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Comprehensive RNA-seq reveals molecular changes in kidney malignancy among people living with HIV.

ABSTRACT: To heighten the awareness of kidney malignancy in patients with HIV infection to facilitate the early diagnosis of kidney cancer, the differentially expressed mRNAs were analyzed in this malignant tumor using RNA sequencing. We identified 2,962 protein-coding transcripts in HIV-associated kidney cancer. KISS1R, CAIX, and NPTX2 mRNA expression levels were specifically increased in HIV-associated kidney cancer while UMOD and TMEM213 mRNA were decreased in most cases based on real-time PCR analyses. These findings were similar to those noted for the general population with renal cell carcinoma. Immunohistochemical staining analysis also showed that a total of 18 malignant kidney cases among the people living with HIV (PLWH) exhibited positive staining for KISS1R and CAIX. Pathway analysis of the differentially expressed mRNAs in HIV-associated kidney cancer revealed that several key pathways were involved, including vascular endothelial growth factor-activated receptor activity, IgG binding, and lipopolysaccharide receptor activity. Altogether, our findings reveal the identified molecular changes in kidney malignancy, which may offer a helpful explanation for cancer progression and open up new therapeutic avenues that may decrease mortality after a cancer diagnosis among PLWH.

SUBMITTER: Bao J

PROVIDER: S-EPMC9240952 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Atherosclerosis (AS)-associated cardiovascular disease is an important cause of mortality in an aging population of people living with HIV (PLWH). This elevated risk of atherosclerosis has been attributed to viral infection, prolonged usage of anti-retroviral therapy, and subsequent chronic inflammation. Methods: To investigate dysregulated immune signaling in PLWH with and without AS, we sequenced 9368 peripheral blood mononuclear cells (PBMCs) from 8 PLWH, 4 of whom also had atherosclerosis (AS+). To develop executable models of signaling pathways that drive cellular states in HIV-associated atherosclerosis, we developed the single-cell Boolean Omics Network Invariant Time Analysis (scBONITA) algorithm. ScBONITA (a) uses single-cell RNA sequencing data to infer Boolean rules for topologically characterized networks, (b) prioritizes genes based on their impact on signaling, (c) performs pathway analysis, and (d) maps sequenced cells to characteristic signaling states. We used scBONITA to identify dysregulated pathways in different cell-types from AS+ PLWH and AS- PLWH. To compare our findings with pathways associated with HIV infection, we used scBONITA to analyze a publicly available dataset of PBMCs from subjects before and after HIV infection. Additionally, the executable Boolean models characterized by scBONITA were used to analyze observed cellular states corresponding to the steady states of signaling pathways Results: We identified an increased subpopulation of CD8+ T cells and a decreased subpopulation of monocytes in AS+ PLWH. Dynamic modeling of signaling pathways and pathway analysis with scBONITA provided a new perspective on the mechanisms of HIV-associated atherosclerosis. Lipid metabolism and cell migration pathways are induced by AS rather than by HIV infection. These pathways included AGE-RAGE and PI3K-AKT signaling in CD8+ T cells, and glucagon and cAMP signaling pathways in monocytes. Further analysis of other cell subpopulations suggests that the highly interconnected PI3K-AKT signaling pathway drives cell migratory state in response to dyslipidemia. scBONITA attractor analysis mapped cells to pathway-specific signaling states that correspond to distinct cellular states. Conclusions: Dynamic network modeling and pathway analysis with scBONITA indicates that dysregulated lipid signaling regulates cell migration into the vascular endothelium in AS+ PLWH. Attractor analysis with scBONITA facilitated pathway-based characterization of cellular states that are not apparent in gene expression analyses.

Project description:BackgroundThe prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA.MethodsBLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated.ResultsscRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy.ConclusionBy integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

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Comprehensive RNA-seq reveals molecular changes in kidney malignancy among people living with HIV.

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