Amino acid metabolism-related genes as potential biomarkers and the role of MATN3 in stomach adenocarcinoma: A bioinformatics, mendelian randomization and experimental validation study.

BACKGROUND: Stomach adenocarcinoma (STAD) is a major contributor to cancer-related mortality worldwide. Alterations in amino acid metabolism, which is integral to protein synthesis, have been observed across various tumor types. However, the prognostic significance of amino acid metabolism-related genes in STAD remains underexplored. METHODS: Transcriptomic gene expression and clinical data for STAD patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Amino acid metabolism-related gene sets were sourced from the Gene Set Enrichment Analysis (GSEA) database. A prognostic model was built using LASSO Cox regression based on the TCGA cohort and validated with GEO datasets (GSE84433, GSE84437, GSE84426). Kaplan-Meier analysis compared overall survival (OS) between high- and low-risk groups, and ROC curves assessed model accuracy. A nomogram predicted 1-, 3-, and 5-year survival. Copy number variations (CNVs) in model genes were visualized using data from the Xena platform, and mutation profiles were analyzed with "maftools" to create a waterfall plot. KEGG and GO enrichment analyses were performed to explore biological mechanisms. Immune infiltration and related functions were evaluated via ssGSEA, and Spearman correlation analyzed associations between risk scores and immune components. The TIDE database predicted immunotherapy efficacy, while FDA-approved drug sensitivity was assessed through CellMiner database. The role of MATN3 in STAD was further examined in vitro and in vivo, including amino acid-targeted metabolomic sequencing to assess its impact on metabolism. Finally, Mendelian randomization (MR) analysis evaluated the causal relationship between the model genes and gastric cancer. RESULTS: In this study, we developed a prognostic risk model for STAD based on three amino acid metabolism-related genes (SERPINE1, NRP1, MATN3) using LASSO regression analysis. CNV amplification was common in SERPINE1 and NRP1, while CNV deletion frequently occurred in MATN3. STAD patients were classified into high- and low-risk groups based on the median risk score, with the high-risk group showing worse prognosis. A nomogram incorporating the risk score and clinical factors was created to estimate 1-, 3-, and 5-year survival rates. Distinct mutation profiles were observed between risk groups, with KEGG pathway analysis showing immune-related pathways enriched in the high-risk group. High-risk scores were significantly associated with the C6 (TGF-ß dominant) subtype, while low-risk scores correlated with the C4 (lymphocyte-depleted) subtype. Higher risk scores also indicated increased immune infiltration, enhanced immune functions, lower tumor purity, and poorer immunotherapy response. Model genes were linked to anticancer drug sensitivity. Manipulating MATN3 expression showed that it promoted STAD cell proliferation and migration in vitro and tumor growth in vivo. Metabolomic sequencing revealed that MATN3 knockdown elevated levels of 30 amino acid metabolites, including alpha-aminobutyric acid, glycine, and aspartic acid, while reducing (S)-ß-Aminoisobutyric acid and argininosuccinic acid. MR analysis found a significant causal effect of NRP1 on gastric cancer, but no causal relationship for MATN3 or SERPINE1. CONCLUSION: In conclusion, the amino acid metabolism-related prognostic model shows promise as a valuable biomarker for predicting the clinical prognosis, selecting immunotherapy and drug treatment for STAD patients. Furthermore, our study has shed light on the potential value of the MATN3 as a promising strategy for combating the progression of STAD.

Radiomics-based model for prediction of TGF-ß1 expression in head and neck squamous cell carcinoma.

OBJECTIVE: To explore the connection between TGF-ß1 expression and the survival of patients with head and neck squamous cell carcinoma (HNSCC), as well as whether non-invasive CT-based Radiomics can predict TGF-ß1 expression in HNSCC patients. METHODS: Data on transcriptional profiling and clinical information were acquired from the TCGA database and subsequently categorized based on the TGF-ß1 expression cutoff value. Based on the completeness of enhanced arterial phase CT scans, 139 HNSCC patients were selected. The PyRadiomics package was used to extract radiomic features, and the 3D Slicer software was used for image segmentation. Using the mRMR_RFE and Repeat LASSO algorithms, the optimal features for establishing the corresponding gradient enhancement prediction models were identified. RESULTS: A survival analysis was performed on 483 patients, who were divided into two groups based on the TGF-ß1 expression cut-off. The Kaplan-Meier curve indicated that TGF-ß1 was a significant independent risk factor that reduced patient survival. To construct gradient enhancement prediction models, we used the mRMR_RFE algorithm and the Repeat_LASSO algorithm to obtain two features (glrlm and ngtdm) and three radiation features (glrlm, first order_10percentile, and gldm). In both the training and validation cohorts, the two established models demonstrated strong predictive potential. Furthermore, there was no statistically significant difference in the calibration curve, DCA diagram, or AUC values between the mRMR_RFE_GBM model and the LASSO_GBM model, suggesting that both models fit well. CONCLUSION: Based on these findings, TGF-ß1 was shown to be significantly associated with a poor prognosis and to be a potential risk factor for HNSCC. Furthermore, by employing the mRMR_RFE_GBM and Repeat_LASSO_GBM models, we were able to effectively predict TGF-ß1 expression levels in HNSCC through non-invasive CT-based Radiomics.

Defective autophagy of pericytes enhances radiation-induced senescence promoting radiation brain injury.

BACKGROUND: Radiation-induced brain injury (RBI) represents a major challenge for cancer patients undergoing cranial radiotherapy. However, the molecular mechanisms and therapeutic strategies of RBI remain inconclusive. With the continuous exploration of the mechanisms of RBI, an increasing number of studies have implicated cerebrovascular dysfunction as a key factor in RBI-related cognitive impairment. As pericytes are a component of the neurovascular unit, there is still a lack of understanding in current research about the specific role and function of pericytes in RBI. METHODS: We constructed a mouse model of RBI-associated cognitive dysfunction in vivo and an in vitro radiation-induced pericyte model to explore the effects of senescent pericytes on the blood-brain barrier and normal CNS cells, even glioma cells. To further clarify the effects of pericyte autophagy on senescence, molecular mechanisms were explored at the animal and cellular levels. Finally, we validated the clearance of pericyte senescence by using senolytic drug and all-trans retinoic acid to investigate the role of radiation-induced pericyte senescence. RESULTS: Our findings indicated that radiation-induced pericyte senescence plays a key role in blood-brain barrier dysfunction, leading to RBI and subsequent cognitive decline. Strikingly, pericyte senescence also contributes to the growth and invasion of glioma cells. We further demonstrate that defective autophagy in pericytes is a vital regulatory mechanism for pericyte senescence. Moreover, autophagy activated by rapamycin can reverse pericyte senescence. Notably, the elimination of senescent cells by senolytic drugs significantly mitigated radiation-induced cognitive dysfunction. DISSCUSSION: Our results demonstrated that pericyte senescence may be a promising therapeutic target for RBI and glioma progression.

Evaluating the Attraction of Scenic Spots Based on Tourism Trajectory Entropy.

With the development of positioning technology and the widespread application of mobile positioning terminal devices, the acquisition of trajectory data has become increasingly convenient. Furthermore, mining information related to scenic spots and tourists from trajectory data has also become increasingly convenient. This study used the normalization results of information entropy to evaluate the attraction of scenic spots and the experience index of tourists. Tourists and scenic spots were chosen as the probability variables to calculate information entropy, and the probability values of each variable were calculated according to certain methods. There is a certain competitive relationship between scenic spots of the same type. When the distance between various scenic spots is relatively close (less than 8 km), a strong cooperative relationship can be established. Scenic spots with various levels of attraction can generally be classified as follows: cultural heritage, natural landscape, and leisure and entertainment. Scenic spots with higher attraction are usually those with a higher A-level and convenient transportation. A considerable number of tourists do not choose to visit crowded scenic destinations but choose some spots that they are more interested in according to personal preferences and based on access to free travel.

Cuprotosis clusters predict prognosis and immunotherapy response in low-grade glioma.

Cuprotosis, an emerging mode of cell death, has recently caught the attention of researchers worldwide. However, its impact on low-grade glioma (LGG) patients has not been fully explored. To gain a deeper insight into the relationship between cuprotosis and LGG patients' prognosis, we conducted this study in which LGG patients were divided into two clusters based on the expression of 18 cuprotosis-related genes. We found that LGG patients in cluster A had better prognosis than those in cluster B. The two clusters also differed in terms of immune cell infiltration and biological functions. Moreover, we identified differentially expressed genes (DEGs) between the two clusters and developed a cuprotosis-related prognostic signature through the least absolute shrinkage and selection operator (LASSO) analysis in the TCGA training cohort. This signature divided LGG patients into high- and low-risk groups, with the high-risk group having significantly shorter overall survival (OS) time than the low-risk group. Its predictive reliability for prognosis in LGG patients was confirmed by the TCGA internal validation cohort, CGGA325 cohort and CGGA693 cohort. Additionally, a nomogram was used to predict the 1-, 3-, and 5-year OS rates of each patient. The analysis of immune checkpoints and tumor mutation burden (TMB) has revealed that individuals belonging to high-risk groups have a greater chance of benefiting from immunotherapy. Functional experiments confirmed that interfering with the signature gene TNFRSF11B inhibited LGG cell proliferation and migration. Overall, this study shed light on the importance of cuprotosis in LGG patient prognosis. The cuprotosis-related prognostic signature is a reliable predictor for patient outcomes and immunotherapeutic response and can help to develop new therapies for LGG.

5-hydroxymethylcytosine sequencing in plasma cell-free DNA identifies unique epigenomic features in prostate cancer patients resistant to androgen deprivation therapy.

Background: Currently there are no biomarkers to identify resistance to androgen-deprivation therapy (ADT) in men with hormone-naive prostate cancer. 5-hydroxymethylcytosines (5hmC) in the gene body are associated with gene activation and are critical for epigenomic regulation of cancer progression. Objective: To evaluate whether 5hmC signature in cell-free DNA (cfDNA) predicts early ADT resistance. Design Setting and Participants: Serial plasma samples from 55 prostate cancer patients receiving ADT were collected at three timepoints including baseline (prior to initiating ADT, N=55), 3-month (after initiating ADT, N=55), and disease progression (N=15) within 24 months or 24-month if no progression was detected (N=14). 20 of the 55 patients showed disease progression during the 24-month follow-up. The remaining 35 patients showed no progression in the same follow-up period. Outcome Measurements and Statistical Analysis: cfDNA (5-10ng) was used for selective chemical labeling (hMe-Seal) sequencing to map 5hmC abundance across the genome. Read counts in gene bodies were normalized with DESeq2. Differential methylation and gene set enrichment analyses were performed to identify the 5hmC-enriched genes and biological processes that were associated with disease progression. Kaplan-Meir analysis was utilized to determine the association of 5hmC signatures with progression-free survival. Results and Limitations: 5hmC-sequencing generated an average of 18.6 (range 6.03 to 42.43) million reads per sample with 98% (95-99%) mappable rate. Baseline sample comparisons identified significant 5hmC difference in 1,642 of 23,433 genes between 20 patients with progression and 35 patients without progression (false discovery rate, FDR<0.1). Patients with progression showed significant enrichments in multiple hallmark gene sets with androgen responses as the top enriched gene set (FDR=1.19E-13). Interestingly, this enrichment was driven by a subgroup of patients with disease progression featuring a significant 5hmC hypermethylation of the gene sets involving AR, FOXA1 and GRHL2. To quantify overall activities of these gene sets, we developed a gene set activity score algorithm using a mean value of log2 ratios of gene read counts in an entire gene set. We found that the activity scores in these gene sets were significantly higher in this subgroup of patients with progression than in the remaining patients regardless of the progression status. Furthermore, the high activity scores in these gene sets were associated with poor progression-free survival (p <0.05). Longitudinal analysis showed that activity scores in this subgroup with progression were significantly reduced after 3-month ADT but returned to high levels when the disease was progressed. Conclusions: 5hmC-sequencing in cfDNA identified a subgroup of prostate cancer patients with preexisting activation (5hmC hypermethylation) of gene sets involving AR, FOXA1 and GRHL2 before initiating ADT. Activity scores in these gene sets may serve as sensitive biomarkers to determine treatment resistance, monitor disease progression and potentially identify patients who would benefit from upfront treatment intensification. More studies are needed to validate this initial finding. Patient summary: There are no clinical tests to identify prostate cancer patients who will develop early resistance to androgen deprivation therapy within 24 months. In this study, we evaluated cell-free DNA epigenomic modification in blood and identified significant enrichment of 5-hydroxymethylation in androgen response genes in a subgroup of patients with treatment resistance. High level 5-hydroxylmethylation in these genes may serve as a discriminative biomarker to diagnose patients who are likely to experience early failure during androgen deprivation therapy.

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma.

Background: Despite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment. Methods: Transcriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment. Results: A prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells. Conclusions: The prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.

Case report and literature review: thyroid carcinoma showing intrathyroid thymic carcinoma.

Background: Intrathyroid thymic carcinoma (ITTC) is a rare malignancy of the thyroid gland with histological and immunophenotypic resemblance to thymic carcinoma. Surgery combined with adjuvant radiotherapy improves the survival of patients with ITTC. However, for patients with extensive metastases, there is currently no effective treatment. Chemotherapy is an option but has not demonstrated improved patient survival. Methods and results: A female patient presented with metastases to the pleura, lung, and bone 16 years after surgery for ITTC. As radiotherapy and chemotherapy failed to control the recurrent disease, lenvatinib treatment was initiated. After 3 months, positron emission tomography/computed tomography showed a substantial reduction of all metastatic lesions and decreased tumor metabolism. The patient continues to receive lenvatinib and remains well and symptom-free. Conclusion: For patients with ITTC who have progressive, life-threatening metastases, lenvatinib represents a valuable salvage therapy that may offer a sustained reduction in tumor burden and maintenance of quality of life.

Development of a Novel Prognostic Model of Glioblastoma Based on m6A-Associated Immune Genes and Identification of a New Biomarker.

Background: Accumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified. Methods: We obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX-least absolute shrinkage and selection operator (LASSO)-multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection in vitro assays to determine the biological role of CD81 in glioblastoma cells. Results: Our glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients' survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 in vitro significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells. Conclusion: We developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.

Prognostic Role of M6A-Associated Immune Genes and Cluster-Related Tumor Microenvironment Analysis: A Multi-Omics Practice in Stomach Adenocarcinoma.

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.

Facile Synthesis of Microporous Ferrocenyl Polymers Photocatalyst for Degradation of Cationic Dye.

Microporous organic polymers (MOPs) were prepared by condensation reactions from substituent-group-free carbazole and pyrrole with 1,1'-ferrocenedicarboxaldehyde without adding any catalysts. The resultant MOPs were insoluble in common solvent and characterized by FTIR, XPS, TGA and SEM. An N2 adsorption test showed that the obtained polymers PFcMOP and CFcMOP exhibited Brunauer-Emmett-Teller (BET) surface areas of 48 and 105 m2 g-1, respectively, and both polymers possessed abundant micropores. The MOPs with a nitrogen and ferrocene unit could be potentially applied in degrading dye with high efficiency.

Development of Tumor Mutation Burden-Related Prognostic Model and Novel Biomarker Identification in Stomach Adenocarcinoma.

Background: Stomach adenocarcinoma (STAD) is one of the most common tumors. Tumor mutation burden (TMB) has been linked to immunotherapy response. We wanted to see if there was any link between TMB and cancer prognosis. Methods: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to obtain mutation data, gene expression profiles, and clinical data. We looked at the differences in gene expression and immune markers between low and high TMB groups, built an immune prognostic model, and created a dynamic nomograph App that may be used in the clinic. Simultaneously, We ran the immunotherapy prediction and model comparison at the same time. Finally, model gene mutation and copy number variation (CNV) were displayed. The cellular functional experiments were used to investigate the potential role of GLP2R in gastric cancer. Results: Firstly, basic mutation information and differences in immune infiltration in STAD are revealed. Secondly, the prognostic model developed by us has good accuracy, and the corresponding dynamic nomograph Apps online and immunotherapy prediction facilitate clinical transformation. Furthermore, GLP2R knockdown significantly inhibited the proliferation, migration of gastric cancer cells in vitro. Conclusion: Our findings imply that TMB plays a significant role in the prognosis of STAD patients from a biological perspective. GLP2R may serve as a potential target for gastric cancer.

Targeting polarized phenotype of microglia via IL6/JAK2/STAT3 signaling to reduce NSCLC brain metastasis.

Tumor-associated macrophages have emerged as crucial factors for metastases. Microglia are indispensable components of the brain microenvironment and play vital roles in brain metastasis (BM). However, the underlying mechanism of how activated microglia promote brain metastasis of non-small cell lung cancer (NSCLC) remains elusive. Here, we purified cell lines with brain-metastatic tropism and employed a co-culture system to reveal their communication with microglia. By single-cell RNA-sequencing and transcriptome difference analysis, we identified IL6 as the key regulator in brain-metastatic cells (A549-F3) to induce anti-inflammatory microglia via JAK2/STAT3 signaling, which in turn promoted the colonization process in metastatic A549-F3 cells. In our clinical samples, patients with higher levels of IL6 in serum showed higher propensity for brain metastasis. Additionally, the TCGA (The Cancer Genome Atlas) data revealed that NSCLC patients with a lower level of IL6 had a longer overall survival time compared to those with a higher level of IL6. Overall, our data indicate that the targeting of IL6/JAK2/STAT3 signaling in activated microglia may be a promising new approach for inhibiting brain metastasis in NSCLC patients.

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04-1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17-0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

Association Between RSK2 and Clinical Indexes of Primary Breast Cancer: A Meta-Analysis Based on mRNA Microarray Data.

Background: Although ribosomal protein S6 kinases, 90 kDa, polypeptide 3 (RSK2, RPS6KA3) has been reported to play an important role in cancer cell proliferation, invasion, and migration, including breast cancer, its clinical implication in primary breast cancer patients is not well understood, and there were not many studies to explore the relationship between RSK2 and breast cancer on a clinical level. Methods: A systematic series matrix file search uploaded from January 1, 2008 to November 31, 2017 was undertaken using ArrayExpress and Gene Expression Omnibus (GEO) databases. Search filters were breast cancer, RNA assay, and array assay. Files eligible for inclusion met the following criteria: a) sample capacity is over 100, b) tumor sample comes from unselected patient's primary breast tumor tissue, and c) expression of RSK2 and any clinical parameters of patients were available from the files. We use median as the cutoff value to assess the association between the expression of RSK2 and the clinical indexes of breast cancer patients. Finding: The meta-analysis identified 13 series matrix files from GEO database involving 3,122 samples that come from patients' primary breast cancer tissue or normal tissue. The expression of RSK2 in tumor tissues is lower than that in normal tissues [odds ratio (OR), 0.54; 95% credible interval (CI), 0.44-0.67; Cochran's Q test p = 0.14; I 2 = 41.7%]. Patients with a high expression of RSK2 showed more favorable overall survival [hazard ratio (HR), 0.71; 95% CI, 0.49-0.94; Cochran's Q test p = 0.95; I 2 = 0.0%] and less potential of distant metastasis (OR, 0.59; 95% CI, 0.41-0.87; Cochran's Q test p = 0.88; I 2 = 0.0%) and lymph node infiltration (OR, 0.81; 95% CI, 0.65-0.998; Cochran's Q test p = 0.09; I 2 = 42.8%). Besides, the expression of RSK2 in luminal breast cancer is lower than Cochran's Q test p = 0.06; I 2 = 63.5%). RSK2 overexpression corresponded with higher histological grade (OR, 1.329; 95% CI, 1.03-1.721; Cochran's Q test p = 0.69; I 2 = 0.0%). RSK2 expression is also associated with estrogen receptor (ER) and age. Conclusion: The meta-analysis provides evidence that RSK2 is a potential biomarker in breast cancer patients. The expression of RSK2 is distinctive in different intrinsic subtypes of breast cancer, indicating that it may play an important role in specific breast cancer. Further study is needed to uncover the mechanism of RSK2 in breast cancer. Systematic Review Registration: (website), identifier (registration number).

Hypoxia Constructing the Prognostic Model of Colorectal Adenocarcinoma and Related to the Immune Microenvironment.

Background: Hypoxia is a common phenomenon in solid tumors, which plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism and chemoradiotherapy resistance. However, comprehensive analysis of hypoxia markers in colorectal adenocarcinoma (COAD) is still lacking. And there is a need for mechanism exploration and clinical application. Methods: The gene expression, mutation and clinical data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. Tumor samples from TCGA were randomly divided into the training and internal validation groups, while tumor samples from GEO were used as the external validation group. Univariate COX-LASSO-multivariate COX method was applied to construct the prognostic model. We clustered all TCGA tumor samples into high, medium and low hypoxia groups, evaluated the correlation between hypoxia degree and immunoactivity, and explored the combined effect of mutation for common target genes and model riskscore on survival in COAD patients. Finally, we developed a dynamic nomograph App online for direct clinical application and carried out multiple validations of the prognostic model. Results: Our hypoxia-related prognostic model for COAD patients is accurate and has been successfully validated internally and externally. Single Sample Gene Set Enrichment Analysis (ssGSEA) and Gene Set Enrichment Analysis (GSEA) results suggest that for COAD patients with higher hypoxia, the stronger the associated immunosuppressive activity, providing a possible mechanism for the lower survival rate. Finally, the dynamic nomograph App online enhances the clinical translational significance of the study. Conclusion: In this study, an accurate prognostic model for COAD patients was established and validated. In addition, our innovative findings include correlations between hypoxia levels and immune activity, as well as an in-depth exploration of common target gene mutations.

Irradiation-Induced Activated Microglia Affect Brain Metastatic Colonization of NSCLC Cells via miR-9/CDH1 Axis.

BACKGROUND AND PURPOSE: Brain metastasis is among the leading causes of death in patients with non-small-cell lung cancer (NSCLC). Through yet unknown mechanisms, prophylactic cranial irradiation (PCI) can significantly decrease the incidence of brain metastases. Given that PCI probably exerts indirect anti-tumoral effects by turning cerebral "soil" unfavorable for the colonization of metastatic tumor "seeds". This study aims to reveal how PCI regulates the brain microenvironment conducing to a reduction in brain metastases. MATERIALS AND METHODS: Key markers of M1/M2 microglia types and mesenchymal-to-epithelial transition (MET) were analyzed by qRT-PCR and Western Blot in vitro. The target miR-9 was obtained by miRNA array analysis and confirmed by qRT-PCR in microglia. We used miRTarBase and TargetScan to analyze the target genes of miR-9 and confirmed by luciferase activity assay. Anti-metastatic effects of irradiation on the brain were evaluated by intravital imaging using a brain metastatic A549-F3 cell line in a nude mouse model. RESULTS: Irradiation induced M1 microglia activation, which inhibited the MET process of A549 cell lines. Furthermore, levels of miR-9 secreted by irradiated M1 microglia significantly increased and played a vital role in the inhibition of the A549 MET process by directly targeting CDH1, concurrently decreasing cell capacity for localization in the brain, thus reducing brain metastases. CONCLUSION: We demonstrated that miR-9 secreted by irradiated M1-type microglia played an important role in modulating A549 cell lines into mesenchymal phenotype and further decreased their localization capabilities in the brain. Our findings signify the modulating effect of irradiation on metastatic soil and the cross-talk between tumour cells and the metastatic microenvironment; importantly, they provide new opportunities for effective anti-metastasis therapies, especially for brain metastasis patients.

Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage.

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas' point clouds. The symmetry was realized by rotating and reflecting the pagodas' point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

The distribution of dissimilatory nitrate reduction to ammonium bacteria in multistage constructed wetland of Jining, Shandong, China.

Dissimilatory nitrate reduction to ammonium (DNRA) is an important process of nitrate reduction in the environment. The distribution of DNRA bacteria and the relationships with environmental factors in multistage constructed wetland were investigated in this study. The quantitative real-time polymerase chain reaction analysis showed that the abundance of DNRA bacteria at all sites ranged from 2.10 × 1010 to 1.10 × 1011 copies/g of dry sediments. The Anaeromyxobacter (belong to Deltaproteobacteria) was the most abundant DNRA bacteria at all sites. The Geobater known as DNRA bacteria was also identified in this study. The abundances of DNRA bacteria, denitrifying bacteria, and anammox bacteria were conspicuously negatively correlated with Eh and positively correlated with the NO3--N removal efficency by statistical analysis.

A Novel RNA Binding Protein-Related Prognostic Signature for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly malignant and aggressive cancer with high recurrence rates and mortality. Some studies have illustrated that RNA binding proteins (RBPs) were involved in the carcinogenesis and development of multiple cancers, but the roles in HCC were still unclear. We downloaded the RNA-seq and corresponding clinical information of HCC from The Cancer Genome Atlas (TCGA) database, and 330 differentially expressed RBPs were identified between normal and HCC tissues. Through series of the univariate, the least absolute shrinkage selection operator (LASSO), and the stepwise multivariate Cox regression analyses, six prognosis-related key RBPs (CNOT6, UPF3B, MRPL54, ZC3H13, IFIT5, and PPARGC1A) were screened out from DE RBPs, and a six-RBP gene risk score signature was constructed in training set. Survival analysis indicated that HCC patients with high-risk scores had significantly worse overall survival than low-risk patients, and furthermore, the signature can be used as an independent prognostic indicator. The good accuracy of this prognostic signature was confirmed by the ROC curve analysis and was further validated in the International Cancer Genome Consortium (ICGC) HCC cohort. Besides, a nomogram based on six RBP genes was established and internally validated in the TCGA cohort. Gene set enrichment analysis demonstrated some cancer-related phenotypes were significantly gathered in the high-risk group. Overall, our study first identified an RBP-related six-gene prognostic signature, which could serve as a promising prognostic biomarker and provide some potential therapeutic targets for HCC.