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pmid:41053280

Transcriptome analysis combined with single-cell analysis identified that APOC1 influences cholesterol transport by macrophages in ccRCC.

Fengran Guo · Ziyang Ma · Hu Wang · Zhenwei Han

Scientific reports · 2025 doi:10.1038/s41598-025-18392-1

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Transcriptome analysis combined with single-cell analysis identified that APOC1 influences cholesterol transport by macrophages in ccRCC.
doi
10.1038/s41598-025-18392-1
pmid
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[0]
Fengran Guo
[1]
Ziyang Ma
[2]
Hu Wang
[3]
Zhenwei Han
journal
Scientific reports
year
2025
abstract
Renal cell carcinoma (RCC) is one of the most prevalent categories of cancer worldwide, accounting for approximately 2% of cancer diagnoses and cancer deaths worldwide. various immunotherapeutic strategies have emerged as key approaches to managing advanced RCC. Bioinformatics methodologies were employed to analyze the TCGA-KIRC RNA sequencing dataset, GSE105261, GSE168845, and single-cell dataset GSE121636. Various techniques, including PCR, Western blotting, migration assays, scratch wound assays, flow cytometry, and immunofluorescence staining, were utilized to elucidate the functional characteristics of the tumor. In this study, we obtained transcriptome RNA sequencing data from the Cancer Genome Atlas (TCGA) database. Using the ESTIMATE algorithm, 249 immune-related genes were identified. Additionally, single-cell RNA sequencing data were integrated to determine the immune-related genes that affect the prognosis of clear cell renal cell carcinoma (ccRCC). Eventually, a total of 42 immune-related genes (IMDGs) were identified. Subsequently, in vitro experiments confirmed that APOC1 is highly expressed in ccRCC and significantly affects the migration and proliferation of ccRCC cells. Furthermore, our findings indicate that APOC1 plays a critical role in modulating cholesterol transport within macrophages and significantly contributes to the polarization of macrophages toward the M2 phenotype.
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A total of 501 tumor samples were selected from 538 clear cell renal cell carcinoma (ccRCC) cases, with Kaplan–Meier method employed to generate survival curves. The log-rank test was applied to assess statistical significance, with a p-value threshold set at 0.05. The ggpubr package was loaded to perform correlation analysis between various scores and clinical pathological features, with statistical significance evaluated using the Wilcoxon rank sum test.
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sections[6] "Correlation analysis between survival analysis and clinical pathological features" paragraph 2
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A total of 501 tumor samples were selected from 538 clear cell renal cell carcinoma (ccRCC) cases, with Kaplan–Meier method employed to generate survival curves. The log-rank test was applied to assess statistical significance, with a p-value threshold set at 0.05. The ggpubr package was loaded to perform correlation analysis between various scores and clinical pathological features, with statistical significance evaluated using the Wilcoxon rank sum test.
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The datasets used in the current study are openly available in TCGA (https://portal.gdc.cancer.gov/) database, UCSC Xena data portal (https://xenabrowser.net/), GEO (https://www.ncbi.nlm.nih.gov/geo/) database. All date and materials are available from the corresponding authors upon request.
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APOC1 is highly expressed in ccRCC and promotes ccRCC cell migration and invasion while promoting M2 macrophage polarization through cholesterol transport.
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Subsequently, in vitro experiments confirmed that APOC1 is highly expressed in ccRCC and significantly affects the migration and proliferation of ccRCC cells.
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Furthermore, our findings indicate that APOC1 plays a critical role in modulating cholesterol transport within macrophages and significantly contributes to the polarization of macrophages toward the M2 phenotype.
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APOC1 is highly expressed in ccRCC and promotes ccRCC cell migration and invasion while promoting M2 macrophage polarization through cholesterol transport.
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A total of 501 tumor samples were selected from 538 clear cell renal cell carcinoma (ccRCC) cases, with Kaplan–Meier method employed to generate survival curves. The log-rank test was applied to assess statistical significance, with a p-value threshold set at 0.05. The ggpubr package was loaded to perform correlation analysis between various scores and clinical pathological features, with statistical significance evaluated using the Wilcoxon rank sum test.
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Transcriptome analysis combined with single-cell analysis
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ics of the tumor. In this study, we obtained transcriptome RNA sequencing data from the Cancer Genome A
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their microenvironments25. By measuring the transcriptome at the single-cell level, scRNA-seq can more
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traditional methodologies26. The single-cell transcriptome dataset GSE121636 was retrieved from the GEO
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related genes were identified. Additionally, single-cell RNA sequencing data were integrated to determine the immune
[1]
hment analysis on these genes. Additionally, single-cell RNA sequencing data were integrated to determine the immune
[2]
ontrols and ccRCC samples The advancement of single-cell RNA sequencing (scRNA-seq) has facilitated a comprehensive
[3]
rehensive immune profile was generated using single-cell RNA-seq data derived from 25,000 immune cells isolat
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A total of 501 tumor samples were selected from 538 clear cell renal cell carcinoma (ccRCC) cases, with Kaplan–Meier method employed to generate survival curves. The log-rank test was applied to assess statistical significance, with a p-value threshold set at 0.05. The ggpubr package was loaded to perform correlation analysis between various scores and clinical pathological features, with statistical significance evaluated using the Wilcoxon rank sum test.
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Bioinformatics methodologies were employed to analyze the TCGA-KIRC RNA sequencing dataset, GSE105261, GSE168845, and single-cell dataset GSE121636. Various techniques, including PCR, Western blotting, migration assays, scratch wound assays, flow cytometry, and immunofluorescence staining, were utilized to elucidate the functional characteristics of the tumor. In this study, we obtained transcriptome RNA sequencing data from the Cancer Genome Atlas (TCGA) database. Using the ESTIMATE algorithm, 249 immune-related genes were identified. Additionally, single-cell RNA sequencing data were integrated to determine the immune-related genes that affect the prognosis of clear cell renal cell carcinoma (ccRCC). Eventually, a total of 42 immune-related genes (IMDGs) were identified. Subsequently, in vitro experiments confirmed that APOC1 is highly expressed in ccRCC and significantly affects the migration and proliferation of ccRCC cells. Furthermore, our findings indicate that APOC1 plays a critical role in modulating cholesterol transport within macrophages and significantly contributes to the polarization of macrophages toward the M2 phenotype.", "claims": [{"id": "claim-method", "statement": "A total of 501 tumor samples were selected from 538 clear cell renal cell carcinoma (ccRCC) cases, with Kaplan–Meier method employed to generate survival curves. The log-rank test was applied to assess statistical significance, with a p-value threshold set at 0.05. 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