Immune infiltration in renal cell carcinoma

Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment

This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.

Identification of prognostic markers by weighted gene co-expression network analysis in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the fatal tumors and is associated with a poor prognosis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify the proportions of 22 types of immune cells. Weighted gene co-expression network analysis (WGCNA) was established from the GSE37745 data, and key modules correlating most with CD8⁺ T cell infiltration were determined. Genes that manifested a high module connectivity in the key module were identified as hub genes. Three bioinformatics online databases were used to evaluate hub gene expression levels in tumor and normal tissues. Finally, survival analysis was conducted for these hub genes. In this study, we chose four hub genes (AURKB, CDC20, TPX2 and KIF2C) based on the comprehensive bioinformatics analyses. All hub genes were overexpressed in tumor tissue, and high expression of AURKB, CDC20, TPX2, and KIF2C correlated with the poor prognosis of these patients. In vitro experiments confirmed that CDC20 knockdown inhibited cell proliferation and growth. The above results indicated that AURKB, CDC20, TPX2, and KIF2C are potential CD8⁺ T cell infiltration-related biomarkers and therapeutic targets.

Prognostic Impact of MITD1 and Associates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most malignant diseases with poor survival rate over the world. The tumor microenvironment (TME) is highly related to the oncogenesis, development, and prognosis of KIRC. Thus, making the identification of KIRC biomarkers and immune infiltrates critically important. Microtubule Interacting and Trafficking Domain containing 1(MITD1) was reported to participate in cytokinesis of cell division. In the present study, multiple bioinformatics tools and databases were applied to investigate the expression level and clinical value of MITD1 in KIRC. We found that the expression of MITD1 was significantly increased in KIRC tissues. Further, the KIRC patients with high MITD1 levels showed a worse overall survival (OS) rate and disease free survival (DFS) rate. Otherwise, we found a significant correlation MITD1 expression and the abundance of CD8+ T cells. Functional enrichment analyses revealed that immune response and cytokine-cytokine receptor are very critical signaling pathways which associated with MITD1 in KIRC. In conclusion, our findings indicated that MITD1 may be a potential biomarker and associated with immune infiltration in KIRC.

Identification and Validation of PIK3CA as a Marker Associated with Prognosis and Immune Infiltration in Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is the most prevalent renal malignancy. The therapeutic strategies for advanced KIRC are very few, with only sunitinib being widely approved. Mutations in the PIK3CA gene can affect tumor cell proliferation, metastasis, and patients' survival.

Methods

Bioinformatics analysis was performed to explore the expression and clinical significance of PIK3CA in KIRC. Moreover, qRT-PCR was conducted to verify the result.

Results

Subgroup analyses of KIRC tissue based on gender, tumor grade, and cancer stage indicated downregulation of PIK3CA mRNA expression. The KIRC patients with high PIK3CA expression indicated a better overall survival, progression-free survival, and disease-free survival. A predictive nomogram was constructed and demonstrated that the calibration plots for the 3-year and 5-year OS rates were predicted relatively well compared with an ideal model in the TCGA KIRC cohort. The validation study revealed that downregulation of PIK3CA in KIRC tissues and low PIK3CA expression had a poor overall survival with an AUC of 0.775 in the ROC curve. Moreover, Cox regression analysis revealed that PIK3CA expression and clinical stage were independent factors affecting the prognosis of KIRC patients. PIK3CA expression was found to be significantly associated with the abundance of immune cells and immune biomarker sets. PIK3CA and associated genes were found to be mainly associated with immune response and the JAK-STAT signaling pathway.

Conclusion

We identified PIK3CA as a potential biomarker for prognosis correlated with immune infiltrates in KIRC. Further studies should focus on the functions of PIK3CA in KIRC carcinogenesis.

Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.

Method

In this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan-Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.

Results

A four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.

Conclusion

We constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods

Colorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to the malignant progression of CRC (metastasis, p = 0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein-protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine-cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in two independent cohorts (GSE17538 and GSE161158). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As the kidney is an iron metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and important.

METHODS

Prognostic ferroptosis-related differentially expressed genes (DEGs) were identified from the KIRC cohort in the cancer genome atlas (TCGA) database, from which a prognostic signature was established using Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned a calculated signature-correlated risk score and categorized to be either in the high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses based on overall survival (OS) in both cohorts. Finally, risk-related DEGs were identified in both cohorts and subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration.

RESULTS

Among 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature with CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk groups, which were visually distributed in two sets and had positive-risk-related mortality. The K-M survival and the ROC curves validated that the signature has prognostic value with P < 0.05 and area under the curve > 0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched terms in GO and KEGG not only showed a high iron correlation but also, interestingly, immune relevance of 3 immune cells (macrophages, mast cells and regulatory T cells) and 1 immune-related function (antigen processing cell co-stimulation).

CONCLUSION

We established a novel 12 ferroptosis-related-gene signature that was proven to be an independent prognostic predictor for OS and inferred to be related to tumour immunity in ccRCC; however, the underlying mechanism is still poorly characterized and needs further exploration.

Identification of the Prognosis Value and Potential Mechanism of Immune Checkpoints in Renal Clear Cell Carcinoma Microenvironment

Background

Kidney Renal Clear Cell Carcinoma (KIRC) is one of the most prevalent types of cancer worldwide. KIRC has a poor prognosis and, to date, immunotherapy based on immune checkpoints is the most promising treatment. However, the role of immune checkpoints in KIRC remains ambiguous.

Methods

Bioinformatics analyses and qRT-PCR were performed to explore and further confirm the prognostic value of immune checkpoint genes and their correlation with immune infiltration in KIRC samples.

Results

The expression of the immune checkpoint genes CD274, PDCD1LG2, HAVCR2, CTLA4, TIGFT, LAG3, and PDCD1 was upregulated in KIRC tissues. These genes were involved in the activation of the apoptosis pathway in KIRC. Low expression of CD274 and HAVCR2 and high expression of CTLA4 were associated with poor overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) of KIRC patients. The univariate and multivariate analyses revealed that CTLA4, HAVCR2, age, pTNM stage, and tumor grade were independent factors affecting the prognosis of KIRC patients. A predictive nomogram demonstrated that the calibration plots for the 3‐year and 5‐year OS probabilities showed good agreement compared to the actual OS of KIRC patients. The expression of CTLA4 and HAVCR2 were positively associated with immune cell infiltration, immune biomarkers, chemokines, and chemokine receptors. Moreover, miR-20b-5p was identified as a potential miRNA target of CTLA4 in KIRC.

Conclusion

Our study clarified the prognostic value of several immune checkpoint regulators in KIRC, revealing a CTLA4/miR-20b-5p axis in the control of immune cell infiltration in the tumor microenvironment.

Identification and Exploration of Novel Macrophage M2-Related Biomarkers and Potential Therapeutic Agents in Endometriosis

Endometriosis (EM) is a chronic neuroinflammatory disorder that is associated with pain and infertility that affects ∼10% of reproductive-age women. The pathophysiology and etiology of EM remain poorly understood, and diagnostic delays are common. Exploration of the underlying molecular mechanism, as well as novel diagnostic biomarkers and therapeutic targets, is urgently needed. Inflammation is known to play a key role in the development of lesions, which are a defining feature of the disorder. In our research, the CIBERSORT and WGCNA algorithms were used to establish a weighted gene co-expression network and to identify macrophage-related hub genes using data downloaded from the GEO database (GSE11691, 7305). The analysis identified 1,157 differentially expressed genes (DEGs) in EM lesions, of which five were identified as being related to M2 macrophages and were validated as differentially expressed by qRT-PCR and immunohistochemistry (IHC). Of these putative novel biomarker genes, bridging integrator 2 (BIN2), chemokine receptor 5 (CCR5), and macrophage mannose receptor 1 (MRC1) were upregulated, while spleen tyrosine kinase (SYK) and metalloproteinase 12 (ADAM12) were downregulated in ectopic endometria vs. normal endometria. Meanwhile, 23 potentially therapeutic small molecules for EM were obtained from the cMAP database, among which topiramate, isoflupredone, adiphenine, dexverapamil, MS-275, and celastrol were the top six molecules with the highest absolute enrichment values. This is our first attempt to use the CIBERSORT and WGCNA algorithms for the identification of novel Mϕ2 macrophage-related biomarkers of EM. Our findings provide novel insights into the impact of immune cells on the etiology of EM; nevertheless, further investigation of these key genes and therapeutic drugs is needed to validate their effects on EM.

Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

Background

Hepatocellular carcinoma (HCC) is often caused by chronic liver infection or inflammation. Searching for potential immunotherapy targets will aid the early diagnosis and treatment of HCC.

Methods

Firstly, detailed HCC data were downloaded from The Cancer Genome Atlas database. GDCRNATools was used for the comprehensive analysis of RNA sequencing data. Subsequently, the CIBERSORT package was used to estimate infiltration scores of 22 types of immune cells in complex samples. Furthermore, hub genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. In addition, multiple databases were used to validate the expression of hub gene in the tumor tissue. Finally, prognostic, diagnostic and immunohistochemical analysis of key hub genes was performed.

Results

In the present study, 9 hub genes were identified using WGCNA and PPI network analysis. Furthermore, the expression levels of 9 genes were positively correlated with the infiltration levels of CD8-positive T (CD8⁺ T) cells. In multiple dataset validations, the expression levels of CCL5, CXCR6, CD3E, and LCK were decreased in cancer tissues. In addition, survival analysis revealed that patients with LCK low expression had a poor survival prognosis (P < 0.05). Immunohistochemistry results demonstrated that CCL5, CD3E and LCK were expressed at low levels in HCC cancer tissues.

Conclusion

The identification of CCL5, CXCR6, CD3E and LCK may be helpful in the development of early diagnosis and therapy of HCC. LCK may be a potential prognostic biomarker for immunotherapy for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13000-021-01118-y.

Development and validation of m6A regulators' prognostic significance for endometrial cancer

BACKGROUND

Endometrial cancer (EC) is the sixth most common cancer in women globally. It has been found that the expression levels of m6A regulators can be potentially used for prognostic stratification in some cancers, but the role of m6A regulators in EC prognosis remains unclear.

METHODS

The data of 584 EC samples were downloaded from The Cancer Genome Atlas and the mRNA expression profiles of 20 m6A regulators were analyzed, followed by functional enrichment analysis, immune infiltration analysis, and least absolute shrinkage and selection operator method-COX regression analysis.

RESULTS

The mRNA expression levels of 20 m6A regulators were significantly different between cancer samples across different grades. The 548 EC samples could be clearly divided into 2 clusters. Kaplan-Meier survival analysis proved that these two groups had highly different overall survival probabilities. Besides, the univariate regression analysis further reserved eight genes related to overall survival from the 20 m6A regulators. We established a prognostic signature including two genes, that is, IGF2BP1 and YTHDF3, that showed a strong ability for stratifying prognostically different EC patients. We identified 3239 differentially expressed genes between the high- and low-risk groups, involving in multiple biological processes and signaling pathways. Meanwhile, 6 differentially infiltrated immune cell types between the high- and low-risk groups could effectively distinguish the high- and low-risk EC groups. The expressions of immune checkpoints were different between high- and low-risk EC patients.

CONCLUSION

We first report the prognostic role of m6A regulators in EC, which should contribute to a better understanding of the underlying mechanisms of EC pathogenesis and progression.

Construction and Comprehensive Prognostic Analysis of a lncRNA-miRNA-mRNA Regulatory Network and Tumor Immune Cell Infiltration in Colorectal Cancer

Colorectal cancer (CRC) is a malignant tumor with high morbidity and mortality worldwide. Recent studies have shown that long noncoding RNAs (lncRNAs) play an important role in almost all human tumors, including CRC. Competitive endogenous RNA (ceRNA) regulatory networks have become hot topics in cancer research. Tumor-infiltrating immune cells (TICs) have also been reported to be closely related to the survival and prognosis of CRC patients. In this study, we used the lncRNA–miRNA–mRNA regulatory network combined with tumor immune cell infiltration to predict the survival and prognosis of 598 CRC patients. First, we downloaded the lncRNA, mRNA, and miRNA transcriptome data of CRC patients from The Cancer Genome Atlas (TCGA) database and identified differentially expressed genes through “limma” package of R software. The ceRNA regulatory network was established by using the “GDCRNATools” R package. Then, univariate Cox analysis and least absolute shrinkage and selection operator analysis were performed to identify the optimal prognostic network nodes, including SRPX, UST, H19, SNHG7, hsa-miR-29b-3p, and TTYH3. Next, we analyzed the differences in 22 types of TICs between 58 normal subjects and 206 CRC patients and included memory CD4 T cells, dendritic cells and neutrophils in the construction of a prognostic model. Finally, we identified the relationship between the ceRNA prognostic model and the infiltrating immune cell prognostic model. In conclusion, we constructed two prognostic models that provide insights on the prognosis and treatment strategy of CRC.

CYP2J2 Is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Cytochrome P450 family 2 subfamily J member 2 (CYP2J2), a member of the monooxygenase cytochrome P450 (CYP) family and the only member of the human CYP2J subfamily, has many functions, including regulation of oxidative stress, inflammation, apoptosis, and immune responses. However, its role in cancer development has not been clearly elucidated. In this study, expression levels of CYP2J2 in various cancer types were determined using the Oncomine, the Gene Expression Profiling Interactive Analysis (GEIPA), DriverDBv3, UALCAN, and Tumor Immune Estimation Resource (TIMER) databases. The prognostic value of CYP2J2 for KIRC was analyzed using GEPIA, UALCAN, OSkirc, and DriverDBv3 databases. We evaluated the expression levels of CYP2J2 transcript, protein, and promoter methylation at different clinical characteristics in KIRC through the UALCAN database. Simultaneously, CYP2J2 network-related functions were evaluated using the GeneMANIA interactive tool while the biological processes involved in CYP2J2 and its interactive genes were investigated through Metascape and FunRich. Then, we used TIMER to determine the correlation between CYP2J2 expression levels and immune infiltration levels in KIRC. In KIRC, the CYP2J2 gene, RNA, and protein were found to be overexpressed. However, the methylation level of CYP2J2 promoter in KIRC was lower than in normal tissues. Surprisingly, elevated expression levels of CYP2J2 exhibited better prognostic outcomes in KIRC. Evaluation of protein-protein interaction networks and biological processes revealed that CYP2J2 was principally involved in immune responses, apoptosis, and other metabolic processes. Moreover, we found that the expression levels of CYP2J2 were positively correlated with infiltration levels of B cells, CD8 + T cells, neutrophils, and dendritic cells in KIRC. Therefore, we speculated that the overexpression of CYP2J2 prolonged the survival outcome of KIRC patients, which may be related to the change of tumor immune microenvironment. Moreover, all these new understandings of CYP2J2 may provide important value for the early diagnosis and new targeted drug therapy of KIRC.

Hypoxic TAM-derived exosomal miR-155-5p promotes RCC progression through HuR-dependent IGF1R/AKT/PI3K pathway

Hypoxic tumor-associated macrophages (TAMs) are related to poor prognosis of patients with clear cell renal cell carcinoma (ccRCC). Exosomes are small lipid-bilayer vesicles that implicated in tumor progression and metastasis. However, whether hypoxic TAM-derived exosomes affect RCC progression within the hypoxic tumor microenvironment has not been elucidated. GSE analysis identified miR-155-5p was upregulated in RCC. Moreover, we quantified levels of miR-155-5p using RT-qPCR, performed immunohistochemical staining in 79 pairs of primary RCC specimens and related them to clinicopathological parameters. Higher miR-155-5p levels were related to more CD163 + TAM infiltration and elevated HIF-1a expression in our cohort. In the in vitro studies, we initially purified and characterized the exosomes from the supernatant of TAMs subjected to normoxia or hypoxia, and then transfected antagomiR-155-5p or control into these TAMs to produce corresponding exosomes. Gain and loss-of-function studies further investigated the effect of transferred hypoxic exosomal miR-155-5p on the cross-talk between TAMs and RCC cells in xenograft model and in vitro co-culture experiments. The results of RNA immunoprecipitation analyses elucidated that miR-155-5p could directly interact with human antigen R (HuR), thus increasing IGF1R mRNA stability. Mechanistically, hypoxic TAM-Exo transferred miR-155-5p promoted RCC progression partially through activating IGF1R/PI3K/AKT cascades. Taken together, transfer of miR-155-5p from hypoxic TAMs by exosomes to renal cancer cells explains the oncogenic manner, in which M2 macrophages confer the malignant phenotype to RCC cells by enhancing HuR-mediated mRNA stability of IGF1R.

Multidiscipline Immunotherapy-Based Rational Combinations for Robust and Durable Efficacy in Brain Metastases from Renal Cell Carcinoma

Advanced imaging techniques for diagnosis have increased awareness on the benefits of brain screening, facilitated effective control of extracranial disease, and prolonged life expectancy of metastatic renal cell carcinoma (mRCC) patients. Brain metastasis (BM) in patients with mRCC (RCC-BM) is associated with grave prognoses, a high degree of morbidity, dedicated assessment, and unresponsiveness to conventional systemic therapeutics. The therapeutic landscape of RCC-BM is rapidly changing; however, survival outcomes remain poor despite standard surgery and radiation, highlighting the unmet medical needs and the requisite for advancement in systemic therapies. Immune checkpoint inhibitors (ICIs) are one of the most promising strategies to treat RCC-BM. Understanding the role of brain-specific tumor immune microenvironment (TIME) is important for developing rationale-driven ICI-based combination strategies that circumvent tumor intrinsic and extrinsic factors and complex positive feedback loops associated with resistance to ICIs in RCC-BM via combination with ICIs involving other immunological pathways, anti-antiangiogenic multiple tyrosine kinase inhibitors, and radiotherapy; therefore, novel combination approaches are being developed for synergistic potential against RCC-BM; however, further prospective investigations with longer follow-up periods are required to improve the efficacy and safety of combination treatments and to elucidate dynamic predictive biomarkers depending on the interactions in the brain TIME.

Complete deconvolution of DNA methylation signals from complex tissues: a geometric approach

MOTIVATION

It is a common practice in epigenetics research to profile DNA methylation on tissue samples, which is usually a mixture of different cell types. To properly account for the mixture, estimating cell compositions has been recognized as an important first step. Many methods were developed for quantifying cell compositions from DNA methylation data, but they mostly have limited applications due to lack of reference or prior information.

RESULTS

We develop Tsisal, a novel complete deconvolution method which accurately estimate cell compositions from DNA methylation data without any prior knowledge of cell types or their proportions. Tsisal is a full pipeline to estimate number of cell types, cell compositions and identify cell-type-specific CpG sites. It can also assign cell type labels when (full or part of) reference panel is available. Extensive simulation studies and analyses of seven real datasets demonstrate the favorable performance of our proposed method compared with existing deconvolution methods serving similar purpose.

AVAILABILITY AND IMPLEMENTATION

The proposed method Tsisal is implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of mast cells as a candidate significant target of immunotherapy for acute myeloid leukemia

OBJECTION

Immunotherapy based on T cells is a new therapy for Acute myeloid leukemia (AML). However, there has not been considerable improvement compared with traditional chemotherapeutics. This study aimed to identify important immune cells, genes, and drugs associated with the immunotherapy of AML.

METHODS

The gene expression profile and clinical data of patients with AML were downloaded from TCGA database, and the abundance ratio of immune cells was obtained via CIBERSORT. Kaplan-Meier (KM) survival analysis was used to assess the relationship between immune cells and survival time of patients with AML. Differentially expressed genes (DEGs) analysis was conducted to obtained DEGs related to mast cells. Then, protein-protein interaction (PPI) analysis and enrichment analysis were performed to explore the hub genes. Finally, Connectivity Map (CMap) database was utilized to predicts potential drugs that may reverse or induce the mast cell-related gene expression.

RESULTS

Our study showed that mast cell was correlated with survival time of patients with AML, and 135 genes were screened to be related with mast cells. 6 hub genes were identified via PPI network, and 3 potential small molecule drugs were screened to be related to regulating the mast cell-related gene expression via CMap database.

CONCLUSION

The hub genes and drugs have high research value and clinical application in AML therapy. Our study not only provides gene targets and small molecule drugs for AML immunotherapy concerning mast cells but also provides new ideas for researchers to explore immunotherapy targets of other tumors.

RAB5C, SYNJ1, and RNF19B promote male ankylosing spondylitis by regulating immune cell infiltration

Background

This study aimed to identify the key genes related to male ankylosing spondylitis (AS) and to analyze the role of immune cell infiltration in the pathological process of this disease.

Methods

The AS dataset was downloaded from the Gene Expression Omnibus (GEO) public database, and the data of male healthy controls (M_HC) and male AS patients (M_AS) were extracted. R software was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analysis of the DEGs was performed. A protein-protein interaction (PPI) network was constructed, and the hub genes were screened out. All expression profile data were analyzed by weighted correlation network analysis (WGCNA) to screen out the hub genes, which were then intersected with the hub genes from the PPI network to obtain the key genes. Finally, the difference in immune cell infiltration in the two sets of samples was evaluated with CIBERSORT, and the correlation between the key genes and infiltrating immune cells was analyzed.

Results

A total of 689 DEGs were obtained, of which 395 genes were up-regulated and 294 genes were down-regulated. Functional and pathway enrichment analysis showed that DEGs were mainly enriched in pathways related to immune response. Based on the PPI analysis, five clusters with high scores were selected. Through WGCNA, 14 gene modules were obtained. The green module with the highest correlation was selected and intersected with the cluster previously obtained to obtain three key genes, RAB5C, SYNJ1, and RNF19B. Immune infiltration analysis found that monocytes and gamma delta T cells may be involved in the process of AS. Also, RAB5C, SYNJ1, and RNF19B are all related to increased levels of monocytes and macrophages.

Conclusions

RAB5C, SYNJ1, and RNF19B are key DEGs expressed in M_AS and may play a role in the disease’s occurrence and development through regulating immune cell functions.

Identification of Epithelial-Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

Single sample scoring of hepatocellular carcinoma: A study based on data mining

Hepatocellular carcinoma (HCC) is a high mortality malignancy and the second leading cause of cancer-related deaths. Because the immune system plays a dual role by assisting the host barrier and tumor progression, there are complex interactions with considerable prognostic significance. Herein, we performed single-sample gene set enrichment (ssGSEA) to explore the tumor microenvironment (TME) and quantify the tumor-infiltrating immune cell (TIIC) subgroups of immune responses based on the HCC cohort of The Cancer Genome Atlas (TCGA) database. We evaluate molecular subpopulations, survival, function, and expression differential associations, as well as reveal potential targets, and biomarkers for immunotherapy. We combined the TME score and the 29 immune cell types in the low, medium, and high immunity groups. The stromal score, immune score, and ESTIMATE score were positively correlated with immune activity but negatively correlated with the tumor purity. There were 23 human leukocyte antigen (HLA)-related genes that were significantly different. However, KIAA1429 was not significant among the different immunity groups. Besides, programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) expression increased with the increase of immune activity. This may provide valuable information for HCC immunotherapy. We also found that there was no significant difference in naïve B cells, macrophages M1, activated mast cells, resting natural killer (NK) cells, and T cells gamma delta among the different immunity groups. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the differential proteins were mainly enriched in alpha-linolenic acid (ALA) metabolism, cytokine-cytokine receptor interaction, glycosaminoglycan biosynthesis-heparan sulfate/heparin, glycosphingolipid biosynthesis-ganglio series and proteasome. Our findings provide a deeper understanding of the immune scene, uncovering remarkable immune infiltration patterns of various subtypes of HCC using ssGSEA. This study advances the understanding of immune response and provides a basis for research to enhance immunotherapy.

Renal Cell Carcinoma-Infiltrating CD3low Vγ9Vδ1 T Cells Represent Potentially Novel Anti-Tumor Immune Players

Due to the highly immunogenic nature of renal cell carcinoma (RCC), the tumor microenvironment (TME) is enriched with various innate and adaptive immune subsets. In particular, gamma-delta (γδ) T cells can act as potent attractive mediators of adoptive cell transfer immunotherapy because of their unique properties such as non-reliance on major histocompatibility complex expression, their ability to infiltrate human tumors and recognize tumor antigens, relative insensitivity to immune checkpoint molecules, and broad tumor cytotoxicity. Therefore, it is now critical to better characterize human γδ T-cell subsets and their mechanisms in RCCs, especially the stage of differentiation. In this study, we aimed to identify γδ T cells that might have adaptive responses against RCC progression. We characterized γδ T cells in peripheral blood and tumor-infiltrating lymphocytes (TILs) in freshly resected tumor specimens from 20 RCC patients. Furthermore, we performed a gene set enrichment analysis on RNA-sequencing data from The Cancer Genome Atlas (TCGA) derived from normal kidneys and RCC tumors to ascertain the association between γδ T-cell infiltration and anti-cancer immune activity. Notably, RCC-infiltrating CD3^low Vγ9Vδ1 T cells with a terminally differentiated effector memory phenotype with up-regulated activation/exhaustion molecules were newly detected as predominant TILs, and the cytotoxic activity of these cells against RCC was confirmed in vitro. In an additional analysis of the TCGA RCC dataset, γδ T-cell enrichment scores correlated strongly with those for CTLs, Th1 cells, “exhausted” T cells, and M1 macrophages, suggesting active involvement of γδ T cells in anti-tumor rather than pro-tumor activity, and Vδ1 cells were more abundant than Vδ2 or Vδ3 cells in RCC tumor samples. Thus, we posit that Vγ9Vδ1 T cells may represent an excellent candidate for adoptive immunotherapy in RCC patients with a high risk of relapse after surgery.

Gene expression-based immune infiltration analyses of renal cancer and their associations with survival outcome

BACKGROUND

Renal cancer is a common malignant tumor with an increasing incidence rate.

METHODS

In this study, based on the gene expression profiles, we analyzed the compositions of tumor-infiltrating immune cells (TIICs) in renal cancer and paracancerous samples using CIBERSORT. The proportions of 22 TIICs subsets in 122 paired renal carcinoma and paracancerous samples, and 224 Wilms tumor (WT) samples varied between intragroup and intergroup.

RESULTS

After analyzed the difference of TIICs composition between renal cancer and paired paracancerous samples, we found that M0 macrophages and CD8 T cells were significantly elevated, while naive B cells were significantly decreased in renal cancer samples compared with paracancerous samples. Survival analysis showed that high overall TIICs proportion, the low proportion of resting mast cells and the high proportion of activated memory CD4 T cells were associated with poor prognosis of renal cancer patients. In addition, 3 clusters were identified by hierarchical clustering analysis, and they presented a distinct prognosis. Cluster 1 had superior survival outcomes, while cluster 2 had an inferior survival outcome.

CONCLUSIONS

Our study indicated that overall TIICs proportion, certain TIICs subset proportion, including resting mast cells and activated memory CD4 T cells, and distinct cluster patterns were associated with the prognosis of renal cancer, which was significant for the clinical surveillance and treatment of renal cancer.

An integrative microenvironment approach for laryngeal carcinoma: the role of immune/methylation/autophagy signatures on disease clinical prognosis and single-cell genotypes

The effects of methylation/autophagy-related genes (MARGs) and immune infiltration in the tumor microenvironment on the prognosis of laryngeal cancer were comprehensively explored in this study. Survival analysis screened out 126 MARGs and 10 immune cells potentially associated with the prognosis of laryngeal carcinoma. Cox and lasso regression analyses were then used to select 8 MARGs (CAPN10, DAPK2, MBTPS2, ST13, CFLAR, FADD, PEX14 and TSC2) and 2 immune cells (Eosinophil and Mast cell) to obtain the prognostic risk scoring system (pRS). The pRS was used to establish a risk prediction model for the prognosis of laryngeal cancer. The predictive ability of the prediction model was evaluated by GEO datasets and our clinical samples. Further analysis revealed that pRS is highly associated with single nucleotide polymorphism (SNP), copy number variation (CNV), immune checkpoint blockade (ICB) therapy and tumor microenvironment. Moreover, the screened pRS-related ceRNA network and circ_0002951/miR-548k/HAS2 pathway provide potential therapeutic targets and biomarkers of laryngocarcinoma. Based on the clustering results of pRS-related genes, single cells were then genotyped and revealed by integrated scRNA-seq in laryngeal cancer samples. Fibroblasts were found enriched in high risk cell clusters at the scRNA-seq level. Fibroblast-related ligand-receptor interactions were then exposed and a neural network-based deep learning model based on these pRS-related hub gene signatures was also established with a high accuracy in cell type prediction. In conclusion, the combination of single-cell and transcriptome laryngeal carcinoma landscape analyses can investigate the link between the tumor microenvironmental and prognostic characteristics.

A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P < .001). A total of 540 genes were screened as DEGs with 539 up‐regulated genes and 1 down‐regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine‐cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4,MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment‐related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

Metastatic Renal Cell Carcinoma Management: From Molecular Mechanism to Clinical Practice

The therapeutic sc"enario of metastatic renal cell cancer (mRCC) has noticeably increased, ranging from the most studied molecular target therapies to those most recently introduced, up to immune checkpoint inhibitors (ICIs). The most recent clinical trials with an ICI-based combination of molecular targeted agents and ICI show how, by restoring an efficient immune response against cancer cells and by establishing an immunological memory, it is possible to obtain not only a better radiological response but also a longer progression-free and overall survival. However, the role of tyrosine kinase inhibitors (TKIs) remains of fundamental importance, especially in patients who, for clinical characteristics, tumor burden and comorbidity, could have greater benefit from the use of TKIs in monotherapy rather than in combination with other therapies. However, to use these novel options in the best possible way, knowledge is required not only of the data from the large clinical trials but also of the biological mechanisms, molecular pathways, immunological mechanisms, and methodological issues related to both new response criteria and endpoints. In this complex scenario, we review the latest results of the latest clinical trials and provide guidance for overcoming the barriers to decision-making to offer a practical approach to the management of mRCC in daily clinical practice. Moreover, based on recent literature, we discuss the most innovative combination strategies that would allow us to achieve the best clinical therapeutic results.

Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

Identification of three molecular subtypes based on immune infiltration in ovarian cancer and its prognostic value

BACKGROUND

Increasing studies suggest that tumor immune infiltration is a relative factor of prognosis in ovarian cancer (OvCa). The present study explored the composition of tumor-infiltrating immune cells (TIICs) in OvCa using CIBERSORT algorithm and further assessed their values for prognosis and therapeutic strategies by molecular subtypes.

METHODS

Publicly available databases including The Cancer Genome Atlas (TCGA) and GTEx were searched. Ovarian tumor samples were available from TCGA, and normal ovarian samples were obtained from the GTEx dataset. The relative proportions of immune cell profiling in OvCa and normal samples were evaluated by CIBERSORT algorithm. Association between each immune cell subtype and survival was inferred by the fractions of 22 immune cell types. "CancerSubtypes" R-package was employed to identify the three types of molecular classification and analyze the functional enrichment in each subclass. Response to immunotherapy and anticancer drug targets was predicted via TIDE algorithm and GDSC dataset.

RESULTS

Substantial variation reflecting individual difference was identified between cancer and normal tissues in the immune infiltration profiles. T cells CD4 memory activated, macrophages M1 were associated with improved overall survival (OS) as evaluated by univariate Cox regression and multivariate Cox. Three subtypes were identified by ´CancerSubtypes' R-package and every sub-cluster possessed specific immune cell characterization. Meanwhile, Cluster II exhibited poor prognosis and sensitive response to immunotherapy.

CONCLUSIONS

The cellular component of immune infiltration shows remarkable variation in OvCa. Profiling of immune infiltration is useful in prediction of prognosis of OvCa. The results from profiling might be considered in therapeutic modulation.

Absent in melanoma 2-mediating M1 macrophages facilitate tumor rejection in renal carcinoma

Absent in melanoma 2 (AIM2) as an immune regulator for the regulation of tumor-associated macrophages (TAMs) function is unclear in tumor development. Here, the AIM2 function was investigated in TAMs-mediated malignant behaviors of renal carcinoma. The correlation analysis result showed that the AIM2 expression in TAMs was negatively correlated with the percentages of M2-like polarization phenotype in human or murine renal cancer specimens. By the cocultured assay with bone marrow-derived macrophages (BMDMs) and Renca cells, overexpression of AIM2 in macrophages enhanced the inflammasome activation and reversed the phenotype from M2 to M1. Compared with BMDMs-Ctrl cocultured group, BMDMs-AIM2 cocultured group showed reduced tumor cell proliferation and migration. The blockade of inflammasome activation by the inhibitor Ac-YVAD-CMK abrogated AIM2-mediated M1 polarization and the inhibition of tumor cell growth. To evaluate the therapeutic efficacy of AIM2-mediated M1 macrophages in vivo, BMDMs-AIM2 were intravenously injected into subcutaneous Renca-tumor mice. The results showed that the infiltration of M1 TAMs was increased and tumor growth was suppressed in BMDMs-AIM2-treated mice when compared with BMDMs-Ctrl-treat mice. Accordingly, the blockade of inflammasome activation reduced the anti-tumor activities of BMDMs-AIM2. Moreover, the lung metastases of renal carcinoma were suppressed by the administration of BMDMs-AIM2 accompanied with the reduced tumor foci. These results demonstrated that AIM2 enhanced TAMs polarization switch from anti-inflammatory M2 phenotypy to pro-inflammatory M1 through inflammasome signaling activation, thus exerting therapeutic intervention in renal carcinoma models. Our results provide a possible molecular mechanism for the modulation of TAMs polarization in tumor microenvironment and open a new potential therapeutic approach for renal cancer.

Construction of a prognostic value model in papillary renal cell carcinoma by immune-related genes

Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma, and the role of immune-related genes (IRGs) in tumorigenesis and metastasis is evident; its prognostic value in PRCC remains unclear. In this study, we downloaded the gene expression profiles and clinical data of patients with PRCC from The Cancer Genome Atlas (TCGA) database and obtained IRGs from the ImmPort database. A total of 371 differentially expressed IRGs (DEIRGs) were discovered between PRCC and normal kidney tissues. Prognostic DEIRGs (PDEIRGs) were identified by univariate Cox regression analysis. Then, we screened the four most representative PDEIRGs (IL13RA2, CCL19, BIRC5, and INHBE) and used them to construct a risk model to predict the prognosis of patients with PRCC. This model precisely stratified survival outcome and accurately identified mutation burden in PRCC. Thus, our results suggest that these four PDEIRGs are available prognostic predictors for PRCC. They could be used to assess the prognosis and to guide individualized treatments for patients with PRCC.

Prognostic significance and immune infiltration of microenvironment-related signatures in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is 1 of the highly fatal and most aggressive types of malignancies and accounts for the vast majority of Pancreatic Cancer. Numerous studies have reported that the tumor microenvironment (TME) was significantly correlated with the oncogenesis, progress, and prognosis of various malignancies. Therefore, mining of TME-related genes is reasonably important to improve the overall survival of patients with PDAC.The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm was applied to identify differential expressed genes. Functional and pathway enrichment analyses, protein-protein interaction network construction and module analysis, overall survival analysis and tumor immune estimation resource database analysis were then performed on differential expressed genes.Data analysis indicated that higher immune scores were correlated with better overall survival (P = 0.033). Differential expression analysis obtained 90 intersection genes influencing both stromal and immune scores. Among these intersection genes, CA9, EBI3, SPOCK2, WDFY4, CD1D, and CCL22 were significantly correlated with overall survival in PDAC patients. Moreover, multivariate Cox analysis revealed that CA9, SPOCK2, and CD1D were the most significant prognostic genes, and were closely correlated with immune infiltration in TCGA cohort. Further analysis indicated that CD1D were significantly related with immune cell biomarkers for PDAC patients.In summary, our findings provide a more comprehensive insight into TME and show a list of prognostic immune associated genes in PDAC. However, further studies on these genes need to be performed to gain additional understanding of the association between TME and prognosis in PDAC.

PD-L1 induces macrophage polarization toward the M2 phenotype via Erk/Akt/mTOR

PD-L1 (programmed death-ligand 1) is the ligand of PD-1 (programmed cell death protein 1) and regulates inhibitory immune responses. It is well known that PD-L1 suppresses T cell function via binding to PD-1. However, little is known about the role of the PD-1/PD-L1 axis in macrophage polarization. According to previous studies, the function of the PD-1/PD-L1 axis in macrophage polarization is controversial, and the underlying mechanism has not been fully elucidated. Thus, we treated THP-1-derived macrophages with human PD-L1 Fc to determine the role of the PD-1/PD-L1 axis in macrophage polarization. To further explore the mechanism, we performed RNA sequencing and used specific inhibitors to identify the implicated signalling pathways. In this study, we found that PD-L1 induces the upregulation of CD206 expression, which is inhibited by nivolumab, LY294002, U0126, and rapamycin. Evaluation of differentially expressed genes (DEGs) and bioinformatics analysis indicated that PD-L1 also induces the upregulation of the expression of genes that maintain mitochondrial function and mediate metabolic switching. In addition, we did not detect PD-L1-induced CD86 alterations, indicating that PD-L1 treatment has no significant influence on M1 polarization. Taken together, these results suggest that PD-L1 binds to PD-1 and promotes M2 polarization accompanied by mitochondrial function enhancement and metabolic reprogramming via Erk/Akt/mTOR. This study elucidates the role of PD-L1 in macrophage polarization and verifies the underlying mechanisms for the first time. Considering that aberrantly upregulated PD-L1 expression contributes to a wide variety of diseases, targeting PD-L1-mediated macrophage polarization is a prospective therapeutic strategy for both neoplastic and nonneoplastic diseases.

Single-cell analysis reveals metastatic cell heterogeneity in clear cell renal cell carcinoma

Renal cell carcinoma (RCC) is one of the leading causes of cancer-related death worldwide. Tumour metastasis and heterogeneity lead to poor survival outcomes and drug resistance in patients with metastatic RCC (mRCC). In this study, we aimed to assess intratumoural heterogeneity (ITH) in mRCC cells by performing a combined analysis of bulk data and single-cell RNA-sequencing data, and develop novel biomarkers for prognosis prediction on the basis of the potential molecular mechanisms underlying tumorigenesis. Eligible single-cell cohorts related to mRCC were acquired using the Gene Expression Omnibus (GEO) dataset to identify potential mRCC subpopulations. We then performed gene set variation analysis to understand the differential function in primary RCC and mRCC samples. Subsequently, we applied weighted correlation network analysis to identify coexpressing gene modules that were related to the external trait of metastasis. Protein-protein interactions were used to screen hub subpopulation-difference (sub-dif) markers (ACTG1, IL6, CASP3, ACTB and RAP1B) that might be involved in the regulation of RCC metastasis and progression. Cox regression analysis revealed that ACTG1 was a protective factor (HR < 1), whereas the other four genes (IL6, CASP3, ACTB and RAP1B) were risk factors (HR > 1). Kaplan-Meier survival analysis suggested the potential prognostic value of these sub-dif markers. The expression of sub-dif markers in mRCC was further evaluated in clinical samples by immunohistochemistry (IHC). Additionally, the genetic features of sub-dif marker expression patterns, such as genetic variation profiles, correlations with tumour-infiltrating lymphocytes (TILs), and targeted signalling pathway activities, were assessed in bulk RNA-seq datasets. In conclusion, we established novel subpopulation markers as key prognostic factors affecting EMT-related signalling pathway activation in mRCC, which could facilitate the implementation of a treatment for mRCC patients.

Investigating T Cell Immunity in Cancer: Achievements and Prospects

T cells play a key role in tumour surveillance, both identifying and eliminating transformed cells. However, as tumours become established they form their own suppressive microenvironments capable of shutting down T cell function, and allowing tumours to persist and grow. To further understand the tumour microenvironment, including the interplay between different immune cells and their role in anti-tumour immune responses, a number of studies from mouse models to clinical trials have been performed. In this review, we examine mechanisms utilized by tumour cells to reduce their visibility to CD8⁺ Cytotoxic T lymphocytes (CTL), as well as therapeutic strategies trialled to overcome these tumour-evasion mechanisms. Next, we summarize recent advances in approaches to enhance CAR T cell activity and persistence over the past 10 years, including bispecific CAR T cell design and early evidence of efficacy. Lastly, we examine mechanisms of T cell infiltration and tumour regression, and discuss the strengths and weaknesses of different strategies to investigate T cell function in murine tumour models.

Roles of the Dynamic Tumor Immune Microenvironment in the Individualized Treatment of Advanced Clear Cell Renal Cell Carcinoma

Immune checkpoint inhibitors (ICIs) are currently a first-line treatment option for clear cell renal cell carcinoma (ccRCC). However, recent clinical studies have shown that a large number of patients do not respond to ICIs. Moreover, only a few patients achieve a stable and durable response even with combination therapy based on ICIs. Available studies have concluded that the response to immunotherapy and targeted therapy in patients with ccRCC is affected by the tumor immune microenvironment (TIME), which can be manipulated by targeted therapy and tumor genomic characteristics. Therefore, an in-depth understanding of the dynamic nature of the TIME is important for improving the efficacy of immunotherapy or combination therapy in patients with advanced ccRCC. Here, we explore the possible mechanisms by which the TIME affects the efficacy of immunotherapy and targeted therapy, as well as the factors that drive dynamic changes in the TIME in ccRCC, including the immunomodulatory effect of targeted therapy and genomic changes. We also describe the progress on novel therapeutic modalities for advanced ccRCC based on the TIME. Overall, this review provides valuable information on the optimization of combination therapy and development of individualized therapy for advanced ccRCC.

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained to build a molecular prognostic score (mPS) system. Afterwards, we investigated the potential mechanisms underlying mPS by assessing gene set enrichment analysis, mutations, copy number variations (CNVs) and immune cell infiltration. A total of 275 prognosis-related genes were identified, which were also differentially expressed between ccRCC patients and healthy controls. We then constructed a universal mPS system that depends on the expression status of only 21 of these genes by applying AI-based algorithms. Then, the mPS were validated by another independent cohort and demonstrated to be applicable to ccRCC subsets. Furthermore, a nomogram comprising the mPS score and several independent variables was established and proved to effectively predict ccRCC patient prognosis. Finally, significant differences were identified regarding the pathways, mutated genes, CNVs and tumor-infiltrating immune cells among the subgroups of ccRCC stratified by the mPS system. The AI-based mPS system can provide critical prognostic prediction for ccRCC patients and may be useful to inform treatment and surveillance decisions before initial intervention.

Preclinical analysis of novel prognostic transcription factors and immune-related gene signatures for bladder cancer via TCGA-based bioinformatic analysis

Bladder cancer (BLCA) is a common malignancy of human urinary tract, whose prognosis is influenced by complex gene interactions. Immune response activity can act as a potential prognostic factor in BLCA. The present study established a prognostic model, based on the identification of tumor transcription factors (TFs) and immune-related genes (IRGs), and further explored their therapeutic potential in BLCA. The enrichment scores of 29 IRG sets, identified in The Cancer Genome Atlas BLCA tumor samples, were quantified by single-sample Gene Set Enrichment Analysis. The abundance of infiltrated immune cells in tumor tissues was determined using the Estimating Relative algorithm. Tumor-related TFs and IRGs signatures were retrieved using Least Absolute Shrinkage and Selection Operator Cox regression analysis. A prognostic gene network was built using Pearson's correlation analysis as a means of predicting the regulatory relationship between prognostic TFs and IRGs. A nomogram was devised to also predict the overall survival (OS) rate of patients with BLCA. Based on the Genomics of Drug Sensitivity in Cancer data, potential therapeutic drugs were identified upon analyzing the relationship between the expression level of prognostic genes and respective IC₅₀ values. In vitro experiments were implemented for further validation. Respective TF binding profiles were acquired from the JASPAR 2020 database. The elevated infiltration of CD8⁺ T Cells was correlated with an improved OS of patients with BLCA. An innovative prognostic model for BLCA was then constructed that composed of nine putative gene markers: CXCL13, prepronociceptin, microtubule-associated protein tau, major histocompatibility class I polypeptide-related sequence B, prostaglandin E2 receptor EP3 subtype, IL20RA, proepiregulin, early growth response protein 1 and FOS-related antigen 1 (FOSL1). Furthermore, a theoretical basis for the correlation between the prognostic TFs and IRGs was reported. For this, 10 potentially effective drugs targeting the TFs in the present model for patients with BLCA were identified. It was then verified that downregulation of FOSL1 can lead to an enhanced sensitivity of the TW37 in T24 bladder cancer cells. Overall, the present prognostic model demonstrated a robust capability of predicting OS of patients with BLCA. Hence, the gene markers identified could be applied for targeted therapies against BLCA.

Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs

Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.

The Immune Cell Landscape in Renal Allografts

Immune cell infiltration plays an important role in the pathophysiology of kidney grafts, but the composition of immune cells is ill-defined. Here, we aimed at evaluating the levels and composition of infiltrating immune cells in kidney grafts. We used CIBERSORT, an established algorithm, to estimate the proportions of 22 immune cell types based on gene expression profiles. We found that non-rejecting kidney grafts were characteristic with high rates of M2 macrophages and resting mast cells. The proportion of M1 macrophages and activated NK cells were increased in antibody-mediated rejection (ABMR). In T cell-mediated rejection (TCMR), a significant increase in CD8 T cell and γδT cell infiltration was observed. CD8 positive T cells were dramatically increased in mixed-ABMR/TCMR. Then, the function of ABMR and TCMR prognostic molecular biomarkers were identified. Finally, we described the gene expression of molecular markers for ABMR diagnosis was elevated and related to the ratio of monocytes and M1 macrophages in ABMR biopsies, while the expression of TCMR diagnosis markers was increased too and positively correlated with γδT cells and activated CD4 memory T cells in TCMR biopsies. Our data suggest that CIBERSORT’s deconvolution analysis of gene expression data provides valuable information on the composition of immune cells in renal allografts.

Intratumoral CXCL13+CD8+T cell infiltration determines poor clinical outcomes and immunoevasive contexture in patients with clear cell renal cell carcinoma

Background

Chemokine (C-X-C motif) ligand 13 (CXCL13) was known as a selective chemotaxis for B cells, a product of follicular helper CD4⁺T cells (T_FH) and a contributor to tertiary lymphoid structures (TLS). Although secretion and function of CXCL13 produced by T_FH have been deeply explored, the immune function and prognostic significance of CXCL13 secreted by CD8⁺T cells still remain unrevealed. This study aims to investigate the clinical merit of CXCL13⁺CD8⁺T cells in clear cell renal cell carcinoma (ccRCC).

Methods

We analyzed prognostic value and immune contexture that associated with CXCL13⁺CD8⁺T cells infiltration level in a total of 755 patients from Zhongshan Hospital cohort (n=223) and The Cancer Genome Atlas cohort (n=532). In vitro analyses were conducted on 42 samples of resected tumor tissue from Zhongshan Hospital in order to detect the immune status of CXCL13⁺CD8⁺T cells and total CD8⁺T cells. Immunohistochemistry (IHC) and flow cytometry were applied to characterize immune cells and portray the tumor microenvironment (TME) in ccRCC.

Results

Intratumoral CXCL13⁺CD8⁺T cells abundance was associated with inferior overall survival and disease-free survival. CXCL13⁺CD8⁺T cells possessed higher level of immune checkpoints like programmed cell-death protein 1 (PD-1), T-cell immunoglobulin mucin 3 (Tim-3), T cell immunoreceptor with Ig and ITIM domains (TIGIT) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), higher Ki-67 expression and lower tumor necrosis factor α (TNF-α), interferon γ (IFN-γ) expression. Total CD8⁺T cells in high-level CXCL13⁺CD8⁺T cells infiltration subgroup exhibited elevated exhausted markers (PD-1, Tim-3, TIGIT) and descended activated markers (TNF-α, IFN-γ) without quantity variance. Furthermore, the abundance of intratumoral CXCL13⁺CD8⁺T cell was correlated with immunoevasive TME accompanied by increased T helper 2 cells, tumor-associated macrophages, Foxp3⁺ regulatory T cells, TLS and decreased natural killer cells, GZMB⁺ cells.

Conclusions

Intratumoral CXCL13⁺CD8⁺T cells infiltration indicated inferior clinical outcome in patients with ccRCC. CXCL13⁺CD8⁺T cells possessed increased exhausted markers, decreased effector molecules and better proliferation ability. CXCL13⁺CD8⁺T cells abundance impaired total CD8⁺T cells’ immune function. Intratumoral CXCL13⁺CD8⁺T cells abundance was associated with immunoevasive contexture. The abundance of CXCL13⁺CD8⁺T cells was an independent prognosticator and a potential immunotherapeutic target marker for ccRCC treatment.

CTHRC1 Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Kidney Renal Papillary Cell Carcinoma and Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) are the most common RCC types. RCC has high immune infiltration levels, and immunotherapy is currently one of the most promising treatments for RCC. Collagen triple helix repeat containing 1 (CTHRC1) is an extracellular matrix protein that regulates tumor invasion and modulates the tumor microenvironment. However, the association of CTHRC1 with the prognosis and tumor-infiltrating lymphocytes of KIRP and KIRC has not been reported. We examined the CTHRC1 expression differences in multiple tumor tissues and normal tissues via exploring TIMER, Oncomine, and UALCAN databases. Then, we searched the Kaplan-Meier plotter database to evaluate the correlation of CTHRC1 mRNA level with clinical outcomes. Subsequently, the TIMER platform and TISIDB website were chosen to assess the correlation of CTHRC1 with tumor immune cell infiltration level. We further explored the causes of aberrant CTHRC1 expression in tumorigenesis. We found that CTHRC1 level was significantly elevated in KIRP and KIRC tissues relative to normal tissues. CTHRC1 expression associates with tumor stage, histology, lymph node metastasis, and poor clinical prognosis in KIRP. The CTHRC1 level correlates to tumor grade, stage, nodal metastasis, and worse survival prognosis. Additionally, CTHRC1 is positively related to different tumor-infiltrating immune cells in KIRP and KIRC. Moreover, CTHRC1 was closely correlated with the gene markers of diverse immune cells. Also, high CTHRC1 expression predicted a worse prognosis in KIRP and KIRC based on immune cells. Copy number variations (CNV) and DNA methylation might contribute to the abnormal upregulation of CTHRC1 in KIRP and KIRC. In conclusion, CTHRC1 can serve as a biomarker to predict the prognosis and immune infiltration in KIRP and KIRC.

IGLL5 is correlated with tumor-infiltrating immune cells in clear cell renal cell carcinoma

Renal cell carcinomas (RCCs) account for about 90% of renal tumors, and their major histological subtype is ccRCC (clear cell RCC). Increasing evidence has indicated that the tumor microenvironment plays a significant role in the occurrence and development of ccRCC. In this study, we used ESTIMATE and CIBERSORT computational methods to calculate the proportion of immune and stromal components and the rate of TICs (tumor‐infiltrating immune cells) in 539 ccRCC samples from The Cancer Genome Atlas database. By examining the intersection of the differentially expressed genes obtained by the protein–protein interaction network and Cox regression analysis, we identified only one overlapping gene: IGLL5 (immunoglobulin lambda‐like polypeptide 5). We report that IGLL5 expression is correlated with TICs. Furthermore, our immunoinfiltration analyses revealed that three types of TIC are positively correlated with IGLL5 expression. IGLL5 may have potential as a prognostic biomarker of ccRCC.

ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19

Background

KIRC is one of the most common cancers with a poor prognosis. ACE2 was involved in tumor angiogenesis and progression in many malignancies. The role of ACE2 in KIRC is still ambiguous.

Methods

Various bioinformatics analysis tools were investigated to evaluate the prognostic value of ACE2 and its association with immune infiltration in KIRC.

Results

ACE2 was shown to be downregulated in KIRC at the mRNA and protein level. Low expression of ACE2 protein in KIRC patients was observed in subgroup analyses based on gender, age, weight, tumor grade, and cancer stage. Upregulation of ACE2 in KIRC was associated with a favorable prognosis. ACE2 mRNA expression showed a positive correlation with the abundance of immune cells (B cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells) and the level of immune markers of different immune cells in KIRC. ACE2 expression could affect, in part, the immune infiltration and the advanced cancer stage. Moreover, enrichment analysis revealed that ACE2 in KIRC were mainly involved in translation factor activity, immunoglobulin binding, metabolic pathways, transcriptional misregulation in cancerous cells, cell cycle, and ribosomal activity. Several ACE2-associated kinases, miRNA, and transcription factor targets in KIRC were also identified.

Conclusion

ACE2 was downregulated in KIRC and served as a prognostic biomarker. It was also shown to be associated with immune infiltration.

Comprehensive analysis of glutathione peroxidase-1 (GPX1) expression and prognostic value in three different types of renal cell carcinoma

Background

Glutathione peroxidase-1 (GPX1) is generally expressed in tissues with high oxygen tension such as the kidneys and lungs, and its main function is to degrade reactive oxygen species (ROS) and protect cells from oxidative stress. Studies have shown that GPX1 is upregulated in many tumor tissues and is closely related to tumor progression and metastasis. This study aimed to explore the possibility of GPX1 as a biomarker for kidney chromophobe cell carcinoma (KICH), kidney renal papillary cell carcinoma (KIRP), and kidney renal clear cell carcinoma (KIRC).

Methods

The Oncomine and GEPIA databases were used to analyze the GPX1 expression differences between tumor and normal tissues, and the UALCAN, GEPIA and DriverDBv3 databases were used to perform the survival analyses. The GeneMANIA interactive tool was then used to find the GPX1-related protein-protein interaction (PPI). Following this, the LinkedOmics database was used for the enrichment analysis of GPX1, and the Timer database was used to estimate the abundance of immune infiltration. Finally, quantitative polymerase chain reaction (qPCR) was performed on patient specimens collected in the clinic to confirm the database findings.

Results

In our study, we found that the expression of GPX1 in three types of renal cell carcinoma (RCC) were upregulated, and the high expression of GXP1 was related to the poor prognosis of patients with KICH and KIRC. On the contrary, KIRP patients with a high expression of GPX1 had a better prognosis. In addition, GPX1 was related to the abundance of immune cell infiltration. The results of the qPCR analysis confirmed that the expression of GPX1 in RCC was increased compared with the control group (P<0.05).

Conclusions

Our results indicate that the expression of GPX1 is related to the prognosis of three types of RCC. As such, GPX1 expression could be a reliable diagnostic and prognostic biomarker for RCC and, more importantly, may provide a new direction for therapeutic strategies.

Identification of immune-based prostate cancer subtypes using mRNA expression

Immune infiltration in Prostate Cancer (PCa) was reported to be strongly associated with clinical outcomes. However, previous research could not elucidate the diversity of different immune cell types that contribute to the functioning of the immune response system. In the present study, the CIBERSORT method was employed to evaluate the relative proportions of immune cell profiling in PCa samples, adjacent tumor samples and normal samples. Three types of molecular classification were identified in tumor samples using the ‘CancerSubtypes’ package of the R software. Each subtype had specific molecular and clinical characteristics. In addition, functional enrichment was analyzed in each subtype. The submap and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were also used to predict clinical response to the immune checkpoint blockade. Moreover, the Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to screen for potential chemotherapeutic targets for the treatment of PCa. The results showed that Cluster I was associated with advanced PCa and was more likely to respond to immunotherapy. The findings demonstrated that differences in immune responses may be important drivers of PCa progression and response to treatment. Therefore, this comprehensive assessment of the 22 immune cell types in the PCa Tumor Environment (TEM) provides insights on the mechanisms of tumor response to immunotherapy and may help clinicians explore the development of new drugs.

ACE2 Correlated With Immune Infiltration Serves As A Novel Prognostic Biomarker In Clear Cell Renal Cell Carcinoma: Implication For COVID-19

The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global infection, and is seriously threatening human life, especially cancer patients. Thus, we sought to determine the clinical roles of ACE2 (the cell entry receptor of SARS-CoV-2) in ccRCC (clear cell renal cell carcinoma). TCGA, GEO and TIP datasets, and immunohistochemistry and western blot results were used to determine the prognostic and clinicopathological characteristics of ACE2. ACE2 expression was down-regulated in ccRCC tissues and cell lines. The multivariate Cox regression analysis results indicated that increased ACE2 expression was independent predictor of longer OS (HR: 0.8259, 95%CI: 0.7734-0.8819, P<0.0001) and RFS (HR: 0.8023, 95%CI: 0.7375-0.8729, P<0.0001) in ccRCC patients. Lower ACE2 expression was also associated with advanced tumor stage, higher histological grade and pathological stage, and metastasis. Besides, ACE2 expression was significantly positively and negatively correlated with CD4 Naïve infiltration and CD4 Memory infiltration, respectively. Moreover, higher CD4 Naïve and lower CD4 Memory infiltration levels were associated with better pathological features and longer OS and RFS. Furthermore, high ACE2 expression group in decreased CD4 Naïve, enriched CD4 Naïve and enriched CD4 memory cohort had favorable prognosis. These findings identified that AEC2 was significantly reduced in ccRCC, and decreased ACE2 was related to worse pathological features and poor prognosis. Low ACE2 expression in ccRCC may partially affect the prognosis due to altered immune cells infiltration levels.

A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction.

METHODS

The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature.

RESULTS

Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8⁺ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature.

CONCLUSION

Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

Bioinformatics Analysis of the Prognostic and Biological Significance of ZDHHC-Protein Acyltransferases in Kidney Renal Clear Cell Carcinoma

ZDHHC-protein acyltransferases (ZDHHCs) are a family of 23 signature Asp-His-His-Cys (DHHC) domain-containing enzymes that mediate palmitoylation by covalent attachment of the 16-carbon fatty acid palmitate to thiol groups of specific cysteine residues in substrate proteins. Emerging evidence has shown abnormal expression of ZDHHCs in a variety of disease states, including cancer. Kidney renal clear cell carcinoma (KIRC) is the eighth most common type of cancer, which accounts for the majority of malignant kidney tumors. However, there are currently no effective therapeutic targets or biomarkers for clinical treatment and prognosis in KIRC. In this study, we first analyzed the expression pattern of the 23 ZDHHCs in KIRC using TCGA and GEPIA database, and found that the expression of ZDHHC2, 3, 6, 14, 15, 21, and 23 was significantly down-regulated whereas the expression of ZDHHC9, 17, 18, 19 and 20 was significantly up-regulated in KIRC patient tissues vs. normal tissues. And the expression of ZDHHC2, 3, 6, 9, 14, 15, and 21 in tumors decreased with the increase of the pathological stage of KIRC patients. Notably, KIRC patients with decreased expression of ZDHHC3, 6, 9, 14, 15, 17, 20, 21, 23 and increased expression of ZDHHC19 were significantly associated with poor prognosis. Further, we found that there was a significant correlation between ZDHHC3, 6, 9, 14, 15, 17, 19, 20, 21, 23 expressions and immune cell infiltration. Besides, high mRNA expression was the most common type of gene alteration and there was a high correlation among the expression of ZDHHC6, 17, 20 and 21. Finally, function prediction indicated that the immune or metabolic disorders or the activation of oncogenic signaling pathways caused by abnormal expression of these ZDHHCs may be important mechanisms of tumor progression and poor prognosis in patients with KIRC. Our results may provide novel insight for identifying tumor markers or molecular targets for the treatment of KIRC.

Potential of Immune-Related Genes as Biomarkers for Diagnosis and Subtype Classification of Preeclampsia

Objective

Preeclampsia is the main cause of maternal mortality due to a lack of diagnostic biomarkers and effective prevention and treatment. The immune system plays an important role in the occurrence and development of preeclampsia. This research aimed to identify significant immune-related genes to predict preeclampsia and possible prevention and control methods.

Methods

Differential expression analysis between normotensive and PE pregnancies was performed to identify significantly changed immune-related genes. Generalized linear model (GLM), random forest (RF), and support vector machine (SVM) models were established separately to screen the most suitable biomarkers for the diagnosis of PE among these significantly changed immune-related genes. The consensus clustering method was used to divide the PE cases into several subgroups to explore the function of the significantly changed immune-related genes in PE.

Results

Thirteen significantly changed immune-related genes were obtained by the differential expression analysis. RF was the best model and was used to select the four most important explanatory variables (CRH, PI3, CCL18, and CCL2) to diagnose PE. A nomogram model was constructed to predict PE based on these four variables. The decision curve analysis (DCA) and clinical impact curves revealed that PE patients could significantly benefit from this nomogram. Consensus clustering analysis of the 13 differentially expressed immune-related genes (DIRGs) was used to identify 3 subgroups of PE pregnancies with different clinical outcomes and immune cell infiltration.

Conclusion

Our study identified four immune-related genes to predict PE and three subgroups of PE with different clinical outcomes and immune cell infiltration. Future studies on the three subgroups may provide direction for individualized treatment of PE patients.