The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.

New technologies enable single-cell transcriptome analysis, mapping genome-wide expression across the human body. Here, we present an extended analysis of protein-coding genes in all major human tissues and organs, combining single-cell and bulk transcriptomics. To enhance transcriptome depth, 31 tissues were analyzed using a pooling method, identifying 557 unique cell clusters, manually annotated by marker gene expression. Genes were classified by body-wide expression and validated through antibody-based profiling. All results are available in the updated open-access Single Cell Type section of the Human Protein Atlas for genome-wide exploration of genes, proteins, and their spatial distribution in cells.

Chronic hepatic injury and inflammation from various causes can lead to fibrosis and cirrhosis, potentially predisposing to hepatocellular carcinoma. The molecular mechanisms underlying fibrosis and its progression remain incompletely understood. Using a proteo-transcriptomics approach, we analyze liver and plasma samples from 330 individuals, including 40 healthy individuals and 290 patients with histologically characterized fibrosis due to chronic viral infection, alcohol consumption, or metabolic dysfunction-associated steatotic liver disease. Our findings reveal dysregulated pathways related to extracellular matrix, immune response, inflammation, and metabolism in advanced fibrosis. We also identify 132 circulating proteins associated with advanced fibrosis, with neurofascin and growth differentiation factor 15 demonstrating superior predictive performance for advanced fibrosis(area under the receiver operating characteristic curve [AUROC] 0.89 [95% confidence interval (CI) 0.81–0.97]) compared to the fibrosis-4 model (AUROC 0.85 [95% CI 0.78–0.93]). These findings provide insights into fibrosis pathogenesis and highlight the potential for more accurate non-invasive diagnosis.

Background Clear cell renal carcinoma (ccRCC) is the predominant form of kidney cancer, but the prognostic value of expression quantitative trait loci (eQTLs) remains underexplored, particularly in Asian populations. Objective We analyzed whole-exome sequencing and RNA sequencing data from 100 Japanese ccRCC patients to identify eQTLs. Multiple Cox proportional hazard models assessed survival associations, with validation in the Cancer Genome Atlas ccRCC cohort (n = 287). Results We identified 805 eGenes and 4,558 cis-eQTLs in the Japanese cohort. Survival analysis revealed a total of 9 eGenes significantly associated with overall survival (FDR < 0.05). Further exploratory analysis were performed using 158 eGenes and 711 eQTLs (p-value <0.05) as potential prognostic signals. Among these, 223 eQTLs regulating 54 eGenes showed consistent prognostic effects at both expression and genetic levels. Cross-population validation identified eight eQTLs regulating 11 eGenes with reproducible survival associations across ethnicities, including a missense mutation in ERV3–1 and regulatory variants near ANKRD20A7P. These variants demonstrated consistent allelic effects on both gene expression and patient survival in both cohorts.

Pyruvate kinase is a key regulator in hepatic glucose metabolism, encoded by the gene pyruvate kinase liver/red blood cells (PKLR). Systems biology-based approaches, including metabolic and gene co-expression networks analyses, as well as genome-wide association studies (GWAS), have led to the identification of PKLR as a pivotal gene influencing liver metabolism in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and hepatocellular carcinoma (HCC). Here, we review the critical role of PKLR in MASLD and HCC progression and examine the effects of PKLR modulation both in vitro and in vivo. We also discuss the development of therapeutic strategies for patients with MASLD and HCC by modulating PKLR, highlighting its promising future in a broader range of liver diseases.

Background: Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding and the development of personalized treatments. The present study aims to establish a solid association between gene expression and patient survival outcomes to enhance the utility of the Human Pathology Atlas for cancer research. Methods: In this updated analysis, we examined the expression profiles of 6918 patients across 21 cancer types. We integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. We applied systems biology approach to identify the association between gene expression profiles and patient survival outcomes. We further constructed prognostic regulatory networks for kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC), which elucidate the molecular underpinnings associated with patient survival in these cancers. Findings: We observed that gene expression during the transition from normal to tumorous tissue exhibited diverse shifting patterns in their original tissue locations. Significant correlations between gene expression and patient survival outcomes were identified in KIRC and LIHC among the major cancer types. Additionally, the prognostic regulatory network established for these two cancers showed the indicative capabilities of the Human Pathology Atlas and provides actionable insights for cancer research. Interpretation: The updated Human Pathology Atlas provides a significant foundation for precision oncology and the formulation of personalized treatment strategies. These findings deepen our understanding of cancer biology and have the potential to advance targeted therapeutic approaches in clinical practice. Funding: The Knut and Alice Wallenberg Foundation ( 72110), the China Scholarship Council (Grant No. 202006940003).

Introduction: Macrophages and T cells play crucial roles in liver physiology, but their functional diversity in hepatocellular carcinoma (HCC) remains largely unknown. Methods: Two bulk RNA-sequencing (RNA-seq) cohorts for HCC were analyzed using gene co-expression network analysis. Key gene modules and networks were mapped to single-cell RNA-sequencing (scRNA-seq) data of HCC. Cell type fraction of bulk RNA-seq data was estimated by deconvolution approach using single-cell RNA-sequencing data as a reference. Survival analysis was carried out to estimate the prognosis of different immune cell types in bulk RNA-seq cohorts. Cell-cell interaction analysis was performed to identify potential links between immune cell types in HCC. Results: In this study, we analyzed RNA-seq data from two large-scale HCC cohorts, revealing a major and consensus gene co-expression cluster with significant implications for immunosuppression. Notably, these genes exhibited higher enrichment in liver macrophages than T cells, as confirmed by scRNA-seq data from HCC patients. Integrative analysis of bulk and single-cell RNA-seq data pinpointed SPP1+ macrophages as an unfavorable cell type, while VCAN+ macrophages, C1QA+ macrophages, and CD8+ T cells were associated with a more favorable prognosis for HCC patients. Subsequent scRNA-seq investigations and in vitro experiments elucidated that SPP1, predominantly secreted by SPP1+ macrophages, inhibits CD8+ T cell proliferation. Finally, targeting SPP1 in tumor-associated macrophages through inhibition led to a shift towards a favorable phenotype. Discussion: This study underpins the potential of SPP1 as a translational target in immunotherapy for HCC.

In the context of rising global health challenges, the mechanistic investigation and

treatment of complex diseases, including cancer, liver diseases, has emerged as a

vital focus in scientific research. A thorough understanding of basic biological

processes is crucial for the development of tools that aid in diagnosing, monitoring,

and treating human diseases. This doctoral thesis investigates the molecular

mechanisms underlying complex human diseases, with an emphasis on discovering

novel therapeutic targets and compounds though systems biology approaches. By

leveraging large-scale transcriptomic data, this work aims to uncover novel insights

into disease biology that can drive drug repositioning and precision medicine. The

thesis integrates various computational strategies and biological frameworks to

connect gene expression patterns with disease progression and therapeutic

opportunities, focusing primarily on cancer and metabolic disorders.

The studies compiled in this thesis contribute to the understanding of human disease

biology through the systematic analysis of gene expression profiles and the

application of network-based methodologies. Paper I introduces the Human

Pathology Atlas, providing an in-depth analysis of gene expression prognostic

features across different cancer types, which improves our understanding of

relationships between gene expression and disease outcomes. Paper II and Paper

III employ gene co-expression network analysis combined with drug repositioning

strategies, identifying promising therapeutic candidates for hepatocellular

carcinoma and pancreatic ductal adenocarcinoma, respectively. These studies

illustrate how network-based approaches can locate key molecular targets and

potential repurposable drugs for various cancer types.

In Paper IV, we apply a network-based approach to investigate the dysregulated

transcriptional regulation in non-alcoholic fatty liver disease (NAFLD). This study

identifies critical genes and pathways involved in the disease progression, providing

new insights into the pathophysiology of NAFLD. Lastly, Paper V presents

comprehensive review on the emerging role of PKLR in liver diseases, highlighting

its connection to metabolic diseases. This review discusses PKLR’s potential as a

therapeutic target, providing a foundation for future studies in metabolic disease

research.

In summary, this thesis contributes to the field of systems biology by integrating

gene expression and network methodologies, offering innovative strategies for

therapeutic development and personalized medicine across complex diseases.

I samband med ökande globala hälsoutmaningar har den mekanistiska

undersökningen och behandlingen av komplexa sjukdomar, inklusive cancer och

leversjukdomar, blivit ett viktigt fokus inom vetenskaplig forskning. En djupgående

förståelse av grundläggande biologiska processer är avgörande för utvecklingen av

verktyg som hjälper till att diagnostisera, övervaka och behandla mänskliga

sjukdomar. Denna doktorsavhandling undersöker de molekylära mekanismerna

bakom komplexa mänskliga sjukdomar, med betoning på att upptäcka nya

terapeutiska mål och substanser genom systembiologiska tillvägagångssätt. Genom

att utnyttja storskaliga transkriptomiska data syftar detta arbete till att avslöja nya

insikter i sjukdomsbiologin som kan driva läkemedelsompositionering och

precisionsmedicin. Avhandlingen integrerar olika beräkningsstrategier och

biologiska ramverk för att koppla genuttrycksmönster till sjukdomsutveckling och

terapeutiska möjligheter, med fokus främst på cancer och metabola sjukdomar.

Studierna som samlats i denna avhandling bidrar avsevärt till förståelsen av

mänsklig sjukdomsbiologi genom systematisk analys av genuttrycksprofiler och

tillämpning av nätverksbaserade metoder. Paper I introducerar Human Pathology

Atlas och ger en djupgående analys av prognostiska genuttrycksdrag i olika

cancertyper, vilket förbättrar vår förståelse av sambanden mellan genuttryck och

sjukdomsutfall. Paper II och Paper III använder genko-

expressionsnätverksanalys kombinerat med läkemedelsompositionering för att

identifiera lovande terapeutiska kandidater för hepatocellulärt karcinom och

pankreatiskt duktalt adenokarcinom. Dessa studier visar hur nätverksbaserade

metoder kan lokalisera viktiga molekylära mål och potentiella återanvändbara

läkemedel för olika cancerformer.

I Paper IV tillämpas ett nätverksbaserat tillvägagångssätt för att undersöka den

dysreglerade transkriptionella regleringen vid icke-alkoholisk fettlever (NAFLD).

Denna studie identifierar kritiska gener och vägar som är involverade i sjukdomens

utveckling och ger nya insikter i NAFLD patofysiologi. Slutligen presenterar Paper

V en omfattande översikt över den framväxande rollen för PKLR i leversjukdomar

och betonar dess koppling till metabola sjukdomar. Denna översikt diskuterar PKLR

potential som ett terapeutiskt mål och ger en grund för framtida studier inom

metabol sjukdomsforskning.

Sammanfattningsvis bidrar denna avhandling till området systembiologi genom att

integrera genuttryck och nätverksmetoder och erbjuda innovativa strategier för

terapeutisk utveckling och personanpassad medicin inom komplexa sjukdomar.

Background: Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients. Methods: Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins. Findings: We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments. Interpretation: We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.