-cadherin/ß-Catenin pathway.

BACKGROUND: Fusobacterium nucleatum (F. nucleatum) is a microbial risk factor whose presence increases the risk of oral squamous cell carcinoma (OSCC) progression. However, whether it can promote the proliferation of OSCC cells remains unknown. METHODS: In this study, we investigated F. nucleatum effect on OSCC cell proliferation using in vitro and in vivo experiments. RESULTS: Our results showed that F. nucleatum promoted OSCC cell proliferation, doubling the cell count after 72 h (CCK-8 assay). Cell cycle analysis revealed G2/M phase arrest. F. nucleatum interaction with CDH1 triggered phosphorylation, upregulating downstream protein ß-catenin and activating cyclinD1 and Myc. Notably, F. nucleatum did not affect noncancerous cells, unrelated to CDH1 expression levels in CAL27 cells. Overexpression of phosphorylated CDH1 in 293T cells did not upregulate ß-catenin and cycle-related genes. In vivo BALB/c nude experiments showed increased tumor volume and Ki-67 proliferation index after F. nucleatum intervention. CONCLUSION: Our study suggests that F. nucleatum promotes OSCC cell proliferation through the CDH1/ß-catenin pathway, advancing our understanding of its role in OSCC progression and highlighting its potential as a therapeutic target.

Disulfidptosis in head and neck squamous carcinoma: Integrative bioinformatic and in-vitro analysis.

BACKGROUND: Head and neck squamous carcinoma (HNSC) is a prevalent global malignancy with limited treatment options, which necessitates the development of novel therapeutic strategies. Disulfidptosis, a recently discovered and unique cell death pathway, may offer promise as a treatment target in HNSC. MATERIALS AND METHODS: We identified disulfidptosis-related genes (DRGs) using multiple algorithms and developed a prognostic model based on a disulfidptosis-related gene index (DRGI). The model's predictive accuracy was assessed by ROC-AUC, and patients were stratified by risk scores. We investigated the tumor immune microenvironment, immune responses, tumorigenesis pathways, and chemotherapy sensitivity (IC50). We also constructed a diagnostic model using 20 machine-learning algorithms and validated PCBP2 expression through RT-qPCR and western blot. RESULTS: We developed a 12-DRG DRGI prognostic model, classifying patients into high- and low-risk groups, with the high-risk group experiencing poorer clinical outcomes. Notable differences in tumor immune microenvironment and chemosensitivity were observed, with reduced immune activity and suboptimal treatment responses in the high-risk group. Advanced machine learning and in-vitro experiments supported DRGI's potential as a reliable HNSC diagnostic biomarker. CONCLUSION: We established a novel DRGI-based prognostic and diagnostic model for HNSC, exploring its tumor immune microenvironment implications, and offering valuable insights for future research and clinical trials.

Inflammatory cytokines mediating the effect of oral lichen planus on oral cavity cancer risk: a univariable and multivariable mendelian randomization study.

BACKGROUND: While observational studies and experimental data suggest a link between oral lichen planus (OLP) and oral cavity cancer (OCC), the causal relationship and the role of inflammatory cytokines remain unclear. METHODS: This study employed a univariable and multivariable Mendelian Randomization (MR) analysis to investigate the causal relationship between OLP and the risk of OCC. Additionally, the potential role of inflammatory cytokines in modulating this association was explored. Instrumental variables were derived from genetic variants associated with OLP (n = 377,277) identified in Finngen R9 datasets, with 41 inflammatory cytokines as potential mediators, and OCC (n = 4,151) as the outcome variable. Analytical methods including Inverse Variance Weighted (IVW), Weighted Median, MR-Egger, and MR-PRESSO were utilized to assess the causal links among OLP, inflammatory cytokines, and OCC risk. Multivariable MR (MVMR) was then applied to quantify the mediating effects of these cytokines in the relationship between OLP and increased OCC risk. RESULTS: MR analysis provided strong evidence of a causal relationship between OLP (OR = 1.417, 95% CI = 1.167-1.721, p < 0.001) and the risk of OCC. Furthermore, two inflammatory cytokines significantly influenced by OLP, IL-13 (OR = 1.088, 95% CI: 1.007-1.175, P = 0.032) and IL-9 (OR = 1.085, 95% CI: 1.005-1.171, P = 0.037), were identified. Subsequent analysis revealed a significant causal association only between IL-13 (OR = 1.408, 95% CI: 1.147-1.727, P = 0.001) and higher OCC risk, establishing it as a potential mediator. Further, MVMR analysis indicated that IL-13 (OR = 1.437, 95% CI = 1.139-1.815, P = 0.002) mediated the relationship between OLP and OCC, accounting for 8.13% of the mediation. CONCLUSION: This study not only elucidates the potential causal relationship between OLP and the risk of OCC but also highlights the pivotal mediating role of IL-13 in this association.

Characterizing hedgehog pathway features in senescence associated osteoarthritis through Integrative multi-omics and machine learning analysis.

Purpose: Osteoarthritis (OA) is a disease of senescence and inflammation. Hedgehog's role in OA mechanisms is unclear. This study combines Bulk RNA-seq and scRNA-seq to identify Hedgehog-associated genes in OA, investigating their impact on the pathogenesis of OA. Materials and methods: Download and merge eight bulk-RNA seq datasets from GEO, also obtain a scRNA-seq dataset for validation and analysis. Analyze Hedgehog pathway activity in OA using bulk-RNA seq datasets. Use ten machine learning algorithms to identify important Hedgehog-associated genes, validate predictive models. Perform GSEA to investigate functional implications of identified Hedgehog-associated genes. Assess immune infiltration in OA using Cibersort and MCP-counter algorithms. Utilize ConsensusClusterPlus package to identify Hedgehog-related subgroups. Conduct WGCNA to identify key modules enriched based on Hedgehog-related subgroups. Characterization of genes by methylation and GWAS analysis. Evaluate Hedgehog pathway activity, expression of hub genes, pseudotime, and cell communication, in OA chondrocytes using scRNA-seq dataset. Validate Hedgehog-associated gene expression levels through Real-time PCR analysis. Results: The activity of the Hedgehog pathway is significantly enhanced in OA. Additionally, nine important Hedgehog-associated genes have been identified, and the predictive models built using these genes demonstrate strong predictive capabilities. GSEA analysis indicates a significant positive correlation between all seven important Hedgehog-associated genes and lysosomes. Consensus clustering reveals the presence of two hedgehog-related subgroups. In Cluster 1, Hedgehog pathway activity is significantly upregulated and associated with inflammatory pathways. WGCNA identifies that genes in the blue module are most significantly correlated with Cluster 1 and Cluster 2, as well as being involved in extracellular matrix and collagen-related pathways. Single-cell analysis confirms the significant upregulation of the Hedgehog pathway in OA, along with expression changes observed in 5 genes during putative temporal progression. Cell communication analysis suggests an association between low-scoring chondrocytes and macrophages. Conclusion: The Hedgehog pathway is significantly activated in OA and is associated with the extracellular matrix and collagen proteins. It plays a role in regulating immune cells and immune responses.

A Mendelian Randomisation Analysis Reveals No Relationship Between Periodontitis and Coronary Atherosclerosis.

OBJECTIVES: Growing evidence appears to intimate a profound connection between periodontitis and coronary atherosclerosis (CA), yet the existence of a causal relationship remains unclear. Through the implementation of Mendelian randomization analysis, we further evaluated the potential causal link between chronic/acute periodontitis (CP/AP) and CA. METHODS: Utilizing genome-wide association study (GWAS) summary statistics, we incorporated periodontitis data derived from European samples (n1 = 198,441; n2 = 195,762) and CA data from 61,194 cases. We conducted a 2 sample, bidirectional Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW) method as the main analytical approach. Supplementary analyses were executed through MR Egger, Weighted median (WM), IVW, Simple mode, and Weighted mode approaches. RESULTS: The IVW analysis revealed no significant causal relationship between CA and periodontitis (CA-CP: OR = 2.110, 95% CI = 0.208-21.317, P = .527; CA-AP: OR = 0.414, 95% CI = 0.051-3.384, P = .644). Similarly, the bidirectional analysis did not identify impact of periodontitis on CA (OR = 1.000, 95% CI = 0.999-1.001, P = .953). The supplementary analyses corroborated these findings. CONCLUSIONS: While studies highlighting a correlation between periodontitis and CA, our comprehensive analysis does not corroborate a causal association between periodontitis and CA. Further research is needed to elucidate other potential shared mechanisms and causal evidence between periodontitis and CA.

Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy.

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.

Multi-omics analysis reveals that ferroptosis-related gene CISD2 is a prognostic biomarker of head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy, with high mortality rate and unavailability of accurate therapies. However, its early prevention remains a challenge. In the purview of predictive, preventive, and personalized medicine (PPPM), it is paramount to identify novel and powerful biomarkers. CISD2 is a crucial regulator of iron homeostasis and reactive oxygen species (ROS). Recent studies showed that the NEET protein (NAF-1) encoded by CISD2 is involved in regulating the proliferation and metastasis of tumor cells. Nevertheless, the prognostic value and immunological correlations of CISD2 remain unclear. METHODS: Bioinformatics analyses conducted utilizing data from comprehensive databases The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). All statistical evaluations were executed employing R software. RESULTS: Our investigation of biological function, enrichment pathway, and immune correlation revealed a discernable linkage between CISD2 and the immune response. Moreover, we found that the suppression of CISD2 is associated with immune cell infiltration and various immune signatures. CONCLUSIONS: The present study successfully revealed the potential prognostic and biological function of CISD2 in HNSCC. High expression of CISD2 are linked to gender, race, grade, etc., can notably enhance the early detection, prognosis, and prediction for individuals afflicted with HNSCC.

Halitosis: etiology, prevention, and the role of microbiota.

OBJECTIVES: This study aims to review halitosis research, discuss its various causes, and propose effective interventions based on the underlying etiologies and mechanisms. The main research question is to identify the primary factors contributing to halitosis and appropriate strategies to address them. MATERIALS AND METHODS: A comprehensive literature review was conducted on halitosis and its associated causes, including oral pathological factors, oral microbial influences, microbial metabolic pathways, gastrointestinal diseases, and gut microbiota dysbiosis. RESULTS: Unhealthy eating habits and an imbalance of microorganisms in the oral cavity and gastrointestinal tract were identified as primary causes of halitosis. Dental caries, periodontal disease, xerostomia, and digestive disorders like gastritis and irritable bowel syndrome were also found to be related to the development of halitosis. Due to poor oral hygiene or antibiotic use, disruption of microbial communities can result in dysbiosis, inflammation, and halitosis. CONCLUSIONS: Halitosis is a multifactorial condition with various underlying causes, including oral and systemic diseases. Effective interventions should be tailored based on the specific etiologies and mechanisms involved. CLINICAL RELEVANCE: Understanding the factors contributing to halitosis is crucial for developing appropriate treatment strategies. Enhancing oral hygiene habits, using antimicrobial drugs, or administering probiotics may help regulate oral or intestinal flora, thereby improving halitosis and overall oral health.

GLS as a diagnostic biomarker in breast cancer: in-silico, in-situ, and in-vitro insights.

Background: Recently, a novel programmed cell death mechanism, Cuproptosis, has been discovered and found to play an important role in the development and progression of diverse tumors. In the present study, we comprehensively investigated the core gene of this mechanism, GLS, in breast cancer. Materials and methods: Bulk RNA sequencing data were curated from the TCGA repository to investigate the aberrant expression of GLS over diverse cancer types. Then, we examined its efficacy as a diagnostic biomarker in breast cancer by Area Under Curve (AUC) of the Receiver Operative Characteristic (ROC) curve. Furthermore, by applying siRNA technique, we knocked down the GLS expression level in cancerous cell lines, measuring the corresponding effects on cell proliferation and metastasis. Afterward, we explored the potential implications of GLS expression in the tumor immune microenvironment quantitatively by using several R packages and algorithms, including ESTIMATE, CIBERSORT, etc. Results: Pan-cancer analysis suggested that GLS was aberrantly over-expressed in many cancer types, with breast cancer being typical. More in-depth analyses revealed the expression of GLS exerted a high ROC-AUC value in breast cancer diagnosis. Through the knock-down of GLS expression, it was found that GLS expression was strongly relevant to the growth and metastasis of tumor. Furthermore, it was also found to be correlated with the immune tumor microenvironment. Conclusion: We highlighted that GLS expression might be applicable as a diagnostic biomarker in breast cancer and possess significant implications in the growth and metastasis of tumor and the immune tumor microenvironment, sharing new insights into ontological and personalized medicine.

Brief literature review and comprehensive bioinformatics analytics unravel the potential mechanism of curcumin in the treatment of periodontitis.

OBJECTIVE: Periodontitis is a chronic oral disease prevalent worldwide, and natural products are recommended as adjunctive therapy due to their minor side effects. Curcumin, a widely used ancient compound, has been reported to possess therapeutic effects in periodontitis. However, the exact mechanism underlying its activity remains unclear. In this context, the present study aimed to conduct computational simulations to uncover the potential mechanism of action of Curcumin in the treatment of periodontitis. MATERIALS AND METHODS: Single-cell analysis was conducted using a dataset (i.e., GSE164241) curated from the Gene Expression Omnibus (GEO) database through an R package "Seurat package." Bulk RNA sequencing data were curated from GSE10334 and GSE16134 and processed by R package "Limma." Then, the marker genes in the single-cell transcriptome and differentially expressed genes (DEGs) in the bulk transcriptome were integrated. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were also carried out to reveal their functionalities. Key targets were mined from their protein-protein interaction (PPI) network topologically. Afterward, molecular docking was performed. The top-ranked pose was subjected to molecular dynamics simulations to investigate the stability of the docking result. RESULTS: FOS, CXCL1, CXCL8, and IL1B, were filtered after a series of selected processes. The results of molecular modeling suggested that except for IL1B, the Vena Scores of the rest exceeded -5 kcal/mol. Furthermore, the molecular dynamic simulation indicated that the binding of the CXCL8-Curcumin complex was stable over the entire 100 ns simulation. CONCLUSION: The present study unlocked the binding modes of CXCL1, FOS, and CXCL8 with the Curcumin molecule, which were relatively stable, especially for CXCL8, hindering its promising potential to serve as the critical targets of Curcumin in periodontitis treatment.

Oral microbiota may affect osteoradionecrosis following radiotherapy for head and neck cancer.

BACKGROUND: Osteoradionecrosis (ORN) is a serious complication of radiotherapy for head and neck cancer (HNC). However, its etiology and pathogenesis have not been completely elucidated. Recent studies suggest the involvement of the oral microbiota in the development of ORN. The aim of this study was to assess the correlation between oral microbiota and the extent of bone resorption in ORN patients. MATERIALS AND METHODS: Thirty patients who received high-dose radiotherapy for HNC were enrolled. Tissue specimens were collected from the unaffected and affected sides. The diversity, species differences and marker species of the oral microbial community were determined by 16 S rRNA sequencing and bioinformatics analysis. RESULTS: The ORN group had greater microbial abundance and species diversity. The relative abundance of f_Prevotellaceaeand, f_Fusobacteriaceae, f_Porphyromonadaceae, f_Actinomycetaceae, f_Staphylococcaceae, g_Prevotella, g_Staphylococcus, s_Endodontalis and s_Intermedia were particular;y increased in ORN, suggesting a potential association between the oral microbiota and ORN. Furthermore, g_Prevotella, g_Streptococcus, s_parvula and s_mucilaginosa were identified as potential diagnostic and prognostic biomarkers of ORN. Association network analysis also suggested an overall imbalance in species diversity and ecological diversity in the oral microbiota of ORN patients. In addition, pathway analysis indicated that the dominant microbiota in ORN may disrupt bone regeneration by regulating specific metabolic pathways that increase osteoclastic activity. CONCLUSION: Radiation-induced ORN is associated with significant changes in the oral microbiota, and the latter may play a potential role in the etiopathology of post-radiation ORN. The exact mechanisms through which the oral microbiota influence osteogenesis and osteoclastogenesis remain to be elucidated.

A decade of progress: bibliometric analysis of trends and hotspots in oral microbiome research (2013-2022).

Background: Over the past decade, a plethora of studies have delved into the oral microbiome. Our objective was to evaluate the trends in oral microbiome research employing a quantitative approach. Materials and methods: We extracted clinical studies on the oral microbiome published between 2013 and 2022 from the Web of Science database, yielding 3024 articles. The assembled literature was visually scrutinized using VOSviewer 1.6.18, Citespace 6.1.6, Pajek, Scimago Graphica, and other specialized software to assess authors, institutions, countries, journals, co-cited literature, keywords, genes, and diseases. Results: Our analysis identified a total of 3024 articles. The volume and rate of annual publications steadily increased, with research interest in the oral microbiome progressively intensifying. The United States, China, and the UK contributed the highest number of publications. Growth rates of publications varied among countries over time. The Forsyth Institute emerged as the most collaborative institution, boasting the highest number of relevant papers (135) and securing the top rank, followed by Sichuan University and Harvard University. Paster Bruce J, Zhou Xuedong, and He Xuesong were pioneers in the field of oral microbiome research. This analysis demonstrates that the homeostatic balance of the oral microbiome, advanced microbial sequencing technology, connections with gut microbiota, and tumorigenesis, including oral cancer, have become emerging topics in the oral microbiome field. Conclusions: This study delineated a comprehensive landscape of hotspots and frontiers in oral microbiome research, thus facilitating the identification of interdisciplinary advancements. We sincerely hope that our bibliometric analysis will enable researchers to leverage the oral microbiome to ultimately improve human oral health.

Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.

Background: Gastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy. Methods: To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy. Results: Eight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features. Conclusion: In summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction.

Background: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication. Materials and methods: The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue. Results: Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified. Conclusion: For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.Graphical abstract.

Lactate in the tumor microenvironment: A rising star for targeted tumor therapy.

Metabolic reprogramming is one of fourteen hallmarks of tumor cells, among which aerobic glycolysis, often known as the "Warburg effect," is essential to the fast proliferation and aggressive metastasis of tumor cells. Lactate, on the other hand, as a ubiquitous molecule in the tumor microenvironment (TME), is generated primarily by tumor cells undergoing glycolysis. To prevent intracellular acidification, malignant cells often remove lactate along with H+, yet the acidification of TME is inevitable. Not only does the highly concentrated lactate within the TME serve as a substrate to supply energy to the malignant cells, but it also works as a signal to activate multiple pathways that enhance tumor metastasis and invasion, intratumoral angiogenesis, as well as immune escape. In this review, we aim to discuss the latest findings on lactate metabolism in tumor cells, particularly the capacity of extracellular lactate to influence cells in the tumor microenvironment. In addition, we examine current treatment techniques employing existing medications that target and interfere with lactate generation and transport in cancer therapy. New research shows that targeting lactate metabolism, lactate-regulated cells, and lactate action pathways are viable cancer therapy strategies.

NCAPG2 could be an immunological and prognostic biomarker: From pan-cancer analysis to pancreatic cancer validation.

More recently, NCAPG2 has emerged as an intrinsically essential participant of the condensin II complex involved in the process of chromosome cohesion and stabilization in mitosis, and its position in particular tumours is now being highlighted. Simultaneously, the genetic properties of NCAPG2 hint that it might have enormous potential to interpret the malignant progression of tumors in a broader perspective, that is, in pan-cancer. Yet, at present, this recognition remains merely superficial and there is a lack of more detailed studies to explore the underlying pathogenesis. To meet this need, the current study was undertaken to comprehensively elucidate the potential functions of NCAPG2 in pan-cancer, based on a combination of existing databases like TCGA and GTEx. NCAPG2 was identified to be overexpressed in almost every tumor and to exhibit significant prognostic and diagnostic efficacy. Furthermore, the correlation between NCAPG2 and selected immune features, namely immune cell infiltration, immune checkpoint genes, TMB, MSI, etc. also indicates that NCAPG2 could potentially be applied in guidance of immunotherapy. Subsequently, in pancreatic cancer, this study further clarified the utility of NCAPG2 that downregulation of its expression could result in reduced proliferation, invasion and metastasis of pancreatic cancer cells, among such phenotypical changes, the epithelial-mesenchymal transition disruption could be at least one of the possible mechanisms raising or enhancing tumorigenesis. Taken above, NCAPG2, as a member of pan-oncogenes, would serve as a biomarker and potential therapeutic target for a range of malignancies, sharing new insights into precision medicine.

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone.

Relationship exploration between the street-greenery rate (SGR) of different street types and land surface temperature (LST) is of great significance for realizing regional sustainable development goals. Given the lack of consideration of the local climate zone concept (LCZ), Chongqing's Inner Ring region was selected as a case to assess the relationship between SGR and LST. Firstly, the LST was retrieved based on Landsat 8 imagery, which was calibrated by the atmospheric correction method; next, the street-greenery rates of different streets were calculated based on the semantic segmentation method; finally, street types were classified in detail by introducing LCZ, and the relationship between SGR and LST was investigated. The results showed that: (1) The LST spatial distribution pattern was closely related to human activity, with the high-temperature zones mainly concentrated in the core commercial areas, dense residential areas, and industrial cluster areas; (2) The average SGR values of expressways, main trunk roads, secondary trunk roads, and branch roads were 21.70%, 22.40%, 24.60%, and 26.70%, respectively. The level of SGR will decrease when the street width increases; (3) There is a negative correlation between the SGR and the LST in most streets. Among them, the LST of secondary trunk roads in low-rise and low-density built-up areas with a south-north orientation had a strong negative correlation with the SGR. Moreover, the wider the street, the higher the cooling efficiency of plants. Specifically, the LST of streets in low-rise and low-density built-up areas with south-north orientation may decrease by 1°C when the street-greenery rate is increased by 3.57%.

Network analysis reveals miRNA crosstalk between periodontitis and oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of the malignant tumors with a poor prognosis. Periodontitis (PD is considered a high-risk factor for OSCC, but the genetic mechanism is rarely studied. This study aims to link OSCC and PD by identifying common differentially expressed miRNAs (Co-DEmiRNAs), their related genes (Hub genes), transcription factors (TFs), signaling pathways, enrichment functions, and compounds, and searching for genetic commonalities. METHODS: The miRNAs expression datasets of OSCC and PD were searched from the GEO database. The miRNA and related crosstalk mechanism between OSCC and PD was obtained through a series of analyses. RESULTS: hsa-mir-497, hsa-mir-224, hsa-mir-210, hsa-mir-29c, hsa-mir-486-5p, and hsa-mir-31are the top miRNA nodes in Co-DEmiRNA-Target networks. The most significant candidate miRNA dysregulation genes are ZNF460, FBN1, CDK6, BTG2, and CBX6, while the most important dysregulation TF includes HIF1A, TP53, E2F1, MYCN, and JUN. 5-fluorouracil, Ginsenoside, Rh2, and Formaldehyde are the most correlated compounds. Enrichment analysis revealed cancer-related pathways and so on. CONCLUSIONS: The comprehensive analysis reveals the interacting genetic and molecular mechanism between OSCC and PD, linking both and providing a foundation for future basic and clinical research.