Biological Characteristics and Predictive Model of Biopsy-Proven Acute Rejection (BPAR) After Kidney Transplantation: Evidences of Multi-Omics Analysis

Transcriptomics-based exploration of shared M1-type macrophage-related biomarker in acute kidney injury after kidney transplantation and acute rejection after kidney transplantation

BACKGROUND

Macrophage type 1 (M1) cells are associated with both acute kidney injury (AKI) during kidney transplantation and acute rejection (AR) after kidney transplantation. Our study explored M1-related biomarkers involved in both AKI and AR and their potential biological functions.

METHODS

Based on the Gene Expression Omnibus (GEO) database, the immune cell infiltration levels and differentially expressed genes were examined in AKI and AR in the kidney transplantation; M1-related genes shared in AKI and AR were identified using weighted gene co-expression analysis (WGCNA) system. Subsequently, protein-protein interaction (PPI) networks and machine learning methods to identify Hub genes and construct diagnostic models. Both AKI model and AR rat models were built to validate the expressions of Hub genes and test the injury phenotype, oxidative stress markers, and inflammatory factors. Finally, the transcription factor (TF)-Hub gene and micro-RNA (miRNA)-Hub gene regulatory networks were constructed based on identified Hub genes.

RESULTS

Out of 2167 differential expression genes (DEGs) in AKI and 2100 DEGs in AR, four M1-related Hub genes were obtained by PPI networks and machine learning methods, namely GBP2, TYROBP, CCR5, and TLR8. The calibration curves in the nomogram diagnostic model for these four Hub genes suggested the same predictive probability as an ideal model for AKI and AR after kidney transplantation (AUC values of the area under the ROC curve were all >0.7). The same observations were confirmed in ischemia reperfusion injury (IRI) and AR rat models by identifying common four Hub genes (GBP2, TYROBP, TLR8, and CCR5). Western blots showed that these four Hub genes were significantly different in rat models of IRI and AR (all p<0.05). Compared with the control group, IRI and AR groups showed aggravated histopathological damage and increased secretion of oxidative stress markers and inflammatory factors in rat kidneys (all p<0.05). Finally, TF-Hub and miRNA-Hub gene regulatory networks were constructed to provide a theoretical basis for the regulation of Hub genes.

CONCLUSION

We identified four macrophage M1-related Hub genes shared among AKI and AR after kidney transplantation. These genes may be considered for diagnosis of AKI and AR after kidney transplantation.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

BACKGROUND

Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.

RESULTS

Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.

CONCLUSION

A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs.

Methods

Data from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA-mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC.

Results

We identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment.

Conclusion

We successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.

TET1 is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Papillary Thyroid Cancer

To investigate the function of ten-eleven translocation 1 (TET1) and its underlying mechanism in papillary thyroid cancer (PTC). Using the RNA-Seq data based on GDC TCGA, we analyzed the gene expression pattern of TET1 in PTC. Immunohistochemistry was carried out to assess the TET1 protein level. Then, its diagnostic and prognostic functions were determined by various bioinformatics approaches. Enrichment analysis was performed to explore the potential pathways in which TET1 is mainly involved. Finally, the immune cell infiltration analysis was conducted and the association of TET1 mRNA expression with the expression levels of immune checkpoints, tumor mutation burden (TMB) score, microsatellite instability (MSI) score, and cancer stem cells (CSC) score was examined. TET1 expression was lower in PTC tissues compared with that in normal tissues (P < 0.01). Besides, TET1 had a certain value in diagnosing PTC, and low-TET1 mRNA expression led to favorable disease-specific survival (DSS) (P < 0.01). The enrichment analysis revealed autoimmune thyroid disease and cytokine-cytokine receptor interaction were the consistent pathways in which TET1 participated. TET1 was negatively correlated with the Stromal score and Immune score. The different proportions of immune cell subtypes were observed between high- and low-TET1 expression groups. Interestingly, TET1 mRNA expression was inversely related to the expression levels of immune checkpoints, and TMB, MSI, and CSC scores. TET1 might be a robust diagnostic and prognostic biomarker for PTC. TET1 affected the DSS of PTC patients possibly through the regulation of immune-related pathways and tumor immunity.

Integrated bioinformatics analysis of ferroptosis-related gene signature in inflammation and immunity in intervertebral disc degeneration

Ferroptosis has recently been shown to play a significant role in the progression of intervertebral disk degeneration (IDD), although the underlying mechanism is still unknown. The objective of this work was to use stringent bioinformatic techniques to clarify the crucial roles played by genes associated with ferroptosis in the emergence of IDD. For additional study, the microarray data pertinent to the IDD were acquired from the Gene Expression Omnibus database. The ferroptosis-related and IDD-related genes (FIDDRGs) were identified using a variety of bioinformatic techniques, which were also used to carry out function enrichment analysis, protein-protein correlation analysis, build the correlation regulatory network, and examine the potential connections between ferroptosis and immune abnormalities and inflammatory responses in IDD. A total of 16 FIDDRGs were eliminated for the further function enrichment analysis, and 10 hub FIDDRGs were chosen to build the correlation regulatory network. Hub FIDDRGs were shown to be highly associated with M2 macrophages and hub inflammatory response-related genes in IDD. When seen as a whole, our findings can give fresh perspectives on the mechanistic studies of ferroptosis in the emergence of IDD and new prospective targets for the therapeutic approaches.

PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma

Background

Cancer growth is significantly influenced by processes such as pyroptosis, apoptosis, and necroptosis that underlie PANoptosis, a proinflammatory programmed cell death. Several studies have examined the long non-coding RNAs (lncRNAs) associated with pancreatic adenocarcinoma (PAAD). However, the predictive value of lncRNAs related to PANoptosis for PAAD has not been established.

Methods

The Clinical Genome Atlas database was used to obtain the transcriptome 、clinical data and the corresponding mutation data of the patients with PAAD in this study. The least absolute shrinkage and selection operator regression analysis was employed to obtain prognosis-related lncRNAs for constructing a risk signature. According to the median risk score of the signature, patients with PAAD were grouped into low- and high-risk groups to further compare the survival prognosis of different risk groups. Time-dependent receiver operating characteristic curves, c-index analysis, nomograms, principal component analysis and univariate Cox and multivariate Cox regression were performed for the internal validation of the signature. In addition, enrichment analysis of different genes was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Lastly, differences in tumor mutation burden (TMB), immune function, tumor immune dysfunction and rejection (TIDE), and drug response were determined for the two risk groups.

Results

The signature was constructed with six PANoptosis-related lncRNAs (AC067817.2、LINC02004、AC243829.1、AC092171.5、AP005233.2、AC004687.1) that predicted the prognosis of the patients with PAAD. Survival curves showed that patients in the two risk groups had statistically significant differences in prognosis (P < 0.05), and multi-cox regression analysis identified risk score as an independent risk factor for PAAD prognosis, and internal validation of nomograms showed high confidence in the signature. GO and KEGG enrichment analysis showed functional and pathway differences between the high- and low-risk groups. TMB evaluation demonstrated that patients in the high-risk group had a higher frequency of mutations. The TIDE score indicated that the high-risk group had a lower risk of immunotherapy escape and better immunotherapy outcomes. Additionally, the two risk groups revealed significantly different responses to 11 anticancer drugs.

Conclusion

We identified a novel risk signature for PANoptosis-related lncRNAs, which is a standalone prognostic indicator for PAAD. The PANoptosis-related lncRNA risk signature may be relevant for immunotherapy and a therapeutic target for PAAD.

Identifying hub genes of sepsis-associated and hepatic encephalopathies based on bioinformatic analysis-focus on the two common encephalopathies of septic cirrhotic patients in ICU

BACKGROUND

In the ICU ward, septic cirrhotic patients are susceptible to suffering from sepsis-associated encephalopathy and/or hepatic encephalopathy, which are two common neurological complications in such patients. However, the mutual pathogenesis between sepsis-associated and hepatic encephalopathies remains unclear. We aimed to identify the mutual hub genes, explore effective diagnostic biomarkers and therapeutic targets for the two common encephalopathies and provide novel, promising insights into the clinical management of such septic cirrhotic patients.

METHODS

The precious human post-mortem cerebral tissues were deprived of the GSE135838, GSE57193, and GSE41919 datasets, downloaded from the Gene Expression Omnibus database. Furthermore, we identified differentially expressed genes and screened hub genes with weighted gene co-expression network analysis. The hub genes were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway functional enrichment analyses, and protein-protein interaction networks were constructed. Receiver operating characteristic curves and correlation analyses were set up for the hub genes. Finally, we explored principal and common signaling pathways by using Gene Set Enrichment Analysis and the association between the hub genes and immune cell subtype distribution by using CIBERSORT algorithm.

RESULTS

We identified seven hub genes-GPR4, SOCS3, BAG3, ZFP36, CDKN1A, ADAMTS9, and GADD45B-by using differentially expressed gene analysis and weighted gene co-expression network analysis method. The AUCs of these genes were all greater than 0.7 in the receiver operating characteristic curves analysis. The Gene Set Enrichment Analysis results demonstrated that mutual signaling pathways were mainly enriched in hypoxia and inflammatory response. CIBERSORT indicated that these seven hub genes were closely related to innate and adaptive immune cells.

CONCLUSIONS

We identified seven hub genes with promising diagnostic value and therapeutic targets in septic cirrhotic patients with sepsis-associated encephalopathy and/or hepatic encephalopathy. Hypoxia, inflammatory, and immunoreaction responses may share the common downstream pathways of the two common encephalopathies, for which earlier recognition and timely intervention are crucial for management of such septic cirrhotic patients in ICU.

Prognosis prediction based on methionine metabolism genes signature in gliomas

BACKGROUND

Glioma cells have increased intake and metabolism of methionine, which can be monitored with 11 C-L-methionine. However, a short half-life of 11 C (~ 20 min) limits its application in clinical practice. It is necessary to develop a methionine metabolism genes-based prediction model for a more convenient prediction of glioma survival.

METHODS

We evaluated the patterns of 29 methionine metabolism genes in glioma from the Cancer Genome Atlas (TCGA). A risk model was established using Lasso regression analysis and Cox regression. The reliability of the prognostic model was validated in derivation and validation cohorts (Chinese Glioma Genome Atlas; CGGA). GO, KEGG, GSEA and ESTIMATE analyses were performed for biological functions and immune characterization.

RESULTS

Our results showed that a majority of the methionine metabolism genes (25 genes) were involved in the overall survival of glioma (logrank p and Cox p < 0.05). A 7-methionine metabolism prognostic signature was significantly related to a poor clinical prognosis and overall survival of glioma patients (C-index = 0.83). Functional analysis revealed that the risk model was correlated with immune responses and with epithelial-mesenchymal transition. Furthermore, the nomogram integrating the signature of methionine metabolism genes manifested a strong prognostic ability in the training and validation groups.

CONCLUSIONS

The current model had the potential to improve the understanding of methionine metabolism in gliomas and contributed to the development of precise treatment for glioma patients, showing a promising application in clinical practice.

Bioinformatics identification of a T-cell-related signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single-cell RNA sequencing data were downloaded from the GSE149614 dataset. The T-cell-related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T-cell-related markers in the Cancer Genome Atlas cohort with 1-, 2- and 3-year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS-based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8+ T cells and NK cells. The response rate was significantly higher in patients with low-risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS-based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5 years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy.

Integrated bioinformatics and validation reveal SOX12 as potential biomarker in colon adenocarcinoma based on an immune infiltration-related ceRNA network

PURPOSE

The primary objective of this study was to construct competing endogenous RNA (ceRNA) networks and evaluate the prognostic significance of tumor-infiltrating immune cells (TIICs) and key biomarkers within the ceRNA networks in colon adenocarcinoma (COAD) patients.

METHODS

Comprehensive bioinformatics tools were used to screen differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs) related to COAD, leading to the creation of ceRNA networks. The CIBERSORT technique was employed to assess the significance of TIICs in COAD, and an immune-related prognosis prediction model was subsequently developed. Co-expression analyses were conducted to determine the relationship between key genes in ceRNA networks and immunologically significant TIICs. The study also utilized 5 GEO datasets and web-based databases to externally validate the findings.

RESULTS

The study revealed a statistically significant relationship between key hub genes and immune cells, as determined through co-expression analysis. Two hub regulators (SOX12 and H19) demonstrated significant prognostic value in the ceRNA-related prognostic model, and their elevated expression levels were verified across multiple CRC cell lines. Additionally, the knockdown of SOX12 led to a suppression of proliferation, migration, and invasion in colon cancer cells.

CONCLUSION

Through the construction of ceRNA networks and evaluation of TIICs, the study successfully established two risk score models and nomograms. These models serve as valuable tools for understanding the molecular processes and predicting the prognosis of COAD patients. Further validation of hub regulators SOX12 and H19 substantiates their potential role as key biomarkers in COAD.

Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity

Lung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.

Construction and validation of a metabolism-associated gene signature for predicting the prognosis, immune landscape, and drug sensitivity in bladder cancer

Tumor Metabolism is strongly correlated with prognosis. Nevertheless, the prognostic and therapeutic value of metabolic-associated genes in BCa patients has not been fully elucidated. First, in this study, metabolism-related differential expressed genes DEGs with prognostic value in BCa were determined. Through the consensus clustering algorithm, we identified two molecular clusters with significantly different clinicopathological features and survival prognosis. Next, a novel metabolism-related prognostic model was established. Its reliable predictive performance in BCa was verified by multiple external datasets. Multivariate Cox analysis exhibited that risk score were independent prognostic factors. Interestingly, GSEA enrichment analysis of GO, KEGG, and Hallmark gene sets showed that the biological processes and pathways associated with ECM and collagen binding in the high-risk group were significantly enriched. Notely, the model was also significantly correlated with drug sensitivity, immune cell infiltration, and immunotherapy efficacy prediction by the wilcox rank test and chi-square test. Based on the 7 immune infiltration algorithm, we found that Neutrophils, Myeloid dendritic cells, M2 macrophages, Cancer-associated fibroblasts, etc., were more concentrated in the high-risk group. Additionally, in the IMvigor210, GSE111636, GSE176307, or our Truce01 (registration number NCT04730219) cohorts, the expression levels of multiple model genes were significantly correlated with objective responses to anti-PD-1/anti-PD-L1 immunotherapy. Finally, the expression of interested model genes were verified in 10 pairs of BCa tissues and para-carcinoma tissues by the HPA and real-time fluorescent quantitative PCR. Altogether, the signature established and validated by us has high predictive power for the prognosis, immunotherapy responsiveness, and chemotherapy sensitivity of BCa.

High expression of CCDC69 is correlated with immunotherapy response and protective effects on breast cancer

BACKGROUND

As a molecule controlling the assembly of central spindles and recruitment of midzone component, coiled-coil domain-containing protein 69 (CCDC69) plays an important role in multiple cancers. Currently, the relationships between CCDC69 and immune infiltration or immunotherapy in breast cancer remain unclear.

METHODS

The expression and prognostic significance of CCDC69 in breast cancer were comprehensively analyzed by quantitative real-time PCR, immunohistochemical staining and various databases. The data source of differentially expressed genes, gene set enrichment analysis, and immune cell infiltration analysis came from The Cancer Genome Atlas (TCGA) database. Single-cell analysis based on IMMUcan database was used. The protein-protein interaction network was developed applying STRING, Cytoscape, CytoHubba, and GeneMANIA. TISIDB was employed in analyzing the CCDC69 co-expressed immune related genes. The correlations between CCDC69 and immunotherapy or immune-related scores were analyzed by CAMOIP and TISMO. Ctr-db was also used to conduct drug sensitivity analysis.

RESULTS

The mRNA of CCDC69 was downregulated in breast cancer tissues compared with normal tissues. Higher CCDC69 expression was associated with a better breast cancer prognosis. Enrichment analysis showed that the co-expression genes of CCDC69 were mainly related to immune-related pathways. The expression of CCDC69 was found to be positively correlated with multiple tumor-suppression immune infiltration cells, especially T cells and dendritic cells. Meanwhile, high CCDC69 expression can predict better immunotherapy responses when compared with low CCDC69 expression. After the interferon-gamma treatment, the CCDC69 expression was elevated in vitro. CCDC69 expression was a reliable predictor for the response status of two therapeutic strategies in breast cancer.

CONCLUSIONS

Our research revealed the clinical significance of CCDC69 in breast cancer and validated the critical roles of CCDC69 in the tumor immune infiltration and immunotherapy responses.

Nomogram for predicting survival in patients with mucinous breast cancer undergoing chemotherapy and surgery: a population-based study

BACKGROUND

The prognosis of patients with mucinous breast cancer (MuBC) is affected by several factors, but the low incidence of MuBC makes it difficult to conduct extensive and in-depth studies. This study was designed to establish a prognostic model and verify its accuracy in patients with MuBC after chemotherapy and surgery to help develop personalized treatment strategies.

MATERIALS AND METHODS

Patients with MuBC who underwent chemotherapy and surgery from 2004 to 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic factors of patients with MuBC were investigated using a Cox proportional hazards regression analysis. Based on the identified factors, a nomogram was constructed to forecast the overall survival (OS) of patients at 3, 5, and 10 years. Internal (from SEER) and external (from Yunnan Cancer Center, YNCC) verification queues were used to verify the nomogram and demonstrate the predictive capacity of this model.

RESULTS

The study comprised 1668 MuBC patients from the SEER database and 107 from the YNCC. The nomogram included four characteristics: age, anatomical stage, surgical method, and radiotherapy. The concordance indices in the training, internal verification, and external verification queues were 0.680, 0.768, and 0.864, respectively. The calibration curves for the nomogram showed excellent agreement between the predictions and observations. This nomogram has good clinical application value according to the decision curve analysis.

CONCLUSIONS

The prognosis of patients with MuBC who have undergone chemotherapy and surgery can be forecasted using this nomogram, which would be beneficial to help create individualized treatment plans for the affected patients.

Immunologic response and seroconversion following third-dose COVID-19 vaccination in solid organ transplant recipients: A meta-analysis

INTRODUCTION

The immunogenicity and efficacy of COVID-19 vaccination varied by demographic, including solid organ transplant recipients on immunosuppressive therapy.

AIM

This purpose of this study is to assess seropositivity and seroconversion in solid-organ transplant recipients before and after third-dose COVID-19 vaccination.

METHODS

This study is a systematic review and meta-analysis performed using PRISMA guidelines. To analyze clinical and cohort studies reporting immunologic response and seroconversion third-dose vaccination, a systematic search was performed using electronic databases (PubMed, Scopus, Cochrane, Directory of Open Access Journal (DOAJ), and Clinicaltrials.gov).

RESULT

There were 18 full-text papers that could be analyzed qualitatively and quantitatively. After the third vaccination, the pooled rate seropositivity was 67.00% (95% CI 59.511; 74.047, I2 = 93.82%), and the pooled rate seroconversion was 52.51% (95% CI 44.03; 60.91, I2 = 92.15%). The pooled rate of seroconversion after the mRNA-based booster was 52.380% (95% CI 40.988; 63.649, I2 = 94.35%), and after the viral-vector-based booster was 42.478% (95% CI 35.222; 49.900, I2 = 0.00%).

CONCLUSION

Based on the analysis of immunologic responses and seroconversion findings, the third-dose vaccination of solid organ transplant recipients is an effective method in establishing better immunity against COVID-19.

Comprehensive analysis and validation of SNX7 as a novel biomarker for the diagnosis, prognosis, and prediction of chemotherapy and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Studies have demonstrated that Sorting nexin 7 (SNX7) functions as an anti-apoptotic protein in liver tissue and plays a crucial role in the survival of hepatocytes during early embryonic development. However, its diagnostic and prognostic value as well as the predictive value of chemotherapy and immunotherapy have not been reported in hepatocellular carcinoma (HCC).

METHODS

SNX7 mRNA expression and its diagnostic efficacy were examined in GEO datasets, and the findings were further confirmed in TCGA, ICGC cohorts, and cell lines. The protein level of SNX7 was determined using CPTAC and HPA databases, and the results were validated through immunohistochemistry (IHC). Survival analyses were performed in TCGA and ICGC cohorts, and the results were subsequently validated via Kaplan-Meier Plotter. The response to chemotherapy and immunotherapy was predicted via GDSC dataset and TIDE algorithm, respectively. R packages were employed to explore the relationship between SNX7 expression and immune infiltration, m6A modification, as well as the functional enrichment of differentially expressed genes (DEGs).

RESULTS

The expression of SNX7 at both mRNA and protein levels was significantly upregulated in HCC tissues. SNX7 exhibited superior diagnostic efficacy compared to AFP alone for HCC detection, and combining it with AFP improved the diagnostic accuracy for HCC. High SNX7 was associated with unfavorable outcomes, including poor overall survival, disease-specific survival, progression-free survival, and advanced pathological stage, in patients with HCC, and SNX7 was identified as an independent risk factor for HCC. Moreover, elevated SNX7 expression was positively correlated with increased sensitivity to various chemotherapy drugs, including sorafenib, while it was associated with resistance to immunotherapy in HCC patients. Correlation analysis revealed a relationship between SNX7 and multiple m6A-related genes and various immune cells. Finally, enrichment analysis demonstrated strong associations of SNX7 with critical biological processes, such as cell cycle regulation, cellular senescence, cell adhesion, DNA replication, and mismatch repair pathway in HCC.

CONCLUSIONS

Our study highlights the association of SNX7 with the immune microenvironment and its potential influence on HCC progression. SNX7 emerges as a promising novel biomarker for the diagnosis, prognosis, and prediction of response to chemotherapy and immunotherapy in patients with HCC.

Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients

BACKGROUND

Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients.

METHODS

We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma.

RESULTS

We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group.

CONCLUSIONS

Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.

Comprehensive analyses of fatty acid metabolism-related lncRNA for ovarian cancer patients

Ovarian cancer (OC) is a disease with difficult early diagnosis and treatment and poor prognosis. OC data profiles were downloaded from The Cancer Genome Atlas. Eight key fatty acid metabolism-related long non-coding RNAs (lncRNAs) were finally screened for building a risk scoring model by univariate/ multifactor and least absolute shrinkage and selection operator (LASSO) Cox regression. To make this risk scoring model more applicable to clinical work, we established a nomogram containing the clinical characteristics of OC patients after confirming that the model has good reliability and validity and the ability to distinguish patient prognosis. To further explore how these key lncRNAs are involved in OC progression, we explored their relationship with LUAD immune signatures and tumor drug resistance. The structure shows that the risk scoring model established based on these 8 fatty acid metabolism-related lncRNAs has good reliability and validity and can better predict the prognosis of patients with different risks of OC, and LINC00861in these key RNAs may be a hub gene that affects the progression of OC and closely related to the sensitivity of current OC chemotherapy drugs. In addition, combined with immune signature analysis, we found that patients in the high-risk group are in a state of immunosuppression, and Tfh cells may play an important role in it. We innovatively established a prognostic prediction model with excellent reliability and validity from the perspective of OC fatty acid metabolism reprogramming and lncRNA regulation and found new molecular/cellular targets for future OC treatment.

Identification of Renal Transplantation Rejection Biomarkers in Blood Using the Systems Biology Approach

Background

Renal transplantation plays an essential role in the quality of life of patients with end-stage renal disease. At least 12% of the renal patients receiving transplantations show graft rejection. One of the methods used to diagnose renal transplantation rejection is renal allograft biopsy. This procedure is associated with some risks such as bleeding and arteriovenous fistula formation. In this study, we applied a bioinformatics approach to identify serum markers for graft rejection in patients receiving a renal transplantation.

Methods

Transcriptomic data were first retrieved from the blood of renal transplantation rejection patients using the GEO database. The data were then used to construct the protein-protein interaction and gene regulatory networks using Cytoscape software. Next, network analysis was performed to identify hub-bottlenecks, and key blood markers involved in renal graft rejection. Lastly, the gene ontology and functional pathways related to hub-bottlenecks were detected using PANTHER and DAVID servers.

Results

In PPIN and GRN, SYNCRIP, SQSTM1, GRAMD1A, FAM104A, ND2, TPGS2, ZNF652, RORA, and MALAT1 were the identified critical genes. In GRN, miR-155, miR17, miR146b, miR-200 family, and GATA2 were the factors that regulated critical genes. The MAPK, neurotrophin, and TNF signaling pathways, IL-17, and human cytomegalovirus infection, human papillomavirus infection, and shigellosis were identified as significant pathways involved in graft rejection.

Concusion

The above-mentioned genes can be used as diagnostic and therapeutic serum markers of transplantation rejection in renal patients. The newly predicted biomarkers and pathways require further studies.

Comprehensive analysis of the immunogenic cell death-related signature for predicting prognosis and immunotherapy efficiency in patients with lung adenocarcinoma

BACKGROUND

Although immunotherapy has been considered as a potent strategy for lung adenocarcinoma (LUAD), only a small part of patients was served as potentially clinical benefiters. Immunogenic cell death (ICD), a type of regulated cell death (RCD), which enable to reshape the tumor immune microenvironment and contribute to the immunotherapy efficiency. Developing a novel ICD-based signature may be a potential strategy to differentiate prognosis of patients with LUAD and predict efficacy of immunotherapy.

METHODS

In this study, 34 ICD-related genes (ICDRGs) were identified and analyzed in LUAD samples from the Cancer Genome Atlas (TCGA). 572 patients with LUAD were divided into two distinct clusters according to ICDRGs expression levels. Patients were subsequently classified into two distinct gene subtypes based on differentially expressed genes (DEGs) analyzed between two ICD-related clusters. We further developed and validated a novel ICD-related score (ICDRS) followed by comprehensive investigation about the landscape of the prognosis, immune-based features, immunotherapautic responses and sensitivity of target drugs in patients with LUAD.

RESULTS

After confirming transcriptomic aberrations and appraising prognostic value of ICDRGs, two ICD-associated subtypes were initially determined by consensus clustering in accordance with differentially expressional levels of ICDRGs. It was shown that patients in the ICD high-subtype possessed the superior clinical prognosis, abundant immune cell infiltration and higher involvement in immune-related signaling compared with the ICD low-subtype. A signature of ICD-related score (ICDRS) was further established and validated, which was served as an independent prognostic indicator for LUAD patients. These comprehensive results revealed that the high-score patients represented better clinical prognosis, higher immune infiltration-related characteristics, stronger expression of immune checkpoints, and better response to immune checkpoint inhibitor therapy and multiple targeted drugs. To further verify our analysis, we selected TLR4 as the representative of ICDRGs and evaluated its expression on the lung normal cells and cancer cells in vitro. Then, relative animal experiments were performed in vivo, with results of that the stimulation of TLR4 suppressed the growth of lung cancer.

CONCLUSIONS

In conclusion, our comprehensive analysis of ICDRGs in LUAD demonstrated their function in serving as a biomarker of predicting prognosis and clinical effects of immunotherapy and targeted drugs, which is meaningful to improve our understanding of ICDRGs and brought inspirations about evaluating prognosis and developing effective therapeutic strategies to patients with LUAD.

Knockdown of RBM15 inhibits tumor progression and the JAK-STAT signaling pathway in cervical cancer

BACKGROUND

RNA binding motif protein 15 (RBM15), a writer of N6-methyladenosine (m6A) methylation, contributes significantly to the development of various tumors. However, the function of RBM15 in cervical cancer (CC) has not been determined.

METHODS

Based on the GSE9750, GSE63514, and m6A datasets, m6A-related differentially expressed genes (DEGs) were screened out. The hub genes were identified by generating a Protein-Protein Interaction (PPI) network. RT-qPCR was conducted to assess the mRNA expression of hub genes. CCK8, scratch wound healing, and transwell assays were utilized to examine the influence of RBM15 on HeLa and SiHa cells. Tumor xenograft models were used to assess the effects of RBM15 on tumorigenesis. A mechanistic analysis of RBM15 in CC tumors was conducted using the GeneCards and Coxpresdb databases, followed by a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and the pathway-related genes were subsequently validated using Western blotting.

RESULTS

Five DEGs were screened, including WTAP, RBM15, CBLL1, and YTHDC2. Among them, WTAP, RBM15, CBLL1, and YTHDC2 were hub genes and can be used as biomarkers for CC. RBM15 expression was considerably increased, while WTAP, CBLL1, and YTHDC2 were significantly downregulated. Knockdown of RBM15 significantly suppressed the proliferation, invasion, and migration of CC cells and tumorigenesis. Moreover, knockdown of RBM15 significantly reduced the expression levels of proteins related to the JAK-STAT pathway.

CONCLUSIONS

Knockdown of RBM15 inhibited the progression of CC cells, which probably by inhibiting the JAK-STAT pathway pathway.

Pancancer analysis of oncogenic BARX2 identifying its prognostic value and immunological function in liver hepatocellular carcinoma

The transcription factor BarH-like homeobox 2 (BARX2), a member of the Bar-like homeobox gene family, is involved in cell proliferation, differentiation, immune responses and tumorigenesis. However, the potential role of BARX2 in the development of liver hepatocellular carcinoma (LIHC) remains unclear. Therefore, we aimed to study the biological role of BARX2 in hepatocellular carcinoma. Through the UALCAN, GTEx PORTAL, TIMER 2.0, LinkedOmics, SMART, MethSurv, Metascape, GSEA and STRING public databases, the BARX2 mRNA level, prognostic value, coexpressed genes, associated differentially expressed genes, DNA methylation and functional enrichment of LIHC patients were studied. The relationships between BARX2 expression and various clinical or genetic parameters of LIHC patients were determined using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and BEAT LIHC databases. In addition, the biological function of BARX2 in LIHC was studied in vitro. Through large-scale data mining, our study showed that BARX2 was differentially expressed between different normal and tumour tissues.BARX2 expression in LIHC tissues was significantly lower than that in corresponding controls, especially in patients with T2-4 stage disease. In patients with LIHC, overexpression of BARX2 was an independent poor prognostic factor associated with poor cytogenetic risk and gene mutations. Genomic hypermethylation of the BARX2 gene was associated with upregulated BARX2 expression and poor overall survival (OS) in LIHC. Functional enrichment analysis showed that BARX2 had an immunomodulatory role and was involved in the inflammatory response in LIHC occurrence. In conclusion, the oncogene BARX2 may serve as a new biomarker and prognostic factor for patients with LIHC. The immunomodulatory function of BARX2 deserves further validation in LIHC.