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: 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.

Combination therapies targeting multiple organs and metabolic pathways are promising therapeutic options to combat obesity progression and/or its comorbidities. The alterations in the composition of the gut microbiota initially observed in obesity have been extended recently to functional alterations. Bacterial functions involve metabolite synthesis that may contribute to both the gut microbiota and the host physiology. Among them are B vitamins, whose metabolism at the systemic, tissue, or microbial level is dysfunctional in obesity. We previously reported that the combination of oral supplementation of a prebiotic (fructo-oligosaccharides, FOS) and vitamin B7/B8 (biotin) impedes fat mass accumulation and hyperglycemia in mice with established obesity. This was associated with an attenuation of dysbiosis with improved microbial vitamin metabolism. We now extend this study by characterizing whole body energy metabolism along with adipose tissue transcriptome and histology in this mouse model. We observed that FOS resulted in increased caloric excretion in parallel with downregulation of genes and proteins involved in jejunal lipid transport. The combined treatments also strongly inhibited the accumulation of subcutaneous fat mass, with a reduced adipocyte size and expression of lipid metabolism genes. Downregulation of inflammatory and fibrotic genes and proteins was also observed in both visceral and brown adipose tissues and liver by combined FOS and biotin supplementation. In conclusion, oral administration of a prebiotic and biotin has a beneficial impact on the metabolism of key organs involved in the pathophysiology of obesity, which could have promising translational applications.

Simple Summary Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results. Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Summary

Background: The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.

Methods: We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA-and drug-perturbed signature profiles of human kidney cell line.

Findings: First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.

Interpretation: These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.

Funding: This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA. 

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genomescale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.