The effects of noise exposure on hippocampal cognition in C57BL/6 mice via transcriptomics.

BACKGROUND: Noise is an important environmental stressor in the industrialized world and has received increasing attention in recent years. Although epidemiological research has extensively demonstrated the relationship between noise and cognitive impairment, the specific molecular mechanisms and targets remain to be fully explored and understood. METHODS: To address this issue, 5-month-old C57BL/6 mice were divided into two groups, with one group exposed to white noise at 98 dB. The effects of noise on cognition in mice were investigated through molecular biology and behavioral experiments. Subsequently, transcriptomic sequencing of the hippocampus in both groups of mice was performed and enrichment analysis of differentially expressed genes (DEGs) was conducted using KEGG and GO databases. Furthermore, LASSO analysis was used to further narrow down the relevant DEGs, followed by enrichment analysis of these genes using KEGG and GO databases. The DEGs were further validated by rt-qPCR. RESULTS: Following noise exposure, the hippocampus levels of inflammation-related factors increased, the phosphorylation of Tau protein increased, the postsynaptic density protein decreased, the number of Nissl bodies decreased, and cell shrinkage in the hippocampus increased. Moreover, the behavioral experiments manifest characteristics indicative of a decline in cognitive.A total of 472 DEGs were identified through transcriptomic analysis, and seven relevant genes were screened by the LASSO algorithm, which were further validated by PCR to confirm their consistency with the omics results. CONCLUSION: In conclusion, noise exposure affects cognitive function in mice through multiple pathways, and the omics results provide new evidence for the cognitive impairment induced by noise exposure.

Enhancing postpartum hemorrhage prediction in pernicious placenta previa: a comparative study of magnetic resonance imaging and ultrasound nomogram.

Objective: To explore the risk factors of postpartum hemorrhage (PPH) in patients with pernicious placenta previa (PPP) and to develop and validate a clinical and imaging-based predictive model. Methods: A retrospective analysis was conducted on patients diagnosed surgically and pathologically with PPP between January 2018 and June 2022. All patients underwent PPP magnetic resonance imaging (MRI) and ultrasound scoring in the second trimester and before delivery, and were categorized into two groups according to PPH occurrence. The total imaging score and sub-item prediction models of the MRI risk score/ultrasound score were used to construct Models A and B/Models C and D. Models E and F were the total scores of the MRI combined with the ultrasound risk and sub-item prediction model scores. Model G was based on the subscores of MRI and ultrasound with the introduction of clinical data. Univariate logistic regression analysis and the logical least absolute shrinkage and selection operator (LASSO) model were used to construct models. The receiver operating characteristic curve andision curve analysis (DCA) were drawn, and the model with the strongest predictive ability and the best clinical effect was selected to construct a nomogram. Internal sampling was used to verify the prediction model's consistency. Results: 158 patients were included and the predictive power and clinical benefit of Models B and D were better than those of Models A and C. The results of the area under the curve of Models B, D, E, F, and G showed that Model G was the best, which could reach 0.93. Compared with Model F, age, vaginal hemorrhage during pregnancy, and amniotic fluid volume were independent risk factors for PPH in patients with PPP (p < 0.05). We plotted the DCA of Models B, D, E, F, and G, which showed that Model G had better clinical benefits and that the slope of the calibration curve of Model G was approximately 45°. Conclusion: LASSO regression nomogram based on clinical risk factors and multiple conventional ultrasound plus MRI signs has a certain guiding significance for the personalized prediction of PPH in patients with PPP before delivery.

Immune regulation and prognosis indicating ability of a newly constructed multi-genes containing signature in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common renal malignancy, although newly developing targeted therapy and immunotherapy have been showing promising effects in clinical treatment, the effective biomarkers for immune response prediction are still lacking. The study is to construct a gene signature according to ccRCC immune cells infiltration landscape, thus aiding clinical prediction of patients response to immunotherapy. METHODS: Firstly, ccRCC transcriptome expression profiles from Gene Expression Omnibus (GEO) database as well as immune related genes information from IMMPORT database were combine applied to identify the differently expressed meanwhile immune related candidate genes in ccRCC comparing to normal control samples. Then, based on protein-protein interaction network (PPI) and following module analysis of the candidate genes, a hub gene cluster was further identified for survival analysis. Further, LASSO analysis was applied to construct a signature which was in succession assessed with Kaplan-Meier survival, Cox regression and ROC curve analysis. Moreover, ccRCC patients were divided as high and low-risk groups based on the gene signature followed by the difference estimation of immune treatment response and exploration of related immune cells infiltration by TIDE and Cibersort analysis respectively among the two groups of patients. RESULTS: Based on GEO and IMMPORT databases, a total of 269 differently expressed meanwhile immune related genes in ccRCC were identified, further PPI network and module analysis of the 269 genes highlighted a 46 genes cluster. Next step, Kaplan-Meier and Cox regression analysis of the 46 genes identified 4 genes that were supported to be independent prognosis indicators, and a gene signature was constructed based on the 4 genes. Furthermore, after assessing its prognosis indicating ability by both Kaplan-Meier and Cox regression analysis, immune relation of the signature was evaluated including its association with environment immune score, Immune checkpoint inhibitors expression as well as immune cells infiltration. Together, immune predicting ability of the signature was preliminary explored. CONCLUSIONS: Based on ccRCC genes expression profiles and multiple bioinformatic analysis, a 4 genes containing signature was constructed and the immune regulation of the signature was preliminary explored. Although more detailed experiments and clinical trials are needed before potential clinical use of the signature, the results shall provide meaningful insight into further ccRCC immune researches.

Identification of Cuproptosis Clusters and Integrative Analyses in Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease; it mainly occurs in the elderly population. Cuproptosis is a newly discovered form of regulated cell death involved in the progression of various diseases. Combining multiple GEO datasets, we analyzed the expression profile and immunity of cuproptosis-related genes (CRGs) in PD. Dysregulated CRGs and differential immune responses were identified between PD and non-PD substantia nigra. Two CRG clusters were defined in PD. Immune analysis suggested that CRG cluster 1 was characterized by a high immune response. The enrichment analysis showed that CRG cluster 1 was significantly enriched in immune activation pathways, such as the Notch pathway and the JAK-STAT pathway. KIAA0319, AGTR1, and SLC18A2 were selected as core genes based on the LASSO analysis. We built a nomogram that can predict the occurrence of PD based on the core genes. Further analysis found that the core genes were significantly correlated with tyrosine hydroxylase activity. This study systematically evaluated the relationship between cuproptosis and PD and established a predictive model for assessing the risk of cuproptosis subtypes and the outcome of PD patients. This study provides a new understanding of PD-related molecular mechanisms and provides new insights into the treatment of PD.

The Interplay Between Metabolites and MicroRNAs in Aqueous Humor to Coordinate Corneal Endothelium Integrity.

Purpose: The purpose of the study was to clarify the interplay between metabolites and microRNAs (miRs) in the aqueous humor (AqH) of bullous keratopathy (BK) patients to retain human corneal endothelium (HCE) integrity. Design: Prospective, comparative, observational study. Participants: A total of 55 patients with BK and 31 patients with cataract (Cat) as control. Methods: A biostatic analysis of miRs and metabolites in the AqH, hierarchical clustering, and a least absolute shrinkage and selection operator (Lasso) analysis were employed. The miR levels in AqH of BK (n = 18) and Cat (n = 8) patients were determined using 3D-Gene human miR chips. Hierarchical clusters of metabolites detected by liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry in AqH specimens from 2 disease groups, BK (total n = 55) and Cat (total n = 31), were analyzed twice to confirm the reproducibility. The analytical procedure applied for investigating the association between metabolites and miRs in AqH was the exploratory data analysis of biostatistics to avoid any kind of prejudice. This research procedure includes a heat-map, cluster analysis, feature extraction techniques by principal component analysis, and a regression analysis method by Lasso. The cellular and released miR levels were validated using reverse transcription polymerase chain reaction and mitochondria membrane potential was assessed to determine the functional features of the released miRs. Main Outcome Measures: Identification of interacting metabolites and miRs in AqH attenuating HCE degeneration. Results: The metabolites that decreased in the AqH of BK patients revealed that 3-hydroxyisobutyric acid (HIB), 2-aminobutyric acid (AB) and branched-chain amino acids, and serine were categorized into the same cluster by hierarchical clustering of metabolites. The positive association of HIB with miR-34a-5p was confirmed (P = 0.018), and the Lasso analysis identified the interplay between miR-34a-5p and HIB, between miR-24-3p and AB, and between miR-34c-5p and serine (P = 0.041, 0.027, and 0.009, respectively). 3-hydroxyisobutyric acid upregulated the cellular miR-34a expression, mitochondrial membrane potential, and release of miR-184 in dedifferentiated cultured HCE cells. Conclusions: Metabolites and miRs in AqH may synchronize in ensuring the integrity of the HCE to maintain efficient dehydration from the stroma. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Construction of an STK11 Mutation and Immune-Related Prognostic Prediction Model in Lung Adenocarcinoma.

Background: STK11 mutation in LUAD affects immune cell infiltration in tumor tissue, and is associated with tumor prognosis. Objective: This study aimed to construct a STK11 mutation and immune-related LUAD prognostic model. Materials and Methods: The mutation frequency of STK11 in LUAD was queried via cBioPortal in TCGA and PanCancer Atlas databases. The degree of immune infiltration was analyzed by CIBERSORT analysis. DEGs in STK11mut and STK11wt samples were analyzed. Metascape, GO and KEGG methods were adopted for functional and signaling pathway enrichment analysis of DEGs. Genes related to immune were overlapped with DEGs to acquire immune-related DEGs, whose Cox regression and LASSO analyses were employed to construct prognostic model. Univariate and multivariate Cox regression analyses verified the independence of riskscore and clinical features. A nomogram was established to predict the OS of patients. Additionally, TIMER was introduced to analyze relationship between infiltration abundance of 6 immune cells and expression of feature genes in LUAD. Results: The mutation frequency of STK11 in LUAD was 16%, and the degrees of immune cell infiltration were different between the wild-type and mutant STK11. DEGs of STK11 mutated and unmutated LUAD samples were mainly enriched in immune-related biological functions and signaling pathways. Finally, 6 feature genes were obtained, and a prognostic model was established. Riskscore was an independent immuno-related prognostic factor for LUAD. The nomogram diagram was reliable. Conclusion: Collectively, genes related to STK11 mutation and immunity were mined from the public database, and a 6-gene prognostic prediction signature was generated.

Identifying key transcription factors and immune infiltration in non-small-cell lung cancer using weighted correlation network and Cox regression analyses.

Introduction: Lung cancer is one of the most common cancers and a significant cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases. Therefore, it is crucial to identify effective diagnostic and therapeutic methods. In addition, transcription factors are essential for eukaryotic cells to regulate their gene expression, and aberrant expression transcription factors are an important step in the process of oncogenesis in NSCLC. Methods: Differentially expressed transcription factors between NSCLC and normal tissues by analyzing mRNA profiling from The Cancer Genome Atlas (TCGA) database program were identified. Weighted correlation network analysis (WGCNA) and line plot of least absolute shrinkage and selection operator (LASSO) were performed to find prognosis-related transcription factors. The cellular functions of transcription factors were performed by 5-ethynyl-2'-deoxyuridine (EdU) assay, wound healing assay, cell invasion assay in lung cancer cells. Results: We identified 725 differentially expressed transcription factors between NSCLC and normal tissues. Three highly related modules for survival were discovered, and transcription factors highly associated with survival were obtained by using WGCNA. Then line plot of LASSO was applied to screen transcription factors related to prognosis and build a prognostic model. Consequently, SETDB2, SNAI3, SCML4, and ZNF540 were identified as prognosis-related transcription factors and validated in multiple databases. The low expression of these hub genes in NSCLC was associated with poor prognosis. The deletions of both SETDB2 and SNAI3 were found to promote proliferation, invasion, and stemness in lung cancer cells. Furthermore, there were significant differences in the proportions of 22 immune cells between the high- and low-score groups. Discussion: Therefore, our study identified the transcription factors involved in regulating NSCLC, and we constructed a panel for the prediction of prognosis and immune infiltration to inform the clinical application of transcription factor analysis in the prevention and treatment of NSCLC.

A novel model based on immune-related genes for differentiating biliary atresia from other cholestatic diseases.

PURPOSE: Based on a public gene expression database, this study established the immune-related genetic model that distinguished BA from other cholestasis diseases (DC) for the first time. We explored the molecular mechanism of BA based on the gene model. METHODS: The BA microarray dataset GSE46960, containing BA, other cause of intrahepatic cholestasis than biliary atresia and normal liver gene expression data, was downloaded from the Gene Expression Omnibus (GEO) database. We performed a comprehensive bioinformatics analysis to establish and validate an immune-related gene model and subsequently identified hub genes as biomarkers associated with the molecular mechanisms of BA. To assess the model's performance for separating BA from other cholestasis diseases, we used receiver operating characteristic (ROC) curves and the area under the curve (AUC) of the ROC. Independent datasets GSE69948 and GSE122340 were used for the validation process. RESULTS: The model was built using eight immune-related genes, including EDN1, HAMP, SAA1, SPP1, ANKRD1, MMP7, TACSTD2, and UCA1. In the GSE46960 and validation group, it presented excellent results, and the prediction accuracy of BA in comparison to other cholestasis diseases was good. Functional enrichment analysis revealed significant immunological differences between BA and other cholestatic diseases. Finally, we found that the TNFα-NF-κB pathway is associated with EDN1 gene expression and may explain fibrosis progression, which may become a new therapeutic target. CONCLUSION: In summary, we have successfully constructed an immune-related gene model that can distinguish BA from other cholestatic diseases, while identifying the hub gene. Our exploration of immune genes provides new clues for the early diagnosis, molecular mechanism, and clinical treatment of biliary atresia.

Analysis of the Prognostic Significance and Immune Infiltration of the Amino Acid Metabolism-Related Genes in Colon Adenocarcinoma.

Amino acid metabolization is verified to be a part in the progression of cancer. However, genes related to the amino acid metabolism have not been identified in colon adenocarcinoma (COAD). A systematic prognostic model of COAD becomes a pressing need. Among genes related to the amino acid metabolism, RIMKLB, ASPG, TH, MTAP, AZIN2, PSMB2, HDC, ACMSD, and PSMA8 were identified to construct a risk model. Kaplan-Meier (K-M) analyses demonstrated that the high-risk group achieved a poor prognosis. Area under the respective ROC (AUC) values indicated the robustness of the model. To highlight its clinical value, multivariate Cox was used to obtain the optimal variables to construct a nomogram. A higher tumor mutation burden was observed in the high-risk group. However, the low-risk group had a stronger immune infiltration. Seven molecular subtypes were found by consensus cluster. Twenty-two hub genes were identified related to the ESTIMATE score using WGCNA. In brief, our research constructed a stable prognostic model related to the amino acid metabolism in COAD, revealing its connection to the immune microenvironment. The model guided the outcome of COAD and the direction of immunotherapy.

A Novel Prognostic Signature Based on Glioma Essential Ferroptosis-Related Genes Predicts Clinical Outcomes and Indicates Treatment in Glioma.

Background: Ferroptosis is a form of programmed cell death (PCD) that has been implicated in cancer progression, although the specific mechanism is not known. Here, we used the latest DepMap release CRISPR data to identify the essential ferroptosis-related genes (FRGs) in glioma and their role in patient outcomes. Methods: RNA-seq and clinical information on glioma cases were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). FRGs were obtained from the FerrDb database. CRISPR-screened essential genes (CSEGs) in glioma cell lines were downloaded from the DepMap portal. A series of bioinformatic and machine learning approaches were combined to establish FRG signatures to predict overall survival (OS) in glioma patients. In addition, pathways analysis was used to identify the functional roles of FRGs. Somatic mutation, immune cell infiltration, and immune checkpoint gene expression were analyzed within the risk subgroups. Finally, compounds for reversing high-risk gene signatures were predicted using the GDSC and L1000 datasets. Results: Seven FRGs (ISCU, NFS1, MTOR, EIF2S1, HSPA5, AURKA, RPL8) were included in the model and the model was found to have good prognostic value (p < 0.001) in both training and validation groups. The risk score was found to be an independent prognostic factor and the model had good efficacy. Subgroup analysis using clinical parameters demonstrated the general applicability of the model. The nomogram indicated that the model could effectively predict 12-, 36-, and 60-months OS and progression-free interval (PFI). The results showed the presence of more aggressive phenotypes (lower numbers of IDH mutations, higher numbers of EGFR and PTEN mutations, greater infiltration of immune suppressive cells, and higher expression of immune checkpoint inhibitors) in the high-risk group. The signaling pathways enriched closely related to the cell cycle and DNA damage repair. Drug predictions showed that patients with higher risk scores may benefit from treatment with RTK pathway inhibitors, including compounds that inhibit RTKs directly or indirectly by targeting downstream PI3K or MAPK pathways. Conclusion: In summary, the proposed cancer essential FRG signature predicts survival and treatment response in glioma.

Comprehensive Genome-Scale Analysis of Esophageal Carcinoma With Esophageal Tissue-Resident Micro-Environment Discrepancy.

To figure out the molecular mechanism in the esophageal squamous carcinoma (ESCC) with the discrepancy in the tissue-resident microbiota, we selected clinical features, RNA sequences, and transcriptomes of ESCC patients from The Cancer Genome Atlas (TCGA) website and detailed tissue-resident microbiota information from The Cancer Microbiome Atlas (n = 60) and explored the infiltration condition of particular microbiota in each sample. We classified the tissue-resident micro-environment of ESCC into two clusters (A and B) and built a predictive classifier model. Cluster A has a higher proportion of certain tissue-resident microbiota with comparatively better survival, while Cluster B has a lower proportion of certain tissue-resident microbiota with comparatively worse survival. We showed traits of gene and clinicopathology in the esophageal tissue-resident micro-environment (ETM) phenotypes. By comparing the two clusters' molecular signatures, we find that the two clusters have obvious differences in gene expression and mutation, which lead to pathway expression discrepancy. Several pathways are closely related to tumorigenesis. Our results may demonstrate a synthesis of the infiltration pattern of the esophageal tissue-resident micro-environment in ESCC. We reveal the mechanism of esophageal tissue-resident microbiota discrepancy in ESCC, which may contribute to therapy progress for patients with ESCC.

Integrative Analysis of Multi-Omics Data-Identified Key Genes With KLRC3 as the Core in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma.

In a recent study, the PD-1 inhibitor has been widely used in clinical trials and shown to improve various cancers. However, PD-1/PD-L1 inhibitors showed a low response rate and were effective for only a small number of cancer patients. Thus, it is important to figure out the issue about the low response rate of immunotherapy. Here, we performed ssGSEA and unsupervised clustering analysis to identify three clusters (clusters A, B, and C) according to different immune cell infiltration status, prognosis, and biological action. Of them, cluster C showed a better survival rate, higher immune cell infiltration, and immunotherapy effect, with enrichment of a variety of immune active pathways including T and B cell signal receptors. In addition, it showed more significant features associated with immune subtypes C2 and C3. Furthermore, we used WGCNA analysis to confirm the cluster C-associated genes. The immune-activated module highly correlated with 111 genes in cluster C. To pick candidate genes in SD/PD and CR/PR patients, we used the least absolute shrinkage (LASSO) and SVM-RFE algorithms to identify the targets with better prognosis, activated immune-related pathways, and better immunotherapy. Finally, our analysis suggested that there were six genes with KLRC3 as the core which can efficiently improve immunotherapy responses with greater efficacy and better prognosis, and our study provided clues for further investigation about target genes associated with the higher response rate of immunotherapy.

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer.

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

A Novel Tool for the Risk Assessment and Personalized Chemo-/Immunotherapy Response Prediction of Adenocarcinoma and Squamous Cell Carcinoma Lung Cancer.

BACKGROUND: The prevalence and cancer-specific death rate of lung cancer (LC) have risen in recent decades. A universally applicable prognostic signature for both adenocarcinoma LC (LUAD) and squamous cell carcinoma LC (LUSC) is still lacking. METHODS: A total of 453 patients from The Cancer Genome Atlas (TCGA)-LUAD cohort and 452 patients from TCGA-LUSC cohort were enrolled, and a prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis based on the consensus prognostic genes in both cohorts. The newly defined pan-lung cancer risk count (PLCRC) of each patient was calculated via the summation formula. RESULTS: A total of 23 genes were selected for the calculation of the PLCRC. The PLCRC showed a moderate prognostic value in the entire (p < 0.001, HR: 2.75, AUC: 0.643), LUAD (p < 0.001, HR: 2.51, AUC: 0.636) and LUSC (p < 0.001, HR: 2.89, AUC: 0.656) cohorts. The PLCRC was an independent prognostic factor after adjusting the clinical features. The PLCRC was also effective in nine external validation cohorts and in patients with different clinical features. Activation of extracellular matrix pathways and infiltration of immunocytes promoted the tumorigenesis and development of both LUAD and LUSC. We generated a universally applicable prognostic signature, the PLCRC, which could dichotomize patients with significantly different clinical outcomes and guide the clinical treatment of LC patients. Chemotherapy is more suitable for patients with a low PLCRC, while anti-cytotoxic T-lymphocyte-associated protein 4 immunotherapy is more suitable for patients with a high PLCRC. CONCLUSION: We established and validated a newly defined prognostic signature, the PLCRC, for both LUAD and LUSC patients and provided clinical strategies for patients from different risk subgroups.

Established and Validated Novel Nomogram for Predicting Prognosis of Post-Mastectomy pN0-1 Breast Cancer without Adjuvant Radiotherapy.

AIM: To establish and validate a nomogram for predicting prognosis of breast cancer patients with pN0-1 who were treated with mastectomy and without adjuvant radiotherapy. MATERIAL AND METHODS: The LASSO regression was performed to identify predictors of breast cancer-specific survival (BCSS), local regional recurrence (LRR) and distant metastasis (DM). Model performance was evaluated by the concordance index (C-index) and calibration plot. RESULTS: The 5-year BCSS, LRR and DM rates for the entire cohort were 98%, 2% and 4%, respectively. LASSO regression analysis found that pathological T stage, number of positive LN, grade and Ki-67 were significant predictors for both BCSS and DM-free survival, while number of resected LN and PR status were predictors for DM-free survival. In addition, number of positive LN was the only significant predictor for developing LRR. The C-indexes for the 5-year BCSS and DM nomograms were 0.81 and 0.78 in the training data set, 0.65 and 0.70 in the testing set and 0.72 and 0.69 in the external validation set, respectively. CONCLUSION: Our prognostic nomograms accurately predict 5-year BCSS and DM-free survival in post-mastectomy breast cancer without adjuvant radiotherapy, which provides a useful tool to identify high-risk patients who could benefit from additional adjuvant therapy.

Integrated Analysis Revealed the MicroRNA-Based Prognostic Predicting Signature for Papillary Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is one of the most frequently occurring tumors worldwide. Herein, we established a microRNA (miRNA) predicting signature to assess the prognosis of papillary-type RCC (PRCC) patients. miR-1293, miR-34a, miR-551b, miR-937, miR-299, and miR-3199-2 were used in building the overall survival (OS)-related signature, whereas miR-7156, miR-211, and miR-301b were used to construct the formula of recurrence-free survival (RFS) with the help of LASSO Cox regression analysis. The Kaplan-Meier and receiver operating characteristic curves indicated good discrimination and efficiency of the two signatures. Functional annotation for the downstream genes of the OS/RFS-related miRNAs exposed the potential mechanisms of PRCC. Notably, the multivariate analyses suggested that the two signatures were independent risk factors for PRCC patients and had better prognostic capacity than any other classifier. In addition, the nomogram indicated synthesis effects and showed better predictive performance than clinicopathologic features and our signatures. We validated the OS and RFS prediction formulas in clinical samples and met our expectations. Finally, we established two novel miRNA-based OS and RFS predicting signatures for PRCC, which are reliable tools for assessing the prognosis of PRCC patients.

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis.

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

Coupling Bacterioplankton Populations and Environment to Community Function in Coastal Temperate Waters.

Bacterioplankton play a key role in marine waters facilitating processes important for carbon cycling. However, the influence of specific bacterial populations and environmental conditions on bacterioplankton community performance remains unclear. The aim of the present study was to identify drivers of bacterioplankton community functions, taking into account the variability in community composition and environmental conditions over seasons, in two contrasting coastal systems. A Least Absolute Shrinkage and Selection Operator (LASSO) analysis of the biological and chemical data obtained from surface waters over a full year indicated that specific bacterial populations were linked to measured functions. Namely, Synechococcus (Cyanobacteria) was strongly correlated with protease activity. Both function and community composition showed seasonal variation. However, the pattern of substrate utilization capacity could not be directly linked to the community dynamics. The overall importance of dissolved organic matter (DOM) parameters in the LASSO models indicate that bacterioplankton respond to the present substrate landscape, with a particular importance of nitrogenous DOM. The identification of common drivers of bacterioplankton community functions in two different systems indicates that the drivers may be of broader relevance in coastal temperate waters.