Abstract
Background
Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area.
Results
In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers.
Conclusions
Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy.
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Background
Liquid biopsy, the molecular analysis of body fluids, has emerged as a promising tool in cancer research, offering a more accessible assessment of patient health status compared to traditional tissue biopsies. Advancements in technology have enabled extensive genomic and transcriptomic analysis of DNA and RNA, especially in blood [1,2,3,4]. Among the cell-free nucleic acids being studied, cell-free RNA (cfRNA) has garnered increasing attention [5], including for its tissue and cell-type specificity [6,7,8].
Cellular deconvolution, a powerful computational approach, is used to determine the cellular origin of RNA in mixed transcriptomic data, such as bulk RNA-seq [9]. A multitude of cell types contributes to the formation of blood cell-free transcriptome [6], and as a result, the comparisons between such heterogeneous samples may occlude the critical differences, which are driven only by select cell types [10]. The knowledge of which specific cell types are responsible for the observed differences in the blood during, for example, carcinogenesis, will provide a more comprehensive characterization of the cell-free transcriptome perturbations [11]. Furthermore, identifying the cell types of importance may accelerate the development of more targeted diagnostic strategies.
Cellular deconvolution using cfRNAs has been demonstrated to yield promising results in an increasing number of studies [6, 7, 9] and the deconvolution algorithm performs its own internal gene filtering.
In order to generate principal component analysis (PCA) plots, samples were separately normalized with variance stabilizing transformation using the function “vst” from the R package DESeq2 (1.34.0) [39]. Plots were generated with the R package ggplot2 (version 3.4.1) [40].
Cell-type deconvolution
Cell-type deconvolution was performed using the R package Bisque (version 1.0.5) [52] with a reference single-cell dataset. As the study focused on liver cancer, we used a liver-derived single-cell dataset hypothesized to predominantly capture liver-specific signals from the data. We selected the reference single-cell dataset in accordance with the guidelines set by the authors of the Bisque algorithm, which stipulate a minimum of three single-cell samples [52], and ensured that it featured robustly defined cell-type annotations. The reference single-cell dataset was generated by MacParland et al. from the livers of five healthy donors and contained cell type annotations for 8,444 cells [41]. The dataset containing log2CPM values and the corresponding annotation file were downloaded from the GEO database (accession number GSE115469). The single-cell reference data and the cell-free datasets were transformed into ExpressionSet class objects with the function “ExpressionSet” from the R package Biobase (version 2.54.0) [42]. To facilitate the cell-type deconvolution, the cell subtype annotations for hepatocytes, T cells, macrophages and liver sinusoidal endothelial cells (LSECs) were collapsed.
Finally, decomposition was carried out using the function “ReferenceBasedDecomposition” from the package Bisque with the parameter “use.overlap = FALSE” for each dataset. The Chen et al. dataset with the additional non-liver solid tumor samples was analyzed separately and was not used in the modeling steps.
Statistical test computation
To test if hepatocyte proportions were greater in liver cancer samples compared to other samples, a one-sided, unpaired Wilcoxon test (Wilcoxon rank-sum test) was calculated using the deconvolution results of all samples with the function “wilcox_test” from the R package rstatix (version 0.7.2) [43]. To this end, the parameters “paired = FALSE,” “exact = TRUE” and “alternative = “greater” were used. For multiple comparisons, p-values were adjusted using the Benjamini–Hochberg method with the “adjust_pvalue” function and parameter “method = ”BH”” from the R package rstatix. Effect size (r) and corresponding confidence intervals were generated with the function “wilcox_effsize” using the parameters “alternative = ”greater”,” “paired = FALSE,” “nboot = 100″ and “ci = TRUE” from the R package rstatix.
To test if the hepatocyte proportions were greater in the plasma compared with extracellular vesicles (EVs) of five liver cancer patients, a one-sided, paired Wilcoxon test (Wilcoxon signed-rank test) was performed as previously described, with the only change being the parameter “paired = TRUE.” Effect size and corresponding confidence intervals were calculated as previously described, with the only change being “paired = TRUE.” The results were visualized with the R packages rstatix, ggpubr (version 0.5.0) [44] and ggplot2.
Hepatocyte proportion-based classification
To analyze the feasibility of classifying liver cancer and healthy samples based on hepatocyte proportions, we tested 20 hepatocyte proportion cutoffs ranging from 0.2 to 0.4 in all cfRNA datasets—with samples above the cutoff classified as liver cancer (LC) patients and healthy donors (HD) if otherwise. Accuracy, sensitivity and specificity were computed at each cutoff with the function “confusionMatrix” from the R package caret (version 6.0–93) [45] and were used to generate a scatter plot using the R package ggplot2. A confusion matrix plot was generated at the cutoff with the highest classification accuracy with the function “evaluate” and a modified version of the function “plot_confusion_matrix” from the R package cvms (version 1.3.9.9000) [46].
Model construction
Random forest
We built random forest models to both determine the relative importance of cell types between biological conditions, sources of samples and to evaluate the diagnostic capabilities of various predictors. First, random forest models were built with each dataset using the cell-type proportions as input using the function “randomForest” with the parameter “importance = TRUE” from the R package randomForest (version 4.7–1.1) [47]. Afterward, the generated models were used as input for the function “varImpPlot” with the parameter “type = TRUE” from the R package randomForest, which calculates how much the model accuracy decreases without a certain predictor (feature). Finally, the results were visualized using the R package ggplot2.
To assess the performance of predictors, we trained a model with the Roskams-Hieter et al. dataset, chosen for its balanced structure and informative sample composition (Additional file 1: Fig. S1), using either the raw counts of gene markers reported by Roskams-Hieter et al. [24] and Chen et al. [2: File S1).
In light of the strengths of each diagnostic model and the enhanced performance of the combined gene marker model, we decided to integrate some of the cellular deconvolution results into the combined gene markers models. Based on previous results, we decided to integrate the proportions of hepatocytes, cholangiocytes, PECs and LSECs into the combined gene marker model. The new, integrated model displayed the highest overall accuracy among all models and closely matched the sensitivity and specificity of the deconvolution and gene marker models. The enhanced performance of the integrated model thus facilitates more comprehensive modeling of liquid biopsy data, incorporating not only gene marker expression but also additional data, such as cell-type proportions. We expect that integrated models will exhibit improved performance as they will incorporate more and varied types of liquid biopsy data. Particularly with the concern of relatively low sensitivity displayed by prospective liquid biopsy assays, the incorporation of cell-type proportions yielded by targeted cellular deconvolution can mitigate that issue to a degree.
Potential confounding factors remain a major issue for the clinical adoption of liquid biopsy. A comprehensive exploration of potential sources of variation in the blood cell-free transcriptome can mitigate these concerns. While our analysis showed one of the major confounders in liquid biopsy—age [61, 62]—to have no discernible effect on the efficacy of the targeted cellular deconvolution model either for male or female samples, we identified sample generation date to be of vital importance. Although sometimes unavoidable, extended storage of blood samples, especially in improper conditions, should be avoided whenever possible for optimal outcomes. Yet, further exploration is needed to identify other unknown confounders and possibly mitigate their adverse effects.
Conclusions
In conclusion, in this study, we showed the viability of liquid biopsy studies that are translatable across different conditions. Furthermore, we highlighted the potential of targeted cellular deconvolution and deconvolution in general for blood cell-free transcriptomic studies, which can improve cfRNA characterization and assist in the development of enhanced diagnostic assays.
In the future, we envision the application of targeted cellular deconvolution to other conditions as well and the expansion of the data generated by us through the deeper analysis of, for example, liver cirrhosis-derived samples. The increase of assay accuracy with the addition of cell-type proportion data to other liquid biopsy biomarkers in the framework of “integrated liquid biopsy” can facilitate its clinical adoption. Finally, as new liquid biopsy datasets are being continuously generated, the need for meta-analyses, comparison and integration of diverse and extensive information will continue to grow and we expect more gene markers will be discovered. We believe that the strategies outlined in this study will contribute to these efforts and expedite the clinical adoption of liquid biopsy diagnostic assays.
Availability of data and materials
The public datasets analyzed during the current study are available in the NCBI GEO repository under accession numbers GSE174302 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174302), GSE142987 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE142987), GSE115469 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115469) and NCBI SRA repository under accession numbers SRP334205 (https://www.ncbi.nlm.nih.gov/sra?term=SRP334205) and PRJNA907745 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA907745). The computational code used in this study is available at GitHub: https://github.com/aramsafrast/cfdeconv.
References
Heitzer E, Haque IS, Roberts CES, Speicher MR. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet. 2019;20:71–88.
Ignatiadis M, Sledge GW, Jeffrey SS. Liquid biopsy enters the clinic — implementation issues and future challenges. Nat Rev Clin Oncol. 2021;18:297–312.
Chen Z, Yam JWP. Recent advances in liquid biopsy in cancers: Diagnosis, disease state and treatment response monitoring. Clin Transl Discov. 2022;2: e111.
Nikanjam M, Kato S, Kurzrock R. Liquid biopsy: current technology and clinical applications. J Hematol OncolJ Hematol Oncol. 2022;15:131.
Cabús L, Lagarde J, Curado J, Lizano E, Pérez-Boza J. Current challenges and best practices for cell-free long RNA biomarker discovery. Biomark Res. 2022;10:62.
Vorperian SK, Moufarrej MN, Quake SR. Cell types of origin of the cell-free transcriptome. Nat Biotechnol. 2022;:1–7.
Koh W, Pan W, Gawad C, Fan HC, Kerchner GA, Wyss-Coray T, et al. Noninvasive in vivo monitoring of tissue-specific global gene expression in humans. Proc Natl Acad Sci. 2014;111:7361–6.
Zaporozhchenko IA, Ponomaryova AA, Rykova EY, Laktionov PP. The potential of circulating cell-free RNA as a cancer biomarker: challenges and opportunities. Expert Rev Mol Diagn. 2018;18:133–45.
Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun. 2020;11:5650.
Jaakkola MK, Elo LL. Computational deconvolution to estimate cell type-specific gene expression from bulk data. NAR Genomics Bioinforma. 2021;3:lqaa110.
Moser T, Kühberger S, Lazzeri I, Vlachos G, Heitzer E. Bridging biological cfDNA features and machine learning approaches. Trends Genet. 2023;39:285–307.
Wang H, Zhan Q, Guo H, Zhao J, **ng S, Chen S, et al. Depletion-assisted multiplexing cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicle. bioRxiv;:2023.01.31.526408.
Metzenmacher M, Váraljai R, Hegedüs B, Cima I, Forster J, Schramm A, et al. Plasma next generation sequencing and droplet digital-qPCR-based quantification of circulating cell-free RNA for noninvasive early detection of cancer. Cancers. 2020;12:353.
** N, Kan C-M, Pei XM, Cheung WL, Ng SSM, Wong HT, et al. Cell-free circulating tumor RNAs in plasma as the potential prognostic biomarkers in colorectal cancer. Front Oncol. 2023;13.
Tosevska A, Morselli M, Basak SK, Avila L, Mehta P, Wang MB, et al. Cell-free RNA as a novel biomarker for response to therapy in head & neck cancer. Front Oncol. 2022;12.
Villanueva A. Hepatocellular carcinoma. N Engl J Med. 2019;380:1450–62.
Rumgay H, Arnold M, Ferlay J, Lesi O, Cabasag CJ, Vignat J, et al. Global burden of primary liver cancer in 2020 and predictions to 2040. J Hepatol. 2022;77:1598–606.
Patel N, Yopp AC, Singal AG. Diagnostic delays are common among patients with hepatocellular carcinoma. J Natl Compr Cancer Netw JNCCN. 2015;13:543–9.
Wang W, Wei C. Advances in the early diagnosis of hepatocellular carcinoma. Genes Dis. 2020;7:308–19.
Zhang J, Chen G, Zhang P, Zhang J, Li X, Gan D, et al. The threshold of alpha-fetoprotein (AFP) for the diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis. PLoS ONE. 2020;15: e0228857.
Wang T, Zhang K-H. New blood biomarkers for the diagnosis of AFP-negative hepatocellular carcinoma. Front Oncol. 2020;10.
Yang J-C, Hu J-J, Li Y-X, Luo W, Liu J-Z, Ye D-W. Clinical applications of liquid biopsy in hepatocellular carcinoma. Front Oncol. 2022;12.
Block T, Zezulinski D, Kaplan DE, Lu J, Zanine S, Zhan T, et al. Circulating messenger RNA variants as a potential biomarker for surveillance of hepatocellular carcinoma. Front Oncol. 2022;12.
Roskams-Hieter B, Kim HJ, Anur P, Wagner JT, Callahan R, Spiliotopoulos E, et al. Plasma cell-free RNA profiling distinguishes cancers from pre-malignant conditions in solid and hematologic malignancies. Npj Precis Oncol. 2022;6:28.
Chen S, ** Y, Wang S, **ng S, Wu Y, Tao Y, et al. Cancer type classification using plasma cell-free RNAs derived from human and microbes. eLife. 2022;11:e75181.
Zhu Y, Wang S, ** X, Zhang M, Liu X, Tang W, et al. Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer. Theranostics. 2021;11:181–93.
Chen VL, Xu D, Wicha MS, Lok AS, Parikh ND. Utility of liquid biopsy analysis in detection of hepatocellular carcinoma, determination of prognosis, and disease monitoring: a systematic review. Clin Gastroenterol Hepatol. 2020;18:2879-2902.e9.
von Felden J, Garcia-Lezana T, Schulze K, Losic B, Villanueva A. Liquid biopsy in the clinical management of hepatocellular carcinoma. Gut. 2020;69:2025–34.
Foda ZH, Annapragada AV, Boyapati K, Bruhm DC, Vulpescu NA, Medina JE, et al. Detecting liver cancer using cell-free DNA fragmentomes. Cancer Discov. 2023;13:616–31.
Kim SS, Baek GO, Son JA, Ahn HR, Yoon MK, Cho HJ, et al. Early detection of hepatocellular carcinoma via liquid biopsy: panel of small extracellular vesicle-derived long noncoding RNAs identified as markers. Mol Oncol. 2021;15:2715–31.
Ibarra A, Zhuang J, Zhao Y, Salathia NS, Huang V, Acosta AD, et al. Non-invasive characterization of human bone marrow stimulation and reconstitution by cell-free messenger RNA sequencing. Nat Commun. 2020;11:400.
SRA Toolkit Development Team. NCBI SRA-Toolkit.
Bushnell B. BBMap. https://sourceforge.net/projects/bbmap/.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.
R Core Team. R: A language and environment for statistical computing. 2021.
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005;21:3439–40.
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–3.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
Hadley W. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.
MacParland SA, Liu JC, Ma X-Z, Innes BT, Bartczak AM, Gage BK, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018;9:4383.
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.
Kassambara A. rstatix: Pipe-friendly framework for basic statistical tests. manual. 2023.
Kassambara A. ggpubr: “ggplot2” based publication ready plots. manual. 2023.
Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.
Olsen LR, Zachariae HB. cvms: Cross-validation for model selection. manual. 2023.
Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.
Friedman JH, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.
Sachs MC. plotROC: A tool for plotting ROC curves. J Stat Softw. 2017;79:1–19.
Stevenson M, Nunes ES with contributions from T, Heuer C, Marshall J, Sanchez J, Thornton R, et al. epiR: Tools for the analysis of epidemiological data. manual. 2023.
Jew B, Alvarez M, Rahmani E, Miao Z, Ko A, Garske KM, et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat Commun. 2020;11:1971.
Sayeed A, Dalvano BE, Kaplan DE, Viswanathan U, Kulp J, Janneh AH, et al. Profiling the circulating mRNA transcriptome in human liver disease. Oncotarget. 2020;11:2216–32.
Huang R, Zhang X, Gracia-Sancho J, **e W-F. Liver regeneration: Cellular origin and molecular mechanisms. Liver Int. 2022;42:1486–95.
Luedde T, Kaplowitz N, Schwabe RF. Cell death and cell death responses in liver disease: mechanisms and clinical relevance. Gastroenterology. 2014;147:765-783.e4.
Nozaki Y, Hikita H, Tanaka S, Fukumoto K, Urabe M, Sato K, et al. Persistent hepatocyte apoptosis promotes tumorigenesis from diethylnitrosamine-transformed hepatocytes through increased oxidative stress, independent of compensatory liver regeneration. Sci Rep. 2021;11:3363.
Wilkinson AL, Qurashi M, Shetty S. The role of sinusoidal endothelial cells in the axis of inflammation and cancer within the liver. Front Physiol. 2020;11.
Safrastyan A, Wollny D. Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data. Front Genet. 2022;13.
Shetty S, Lalor PF, Adams DH. Liver sinusoidal endothelial cells — gatekeepers of hepatic immunity. Nat Rev Gastroenterol Hepatol. 2018;15:555–67.
Labib PL, Goodchild G, Pereira SP. Molecular pathogenesis of cholangiocarcinoma. BMC Cancer. 2019;19:185.
Teo YV, Capri M, Morsiani C, et al. Cell-free DNA as a biomarker of aging. Aging Cell. 2019;18: e12890.
Laconi E, Marongiu F, DeGregori J. Cancer as a disease of old age: changing mutational and microenvironmental landscapes. Br J Cancer. 2020;122:943–52.
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Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Ministry for Economics, Sciences and Digital Society of Thuringia (DigLeben-5575/10-9); German DFG Collaborative Research Centre AquaDiva (CRC 1076/3-A06 AquaDiva).
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AS, DW and CHzS conceptualized the study. AS performed all data analyses. AS and DW wrote the manuscript.
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Additional file 1
. Figure S1: Principal component analysis (PCA) plot. Plots were generated using variance stabilized counts prior to batch correction for Roskams-Hieter et al. (A), Chen et al. (B), Zhu et al. (C) and Block et al. (D) datasets. HD, healthy donor; LC, liver cancer. Figure S2: Performance of model training with Roskams-Hieter et al. (2022) dataset. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values. Figure S3: Differences in cellular sources of cfRNA between matched plasma and extracellular vesicle (EV) samples in the Block et al. (2022) dataset. (A) Importance of cell types in the classification of plasma and EV samples as measured by the Mean Decrease in Accuracy (MDA) value from the random forest model. Cell types were ordered in descending order of MDA. (B) Hepatocyte proportion differences between matched plasma and EV samples from each patient. Figure S4: Performance of model testing on each dataset represented with receiver operator characteristic (ROC) curves and area under ROC (AUC) values. The targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) models were trained with the Roskams-Hieter et al. (2022) dataset and tested on the remaining datasets. The results were assessed with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values . The error bars depicted in the figure represent the 95% confidence intervals. Figure S5: Confusion matrices of model testing. Confusion matrices for the targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) model testing. The left-hand top and right-hand bottom numbers represent the positive and negative predictive values, respectively. The numbers on the top and bottom represent the success rate of classifications. Figure S6: Age distribution in Roskams-Hieter et al. and Zhu et al. datasets. Age distribution of female and male healthy donor (HD) and liver cancer (LC) samples in Roskams-Hieter et al. and Zhu et al. datasets (only these datasets contained comprehensive per sample annotations) (A), all samples (B), samples misclassified by the targeted cellular deconvolution model. Figure S7: Influence of sample collection date on model performance. Performance of targeted cellular deconvolution model on Block et al. dataset (only this dataset contained bleed date information): full, after removing liver cancer samples generated after 2010 and after further removing liver cancer samples generated after 2016. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values.
Additional file 2.
Detailed overview of liver cancer patient characteristics across datasets.
Additional file 3.
Results of statistical analyses.
Additional file 4.
Classification and modeling results.
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Safrastyan, A., zu Siederdissen, C.H. & Wollny, D. Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer. Hum Genomics 17, 90 (2023). https://doi.org/10.1186/s40246-023-00537-w
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DOI: https://doi.org/10.1186/s40246-023-00537-w