Integrated single-cell and bulk RNA sequencing analysis identifies a cancer-associated fibroblast-related gene signature for predicting survival and therapy in gastric cancer.

As the dominant component of the tumor microenvironment, cancer-associated fibroblasts (CAFs), play a vital role in tumor progression. An increasing number of studies have confirmed that CAFs are involved in almost every aspect of tumors including tumorigenesis, metabolism, invasion, metastasis and drug resistance, and CAFs provide an attractive therapeutic target. This study aimed to explore the feature genes of CAFs for potential therapeutic targets and reliable prediction of prognosis in patients with gastric cancer (GC). Bioinformatic analysis was utilized to identify the feature genes of CAFs in GC by performing an integrated analysis of single-cell and transcriptome RNA sequencing using R software. Based on these feature genes, a CAF-related gene signature was constructed for prognostic prediction by LASSO. Simultaneously, survival analysis and nomogram were performed to validate the prognostic predictive value of this gene signature, and qRT-PCR and immunohistochemical staining verified the expression of the feature genes of CAFs. In addition, small molecular drugs for gene therapy of CAF-related gene signatures in GC patients were identified using the connectivity map (CMAP) database. A combination of nine CAF-related genes was constructed to characterize the prognosis of GC, and the prognostic potential and differential expression of the gene signature were initially validated. Additionally, three small molecular drugs were deduced to have anticancer properties on GC progression. By integrating single-cell and bulk RNA sequencing analyses, a novel gene signature of CAFs was constructed. The results provide a positive impact on future research and clinical studies involving CAFs for GC.

A stable gene set for prediction of prognosis and efficacy of chemotherapy in gastric cancer.

BACKGROUND: Gastric cancer (GC) is a primary reason for cancer death in the world. At present, GC has become a public health issue urgently to be solved to. Prediction of prognosis is critical to the development of clinical treatment regimens. This work aimed to construct the stable gene set for guiding GC diagnosis and treatment in clinic. METHODS: A public microarray dataset of TCGA providing clinical information was obtained. Dimensionality reduction was carried out by selection operator regression on the stable prognostic genes discovered through the bootstrap approach as well as survival analysis. FINDINGS: A total of 2 prognostic models were built, respectively designated as stable gene risk scores of OS (SGRS-OS) and stable gene risk scores of PFI (SGRS-PFI) consisting of 18 and 21 genes. The SGRS set potently predicted the overall survival (OS) along with progression-free interval (PFI) by means of univariate as well as multivariate analysis, using the specific risk scores formula. Relative to the TNM classification system, the SGRS set exhibited apparently higher predicting ability. Moreover, it was suggested that, patients who had increased SGRS were associated with poor chemotherapeutic outcomes. INTERPRETATION: The SGRS set constructed in this study potentially serves as the efficient approach for predicting GC patient survival and guiding their treatment.

Ecosystem services dynamics and their influencing factors: Synergies/tradeoffs interactions and implications, the case of upper Blue Nile basin, Ethiopia.

Understanding the relationships among ecosystem services (ESs) and their interactions with influencing factors is essential for spatially targeted ecosystem governance. However, classifying the spatial distribution of these diverse interactions still needs improvement. Furthermore, existing studies have insufficiently addressed the specific impacts of bidirectional land cover transitions on ESs. Taking the upper Blue Nile basin as a study area, we estimated the spatiotemporal distribution of annual water yield (AWY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, using InVEST models and associated formulas. Changes in ESs per inward-outward land cover transition were quantified based on the Cross-Tabulation Matrix. An improved pairwise method was employed to assess the spatially diverse interactions between ESs pairs and their relationship with influencing factors. The statistical significance of influencing factors was evaluated using partial least square regression. The findings indicated that high HQ values were prevalent in the west, while they were in the east for SR. The central and southern areas experienced higher CS and AWY values. During the study period, variations were observed in the mean values of SR (ranging from 22.89 to 23.88 × 102 t/ha/y), AWY (32.13-42.2 × 102 mm/ha/y), CS (90.5-102.9 × 103gC/ha/y) and HQ (0.62-0.64). Synergies were predominant in AWY-CS, AWY-SR, and CS-SR pairs. HQ revealed more of a no-effect and tradeoff relationship with other ESs. The interactions between ESs and influencing factors were dominated by synergies, followed by tradeoffs and no-effect. The influence of landscape structure (gyrate and landscape shape index) and land surface temperature on all ESs and precipitation on AWY and SR was significant (1.049 ≤ Variable Importance in the Projection ≤ 1.371). Overall, the spatiotemporal dynamics of key ESs and the modeling of their drivers are essential policy information for taking spatially explicit conservation measures. This study will also serve as a valuable methodological reference for future research.

Comparative Effectiveness of Two Models of Point-of-Care Ultrasound for Detection of Post-Void Residual Urine during Acute Ischemic Stroke: Preliminary Findings of Real-World Clinical Application.

We conducted a comparative study of two models of point-of-care ultrasound devices for measuring post-void residual urine (PVRU). We prospectively enrolled 55 stroke inpatients who underwent both real-time B-mode ultrasound (Device A) and automated three-dimensional (3D) scanning ultrasound (Device B), with a total of 108 measurements. The median PVRU volume of Device B was 40 mL larger than that of Device A. The PVRU difference between the devices was positively and linearly correlated with PVRU. The correlation of PVRU volume between the devices was strong, but the agreement level was only moderate. Measurement deviations were observed in 43 (40%) and 11 (10%) measurements with Device B and Device A, respectively. The PVRU volume was low in spherical bladder shapes but sequentially increased in triangular, undefined, ellipsoid, and cuboid bladder shapes. Further comparison of 60 sets of PVRU without measurement deviations revealed higher agreements between the devices at correction coefficients of 0.52, 0.66, and 0.81 for PVRU volumes of <100, 100-200, and >200 mL, respectively. The automated 3D scanning ultrasound is more convenient for learning and scanning, but it exhibits larger measurement deviations. Real-time B-mode ultrasound accurately visualizes the urinary bladder but tends to underestimate the urinary bladder when the PVRU volume is large. Hence, real-time B-mode ultrasound with automated PVRU-based adjustment of calculation formulas may be a better solution for estimating bladder volume.

Targeting RPL23 restores chemosensitivity of cisplatin-resistant ovarian carcinoma by inhibiting EMT.

Chemoresistance is the inevitable outcome of chemotherapy for epithelial ovarian carcinoma (EOC), and its mechanism is still not fully understood. This study explored the role of ribosomal protein L23 (RPL23) in cisplatin resistance of EOC. WGCNA based on TCGA and GEO was used to screen and analyze target genes related to EOC chemotherapy sensitivity. Clinical samples of cisplatin resistance were collected to detect the expression of target genes. Cisplatin resistance was induced in EOC cell lines A2780 and SKOV3. The cell abilities of invasion, migration and adhesion were observed. Western blotting was used to detect protein expressions. Bioinformatics analysis showed that RPL23 may be related to EOC chemotherapy sensitivity, and was highly expressed in clinical samples and cell lines of cisplatin-resistant. After A2780 and SKOV3 were resistant to cisplatin, the inhibitory abilities of therapeutic dose of cisplatin on their invasion, migration and adhesion were significantly attenuated, and N-cadherin and vimentin were significantly up-regulated while E-cadherin was significantly down-regulated. However, above phenomena were significantly reversed after RPL23 knockdown. Taken together, the overexpressed RPL23 may lead to platinum resistance by inducing epithelial-mesenchymal transition (EMT) in EOC. Targeting knockdown RPL23 would restore the sensitivity of EOC cells to cisplatin by inhibiting EMT, suggesting that RPL23 is a potential therapeutic target for EOC after platinum resistance.

Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes.

BACKGROUND: Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear. MATERIALS AND METHODS: We identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (t-ROC) curve and Kaplan-Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups. RESULTS: Twenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration. CONCLUSION: The prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.

Study on the interaction mechanism between C-reactive protein and platelets in the development of acute myocardial infarction.

BACKGROUND: Myocardial infarction (MI) is the single most critical event in coronary disease. Platelets are involved in the processes of acute MI (AMI). They lack nuclear DNA but retain megakaryocyte mRNAs, hence, their transcriptome could provide information preceding coronary events. However, their mechanisms are not clear. In this study, we obtained a gene expression atlas of platelets from patients after their very first AMI, and our purpose was to clarify the mechanisms of platelet involvement in the occurrence of AMI through bioinformatics analyses and animal models of AMI in vivo. METHODS: We obtained a gene expression atlas of platelets from patients after their very first AMI from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were retrieved using R language. Weighted gene co-expression network analysis (WGCNA) was implemented in order to construct a gene co-expression correlation network among DEGs. Animal models of AMI in vivo were constructed to confirm the results of the bioinformatics analysis. RESULTS: Gene integration analysis yielded 2,852 DEGs (P<0.05, |log2FC| >1). Bioinformatics analysis demonstrated a significant association between C-reactive protein (CRP) and Staphylococcus aureus infection (SAI) (P=0.015). Data from in vivo experiments showed that CRP increased significantly in AMI rats (P<0.001), and the expression of FCGR2B mRNA and HLA-DRB4 mRNA was elevated in response to the increase of CRP (P<0.001). CONCLUSIONS: From the results of this study, we speculate that in the development of AMI, the increase in CRP activates platelets and induces platelets to play an anti-inflammatory role.

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets.

BACKGROUND: Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets. METHODS: We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups. RESULTS: Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II-IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways. CONCLUSION: The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.

Identification of a novel pyroptosis-related gene signature correlated with the prognosis of diffuse glioma patients.

BACKGROUND: Diffuse glioma is the most common primary tumor of the central nervous system and has a poor prognosis. Recently, a new type of programmed cell death (PCD), pyroptosis, has been found to be widely involved in the process of tumor diseases. However, the expression of pyroptosis-related genes (PRGs) in diffuse gliomas and their relationship with prognosis have rarely been evaluated. METHODS: In this study, we obtained RNA sequencing and clinical data from the Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas (CGGA) of diffuse glioma patients. Simultaneously, differentially expressed PRGs between TCGA-Glioma tumor samples and the normal brain samples from the Genome Tissue Expression (GTEx) were investigated. Besides, univariate and multivariate Cox regression analysis were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve and principal component analysis (PCA) was undertaken to assess the prognostic capacity of the signature. Gene set enrichment analyses (GSEA) and single sample GSEA (ssGSEA) were used to further understand the molecular mechanisms and the difference of immune microenvironment. External validation of two separate cohorts from the CGGA database was then performed. RESULTS: Caspase 3 (CASP3) and interleukin-18 (IL18) were identified as potential prognostic biomarkers. A novel prognostic model was constructed to predict diffuse glioma patients' overall survival (OS) time. Patients in high-risk subgroup had shorter survival than those with high-risk with P<0.0001. GSEA and ssGSEA showed the activation of immune-related pathways and the extensive infiltration of immune cells [such as cytotoxic T cells, dendritic cells (DC), natural killer T cell (NKT), induced regulatory T cells (iTreg), naturally occurring regulatory T cells (nTreg)] in high-risk subgroup. CONCLUSIONS: A novel two-PRGs prognostic signature based on gene expression was identified, which could predict diffuse glioma patients' OS time. Pyroptosis may be involved in the establishment of immune microenvironment in diffuse glioma.

Clinical and genomic landscape of hepatocellular carcinoma subtypes with various proportions of nonleukocyte stromal cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies and inflicts high mortality worldwide. The effect of tumor microenvironment components on HCC oncogenesis remains unknown. In particular, the nonleukocyte portion of the stromal fraction (SF) is poorly understood. METHODS: We comprehensively evaluated the proportional cell counts and gene expression data from The Cancer Genome Atlas (TCGA) to examine the contributions of cell components to the tumor microenvironment. Single-cell sequencing data from the Gene Expression Omnibus (GEO) were also analyzed to verify the association between the nonleukocyte SF and genes. We classified HCC using a hierarchical clustering method based on diversity of nonleukocyte SF-related gene expression among different components, and we used an appropriate GEO dataset to verify the clusters with a support vector machine (SVM) model. The prognosis of subtypes and their relationship with tumor microenvironmental cell proportions, clinicopathogenesis factors, and other indicators were evaluated. RESULTS: Based on linear regression, 711 genes related to nonleukocyte SF were selected from the TCGA dataset. We classified HCC into three subtypes using genes related to the nonleukocyte SF. Additionally, the GEO single-cell sequencing data confirmed the relationship between genes and the nonleukocyte SF. The tumor microenvironment of Type 2 contained the most significant mutually reinforcing interaction between the nonleukocyte SF and tumor cells. Meanwhile, Type 2 patients had the poorest prognosis and the most severe tumor-node-metastasis (TNM) stages, histological grades, etc. The analysis based on the GEO dataset verified the classification results with an SVM model. Type 2 was associated with worse clinicopathological characteristics, including tumor grading and staging, than the other types. In addition, the pathway analysis revealed that signals related to the SF and cell proliferation were significantly enhanced in Type 2 compared to the other group, which consisted of Types 1 and 3. CONCLUSION: The nonleukocyte SF in the tumor microenvironment contributed greatly to HCC oncogenesis. We can use these HCC classification criteria to stratify patients into subtypes for personalized treatment.

Photophysical properties and singlet oxygen generation of meso-iodinated free-base corroles.

In order to study the effect of meso-iodination of free-base corroles on their photophysical character, we designed and synthesized a series of free-base corrole derivatives F10-OH (iodine-free), F10-OH-I (mono-iodo) and F10-OH-2I (di-iodo), with different substitution patterns at the meso-position as candidates for photodynamic therapy (PDT). We employed several optical spectroscopic techniques, including time-resolved spectroscopy from a femtosecond to microsecond and singlet oxygen luminescence to study the properties of excited singlet and triplet states, as well as the singlet oxygen quantum yields. The sub-picosecond internal conversion, ∼1 ps intramolecular vibrational energy redistribution, tens of ps vibrational cooling, are similar across the three corroles. The addition of one (F10-OH-I) and two iodine (F10-OH-2I) atoms to the remote aryl ring of triarylcorroles induces a 4.6-fold and 6.2-fold decrease in fluorescence quantum yields Φ fl and a 2.2-fold and 4.9-fold increase in the time constant of intersystem crossing k ISC. In addition, a slight increase in intersystem crossing quantum yields Φ T was also observed from F10-OH to F10-OH-2I. It means the intersystem crossing is improved by the iodination, from F10-OH to F10-OH-2I, because of the heavy atom effect. However, the sample F10-OH-I, instead of F10-OH-2I, shows the highest singlet oxygen quantum yield Φ Δ.