BackgroundTumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.ResultsWe compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.ConclusionsOur analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.

Additional file 1: Figures S1â S25. of Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

YY1 regulates cancer cell immune resistance by modulating PD-L1 expression

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/057893v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@a51380org.highwire.dtl.DTLVardef@1a34837org.highwire.dtl.DTLVardef@da4809org.highwire.dtl.DTLVardef@af4f83_HPS_FORMAT_FIGEXP M_FIG C_FIG

/pdf/causal-integration-of-multi-omics-data-with-prior-knowledge-53yhntgx20.pdf

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Colon cancer is a common and highly malignant cancer. Its morbidity is rapidly increasing, and its prognosis is poor. Currently, immunotherapy is a rapidly developing therapeutic modality of colon cancer. This study aimed to construct a prognostic risk model based on immune genes for the early diagnosis and accurate prognostic prediction of colon cancer. Transcriptomic data and clinical data were downloaded from The Cancer Genome Atlas database. Immune genes were obtained from the ImmPort database. Differentially expressed (DE) immune genes between 473 colon cancer and 41 adjacent normal tissues were identified. The entire cohort was randomly divided into the training and testing cohort. The training cohort was used to construct the prognostic model. The testing and entire cohorts were used to validate the model. The clinical utility of the model and its correlation with immune cell infiltration were analyzed. A total of 333 DE immune genes (176 up-regulated and 157 down-regulated) were detected. We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model was approved to be an independent prognostic variable, which was more accurate than age and the pathological stage for predicting overall survival at five years. Besides, as the risk score increased, the content of CD8+ T cells in colon cancer was decreased. We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model could be used as an instrumental variable in the prognosis prediction of colon cancer.

/pdf/development-and-validation-of-a-five-immune-gene-prognostic-116kb7xk8s.pdf

Development and validation of a five-immune gene prognostic risk model in colon cancer.

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.

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.

We examined the role of differentially expressed autophagy-related genes (DEARGs) in clear cell Renal Cell Carcinoma (ccRCC) using high-throughput RNA-seq data from The Cancer Genome Atlas (TCGA). Cox regression analyses showed that 5 DEARGs (PRKCQ, BID, BAG1, BIRC5, and ATG16L2) correlated with overall survival (OS) and 4 DEARGs (EIF4EBP1, BAG1, ATG9B, and BIRC5) correlated with disease-free survival (DFS) in ccRCC patients. Multivariate Cox regression analysis using the OS and DFS prognostic risk models showed that expression of the nine DEARGs accurately and independently predicted the risk of disease recurrence or progression in ccRCC patients (area under curve or AUC values > 0.70; all p 0.60), suggesting their potential as diagnostic biomarkers for ccRCC. The expression of DEARGs also correlated with the drug sensitivity of ccRCC cell lines. The ccRCC cell lines were significantly sensitive to Sepantronium bromide, a drug that targets BIRC5. This makes BIRC5 a potential therapeutic target for ccRCC. Our study thus demonstrates that DEARGs are potential diagnostic and prognostic biomarkers and therapeutic targets in ccRCC.

Differentially expressed autophagy-related genes are potential prognostic and diagnostic biomarkers in clear-cell renal cell carcinoma.

miR-198 is involved in the formation, migration, invasion, and metastasis of various malignant cancers. However, the function and mechanism of action of miR-198 in the tumorigenesis of renal cell carcinoma (RCC) remain elusive. Here, we aimed to explore the role of miR198 in RCC. Immunohistochemistry was performed to estimate the level of survivin in RCC sections. Quantitative real-time polymerase chain reaction was performed to determine the expression level of miR-198 in fresh RCC tissues. Furthermore, the target relationship between miR-198 and BIRC5 was predicted using the TargetScanHuman 7.2 database and verified via dual-luciferase reporter assay and western blotting. The effects of miR-198 on the viability, apoptosis, invasion, and migration of A498 and ACHN cells were studied using Cell Counting Kit-8, flow cytometry, transwell migration assay, and wound healing assay, respectively. Additionally, a xenograft nude mouse model was established to evaluate the effect of miR-198 on RCC tumorigenesis. The expression levels of BIRC5 and miR-198 were respectively higher and lower in RCC tissues than those in normal adjacent tissues. Furthermore, miR-198 could inhibit luciferase activity and reduce the protein level of survivin without affecting the BIRC5 mRNA levels. miR-198 inhibited cell viability, migration, and invasion and promoted cell apoptosis; co-transfection with BIRC5 could rescue these effects. Moreover, miR-198 could repress tumor growth in the xenograft nude mouse model of RCC. Our study demonstrates that miR-198 suppresses RCC progression by targeting BIRC5.

/pdf/mir-198-inhibits-the-progression-of-renal-cell-carcinoma-by-26b17ucfu8.pdf

miR-198 inhibits the progression of renal cell carcinoma by targeting BIRC5.

Apoptosis-inducing factor (AIF) serves a crucial role in cell death and is involved in several types of cancer, including kidney cancer. The present study aimed to explore the association between AIF expression and patient survival based on tumor grades. AIF expression in 96 patients with renal cell carcinoma (RCC) was investigated using immunohistochemistry. Negative AIF expression was determined in 80 patients (83.3%). mRNA expression of AIF was analyzed in RCC and adjacent tissue samples from 15 patients. AIF mRNA expression in RCC tissues were significantly lower compared with that in adjacent tissues. Analysis of histopathological grades revealed that AIF expression was negatively associated with RCC grade, with AIF expression in Grade II tumors being lower than Grade I types, but higher than Grade III. Finally, 68 patients were followed up for 6-118 months, and it was revealed that the overall postoperative survival rate of patients with negative AIF expression was significantly lower compared with those those with positive AIF expression. These results suggest that decreased AIF expression could be associated with worsening RCC grade. Therefore, reduced AIF expression may potentially help diagnose RCC and distinguish tumor grades.

/pdf/decreased-expression-of-apoptosis-inducing-factor-in-renal-3b1yfhhekt.pdf

Decreased expression of apoptosis‑inducing factor in renal cell carcinoma is associated with poor prognosis and reduced postoperative survival

Multiple rare primary trans-systemic triple carcinoma: A case report.

BCL-2-associated athanogene 3 (BAG3) is an anti-apoptotic protein that plays an essential role in the onset and progression of multiple cancer types. However, the clinical significance of BAG3 in kidney renal clear cell carcinoma (KIRC) remains unclear.Using Tumor IMmune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) database, we explored the expression, prognostic value, and clinical correlations of BAG3 in KIRC. In addition, immunohistochemistry (IHC) of HKH cohort further validated the expression of BAG3 in KIRC and its impact on prognosis. Gene Set Cancer Analysis (GSCA) was utilized to scrutinize the prognostic value of BAG3 methylation. Gene Ontology (GO) term analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to identify potential biological functions of BAG3 in KIRC. Single-sample gene set enrichment analysis (ssGSEA) was performed to confirm the correlation between BAG3 expression and immune cell infiltration.BAG3 mRNA expression and protein expression were significantly downregulated in KIRC tissues compared to normal kidney tissues, associated with adverse clinical-pathological factors and poor clinical prognosis. Multivariate Cox regression analysis indicated that low expression of BAG3 was an independent prognostic factor in KIRC patients. GSEA analysis showed that BAG3 is mainly involved in DNA methylation and the immune-related pathways in KIRC. In addition, the expression of BAG3 is closely related to immune cell infiltration and immune cell marker set.BAG3 might be a potential therapeutic target and valuable prognostic biomarker of KIRC and is closely related to immune cell infiltration. 

BAG3 as a novel prognostic biomarker in kidney renal clear cell carcinoma correlating with immune infiltrates

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