We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.

Data and pre-trained MEFISTO model to run the vignettes and tutorials provided at https://biofam.github.io/MOFA2/MEFISTO.html.* Evodevo application: Input data is provided as evodevo.csv and evodevo.RData, the trained MEFISTO model is provided in evodevo_model.hdf5 * Longitudinal microbiome application: Input data is provided as microbiome_data.csv and microbiome_features_metadata.csv, the trained MEFISTO model is provided in microbiome_model.hdf5 * single cell multi-omics application: Input data is provided as scnmt_data.txt.gz and scnmt_sample_metadata.txt the trained MEFISTO model is provided in scnmt_mefisto_model.rds * spatial transcriptomics application: Input data is downloaded as described in the tutorial, the trained MEFISTO model is provided in ST_model.hdf5

Single Cell Multi-Omics Market Outlook

As of 2023, the global single cell multi-omics market size is valued at approximately USD 2.5 billion, with a robust projected CAGR of 20.1% forecasted to propel the market to USD 9.8 billion by 2032. This remarkable growth is driven by several key factors, including technological advancements in single-cell analysis techniques, increased funding for omics research, and a growing emphasis on personalized medicine. The market is experiencing a surge in demand as researchers and healthcare providers seek more precise and comprehensive insights into cellular behavior, disease mechanisms, and therapeutic responses. The integration of multi-omics data at a single-cell level offers unparalleled resolution and depth, enabling a transformative understanding of complex biological systems.

One of the primary growth drivers of the single cell multi-omics market is the rapid advancement of technology, particularly in sequencing and analytical tools. Innovations in microfluidics, next-generation sequencing, and enhanced bioinformatics capabilities have significantly lowered the cost and increased the efficiency of single-cell analysis. These technological advancements allow researchers to dissect the heterogeneity of cellular populations with unprecedented precision, facilitating breakthroughs in understanding disease pathology and developing targeted therapeutics. Moreover, the continuous evolution of these technologies fosters their adoption across various fields, further expanding the market's scope and application.

Another significant factor contributing to market growth is the escalating demand for personalized medicine. As the healthcare industry shifts towards more individualized treatment approaches, the need for comprehensive insights at a cellular level becomes paramount. Single cell multi-omics provides a holistic view of cellular function by integrating genomic, transcriptomic, proteomic, and metabolomic data. This integrated approach not only enhances the understanding of disease mechanisms but also aids in the development of personalized therapeutic strategies, thereby driving the adoption of single cell multi-omics in clinical settings. The ability to tailor treatments based on unique cellular profiles is expected to significantly boost market demand over the forecast period.

Additionally, increasing funding and investments in life sciences research is acting as a catalyst for the growth of the single cell multi-omics market. Governments, academic institutions, and private entities are investing heavily in omics research to unlock new scientific insights and address pressing healthcare challenges. This influx of funding is facilitating the establishment of state-of-the-art research facilities and fostering collaborations between academic institutions and industry players. The enhanced research infrastructure and collaborative efforts are expected to accelerate scientific discoveries and propel the market's expansion, as researchers strive to unravel the complexities of biological systems at a single-cell level.

From a regional perspective, North America currently dominates the single cell multi-omics market, owing to its robust research infrastructure, presence of leading biotechnology firms, and substantial government funding for genomics and precision medicine initiatives. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, driven by increasing investments in healthcare research, the rising prevalence of chronic diseases, and the burgeoning biotechnology sector. European countries are also witnessing a growing adoption of single cell multi-omics technologies, supported by collaborative research initiatives and favorable regulatory frameworks. These regional dynamics underscore the diverse growth opportunities within the global market, as stakeholders capitalize on regional strengths and address specific healthcare needs.

Technology Analysis

The technology segment within the single cell multi-omics market is predominantly categorized into single cell genomics, single cell transcriptomics, single cell proteomics, and single cell metabolomics. Each of these sub-segments plays a crucial role in providing comprehensive insights into cellular functions and interactions. Single cell genomics, which involves the analysis of DNA at a single-cell level, has become a cornerstone technology in this market. It enables researchers to investigate genetic variations, mutations, and chromosomal aberrations with unprecedented accuracy. This technology is pivotal in advancing our understanding of genetic predisposit

Additional file 2: Table S2. Summary of datasets used in the study.

Fig 2

Bone marrow (Fig 2B, D, E, F, H, Supplementary Fig 1A, 2,3)

1. Fig 2/BM/Reference/ Fig2_BM_prepare_data.R: Prepare bone marrow for CellFuse

2. Fig 2/BM/ BM_CellFuse_Integration.R: Run CellFuse

3. Fig 2/BM/BM_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN)

4. Fig 2/BM/BM_scIB_Benchmarking.ipynb: evaluate performance of CellFuse and other benchmarking methods using scIB framework proposed by Luecken et al.

5. Fig 2/BM/ BM_scIB_prepare_figures.R: Visualize results of scIB framework

6. Fig 2/BM/Sequential_Feature_drop/Prepare_data.R: Prepare data for evaluating sequential feature drop

7. Fig 2/BM/Sequential_Feature_drop/Run_methods.R: Run CellFuse, Harmony, Seurat and FastMNN for sequential feature drop

8. Fig 2/BM/Sequential_Feature_drop/Evaluate_results.R: Evaluate results features drop and visualize data.

PBMC (Fig 2G,I, Supplementary Fig 1B and 4)

1. Fig 2/PBMC/Reference/ Fig2_PBMC_prepare_data.R: Prepare PBMC data for CellFuse

2. Fig 2/ PBMC / PBMC_CellFuse_Integration.R: Run CellFuse

3. Fig 2/ PBMC /PBMC_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN)

4. Fig 2/ PBMC /PBMC_scIB_Benchmarking.ipynb: evaluate performace of CellFuse and other benchmarking methods using scIB framework proposed by Luecken et al., 2021

5. Fig 2/ PBMC /PBMC_scIB_prepare_figures.R: Visualize results of scIB framework

6. Fig 2/ PBMC/ RunTime_benchmark/Run_Benchmark.R: Prepare data, run benchmarking method and evaluate results.

Fig 3 and Supplementary Fig 5

1. Fig 3/Reference/ Fig3_CyTOF_prepare_data.R: Prepare CyTOF and CITE-Seq data for CellFuse

2. Fig 3/CellFuse_Integration_CyTOF.R: Run CellFuse to remove batch effect and integrate CyTOF data from day 7 post-infusion

3. Fig 3/CellFuse_Integration_CITESeq.R: Run CellFuse to integrate CyTOF and CITE-Seq data

4. Fig 3/CART_Data_visualisation.R: Visualize data

Fig 4

HuBMAP CODEX data (Fig. 4A, B, C, D and Supplementary Fig 6)

1. Fig 4/CODEX_colorectal/Reference/ CODEX_HuBMAP_prepare_data.R: Prepare CODEX data from annotated and unannotated donor

2. Fig 4/ CODEX_colorectal/ CODEX_HuBMAP_CellFuse_Predict.R: Run CellFuse on cells from from annotated and unannotated donor

3. Fig 4/ CODEX_colorectal/CODEX_HuBMAP_Data_visualisation.R: Visualize data and prepare figures.

4. Fig 4/ CODEX_colorectal/ CODEX_HuBMAP_Benchmark.R: Benchmarking CellFuse against CELESTA, SVM and Seurat using cells from annotated donors and prepare figures.

a. Astir is python package so run following python notebook: Fig 4/ CODEX_colorectal/ Benchmarking/Astir/Astrir.ipynb

5. Fig 4/ CODEX_colorectal/CODEX_HuBMAP_Suppl_figure_heatmap.R: F1score calculation per celltype per Benchmarking methods and heatmap comparing celltypes from annotated and unannotated donors (Supplementary Fig 6)

IMC Breast cancer data (Fig. 4E,F, G and Supplementary Fig 7)

1. Fig 4/ IMC_Breast_Cancer/ IMC_prepare_data.R: Prepare CODEX data from annotated and unannotated donor

2. Fig 4/ IMC_Breast_Cancer/ IMC_CellFuse_Predict.R: Run CellFuse to predict cell types

3. Fig 4/ IMC_Breast_Cancer/ IMC_dat_visualization.R: Visualize data and prepare figures.

Fig 5

1. Fig5/ Reference/ Fig5_CyTOF_Data_prep.R: Prepare CyTOF data from healthy PBMC and healthy colon single cells

2. Fig5/ MIBI_CellFuse_Predict.R: Run CellFuse to predicte cells from colon cancer patients

3. Fig5/ MIBI_PostPrediction.R: Visualize data and prepare figures

4. Fig5/ Predicted_Data/ mask_generation.ipynb: Post CellFuse prediction annotated cell types in segmented images. This will generate Fig5C and D

Additional file 3 Table S1. CSV file containing the cell clustering of the mouse cortex data integration.

The global multiomics market, valued at $3.11 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 15.26% from 2025 to 2033. This expansion is driven by several key factors. Advancements in sequencing technologies, particularly next-generation sequencing (NGS), are enabling researchers to analyze multiple omics datasets simultaneously, providing a more comprehensive understanding of complex biological systems. This holistic approach is proving invaluable in drug discovery and development, accelerating the identification of novel therapeutic targets and biomarkers. Furthermore, the increasing prevalence of chronic diseases, such as cancer and neurodegenerative disorders, is fueling demand for more precise diagnostic and therapeutic tools, bolstering the multiomics market. Growing investments in research and development across both academia and the pharmaceutical and biotechnology sectors further contribute to this market's rapid growth. The integration of artificial intelligence (AI) and machine learning (ML) in multiomics data analysis is also significantly impacting the field, enabling faster and more accurate interpretations of complex datasets. The market segmentation reveals significant opportunities across various product types, platforms, and applications. While instruments and reagents constitute major segments, the 'Other Products' category, encompassing software and data analysis tools, is experiencing rapid growth due to the increasing complexity of multiomics data. Single-cell multiomics, offering higher resolution and insights into cellular heterogeneity, is gaining traction over bulk multiomics. Within platforms, genomics maintains a dominant position, followed by transcriptomics and proteomics. However, integrated omics platforms, offering a more comprehensive analysis of multiple datasets simultaneously, are showing significant potential for future growth. Oncology and neurology are leading application areas, with substantial research focused on developing personalized medicine approaches leveraging multiomics data. The academic and research institutes segment remains a key end-user, while pharmaceutical and biotechnology companies are increasingly adopting multiomics for drug discovery and development, promising sustained long-term market growth. Competition among established players like Illumina, Thermo Fisher Scientific, and Agilent Technologies, alongside emerging innovative companies, drives further market dynamism and technological advancement. Recent developments include: February 2024: Vizzhy Inc. launched the world's inaugural Multiomics Lab in Bengaluru, India, heralding a major advancement in healthcare innovation. Equipped with cutting-edge tools and health AI technology, the lab enables physicians to pinpoint root causes and offer personalized recommendations for their patients.September 2023: MGI, a provider of technology and tools for life science, introduced the DCS Lab Initiative to stimulate crucial scientific research. This initiative encourages large-scale multiomics laboratories. Under the initiative, the organization offers products for numerous applications, including cell omics, DNA sequencing, and spatial omics based on DNBSEQ technologies, to specified research institutions globally.April 2023: Biomodal, formerly Cambridge Epigenetix, introduced a new duet multiomics solution that can enable simultaneous phased reading of epigenetic and genetic information in a single, low-volume sample.. Key drivers for this market are: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Potential restraints include: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Notable trends are: The Bulk Multiomics Segment is Expected to Hold the Largest Share of the Market.

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. The development of high-throughput single-cell RNA-sequencing (RNA-seq) technology and the advent of multi-omics have provided a solid basis for a systematic understanding of the heterogeneity in cancers. Although numerous studies have revealed the molecular features of NSCLC, it is important to identify and validate the molecular biomarkers related to specific NSCLC phenotypes at single-cell resolution. In this study, we analyzed and validated single-cell RNA-seq data by integrating multi-level omics data to identify key metabolic features and prognostic biomarkers in NSCLC. High-throughput single-cell RNA-seq data, including 4887 cellular gene expression profiles from NSCLC tissues, were analyzed. After pre-processing, the cells were clustered into 12 clusters using the t-SNE clustering algorithm, and the cell types were defined according to the marker genes. Malignant epithelial cells exhibit individual differences in molecular features and intra-tissue metabolic heterogeneity. We found that oxidative phosphorylation (OXPHOS) and glycolytic pathway activity are major contributors to intra-tissue metabolic heterogeneity of malignant epithelial cells and T cells. Furthermore, we constructed T-cell differentiation trajectories and identified several key genes that regulate the cellular phenotype. By screening for genes associated with T-cell differentiation using the Lasso algorithm and Cox risk regression, we identified four prognostic marker genes for NSCLC. In summary, our study revealed metabolic features and prognostic markers of NSCLC at single-cell resolution, which provides novel findings on molecular biomarkers and signatures of cancers.

This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.

Genetic background is a major driver of the phenotypic variability observed across pluripotent stem cells (PSCs), and studies addressing it have relied on transcript abundance as the primary molecular readout of cell state. However, little is known about how proteins, the functional units in the cell, vary across genetically diverse PSCs and how this relates to variation in other measures of gene output. Here we present the first comprehensive genetic study characterizing the pluripotent proteome using 190 unique mouse embryonic stem cell lines derived from highly heterogeneous Diversity Outbred mice. Moreover, we integrated the proteome with chromatin accessibility and transcript abundance in 163 cell lines with matching genotypes using multi-omics factor analysis to distinguish shared and unique drivers of variability across molecular layers. Our findings highlight the power of multi-omics data integration in revealing the distal impacts of genetic variation. We show that limitations in mapping of individual molecular traits may be overcome by utilizing data integration to consolidate the influence of genetic signals shared across molecular traits and increase detection power.

The Multiomics Market offers a range of products, including instruments, consumables, software, and services. Instruments include sequencing systems, mass spectrometers, and flow cytometers. Consumables encompass reagents, kits, and microarrays. Software solutions provide data analysis and visualization capabilities. Services include sample preparation, data analysis, and interpretation. Recent developments include: September 2023: The chromium single-cell gene expression flex assay manufactured by 10x Genomics Inc. now offers high throughput multi-omic cellular profiling as a commercially available capability thanks to the introduction of a new kit. Researchers and their options may detect simultaneous gene and protein expression, which can be expanded at a greater scale thanks to the new kit, which makes the multi-omic characterization of cell populations simple and efficient. The company's product portfolio was able to grow due to this technique., February 2023: Becton, Dickinson, and Company introduced the Rhapsody HT Xpress System, a high-throughput single-cell multiomics platform, to broaden the field of scientific research. With up to eight times more cells per sample than previous BD single-cell analyzers, this innovative technology allows scientists to extract, label, and analyze individual cells at a high sample throughput. This plan should assist the business in expanding its product's uses and serving more clients.. Notable trends are: Rising integration of multi-omics data is driving the market growth.

Multi-omics datasets, including scRNA-seq, scATAC-seq, and CITE-seq, are used for integration with BiCLUM

Additional file 4 Table S2. CSV file containing the cell clustering of the 10X PBMC integration.

Spatial Multi-Omics Data Integration Software Market Outlook

According to our latest research, the global spatial multi-omics data integration software market size reached USD 392.5 million in 2024, demonstrating robust growth fueled by increasing adoption of multi-omics technologies in biomedical research and clinical practice. The market is projected to expand at a remarkable CAGR of 13.7% during the forecast period, with the value expected to reach approximately USD 1,162.8 million by 2033. This accelerated growth is primarily driven by the surging demand for integrated data solutions to unravel complex biological mechanisms, enhance drug discovery, and enable precision medicine initiatives. As per our latest research, the market’s momentum is underpinned by technological advancements, rising R&D investments, and the growing prevalence of chronic diseases necessitating advanced diagnostic and therapeutic strategies.

One of the primary growth factors propelling the spatial multi-omics data integration software market is the increasing need for comprehensive biological insights at the cellular and tissue levels. The convergence of genomics, transcriptomics, proteomics, metabolomics, and epigenomics data enables researchers and clinicians to capture a multidimensional view of biological systems. This holistic approach is essential for understanding disease heterogeneity, tumor microenvironments, and cellular interactions, particularly in oncology and immunology. The rapid evolution of spatial omics technologies, coupled with the availability of high-throughput sequencing platforms, has generated massive datasets that require sophisticated integration and analysis tools. Consequently, the demand for advanced software solutions capable of harmonizing and interpreting complex multi-omics data is experiencing a significant uptick across both academic and industrial settings.

Another critical driver for the market is the accelerating pace of drug discovery and development, which increasingly relies on spatial multi-omics data integration to identify novel therapeutic targets and biomarkers. Pharmaceutical and biotechnology companies are leveraging these software platforms to streamline the drug development pipeline, reduce attrition rates, and personalize treatment regimens based on patient-specific molecular profiles. The integration of spatial and multi-omics data enhances the ability to predict drug responses, monitor disease progression, and assess therapeutic efficacy in real time. Furthermore, collaborations between software providers, academic institutions, and life science companies are fostering the development of user-friendly, scalable, and interoperable solutions that cater to the evolving needs of end users. This collaborative ecosystem is expected to sustain market growth by facilitating knowledge transfer, standardization, and innovation.

The rising adoption of personalized medicine and precision diagnostics is further fueling the spatial multi-omics data integration software market. As healthcare systems worldwide shift toward individualized care paradigms, there is a growing emphasis on leveraging multi-layered molecular data to inform clinical decision-making. Spatial multi-omics integration software enables clinicians to correlate genetic, transcriptomic, proteomic, and metabolic alterations with spatial context, thereby improving the accuracy of disease classification, prognosis, and therapeutic selection. This paradigm shift is particularly evident in oncology, neurology, and rare disease management, where spatially resolved molecular insights can guide targeted interventions. The increasing prevalence of chronic diseases, aging populations, and the need for early disease detection are expected to drive sustained investments in multi-omics data integration capabilities across healthcare and research institutions.

Regionally, North America continues to dominate the spatial multi-omics data integration software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of leading life science companies, advanced healthcare infrastructure, and substantial government funding for multi-omics research. Europe follows closely, benefiting from strong academic networks and growing investments in precision medicine initiatives. The Asia Pacific region is emerging as a high-growth market, driven by expanding genomics research, increasing healthcare expenditure, and rising awareness of the benefits of integrated omics analyses.

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.

The emerging single-cell technology market is experiencing rapid growth, driven by advancements in genomics, proteomics, and bioinformatics. This technology allows researchers to analyze individual cells, providing unprecedented insights into cellular heterogeneity and function across various biological systems. The market's expansion is fueled by increasing demand for personalized medicine, drug discovery, and disease diagnostics. Applications span oncology, immunology, neuroscience, and infectious diseases, with single-cell RNA sequencing (scRNA-seq) currently dominating the market share. The high cost of instrumentation and data analysis remains a barrier to wider adoption, but ongoing technological innovations are driving down costs and improving accessibility. Furthermore, the development of new analytical tools and bioinformatics pipelines is enhancing data interpretation and accelerating research progress. This burgeoning field is attracting significant investment and collaborative efforts from both established players and innovative startups, fostering a competitive yet collaborative landscape. The projected market growth signifies a transformative impact on healthcare and life sciences, promising significant advancements in disease understanding and treatment. The forecast period from 2025 to 2033 anticipates substantial market expansion, propelled by increasing adoption across research institutions, pharmaceutical companies, and biotechnology firms. Key growth drivers include the development of more affordable and user-friendly single-cell technologies, the integration of multi-omics approaches (combining genomics, proteomics, and metabolomics), and expanding collaborations between academia and industry. Competitive pressures are driving innovation in areas like sample preparation, data analysis software, and the development of novel single-cell applications, such as spatial transcriptomics. Although challenges such as data complexity and the need for specialized expertise persist, the potential for single-cell technologies to revolutionize biological research and healthcare remains immense. This is reflected in the continuous influx of funding and the emergence of new market participants. By 2033, the market is poised to be significantly larger and more diverse, with a wider range of applications and technological advancements shaping the future of biological research and medicine.

The multiomics market is experiencing robust growth, driven by advancements in high-throughput sequencing technologies, decreasing costs, and the increasing need for comprehensive biological insights across various research and clinical applications. The market size in 2025 is estimated at $5.612 billion, representing a significant expansion from previous years. While the exact CAGR is not provided, considering the rapid technological advancements and expanding applications within genomics, proteomics, metabolomics, and transcriptomics, a conservative estimate of the Compound Annual Growth Rate (CAGR) would be between 15% and 20% for the forecast period (2025-2033). This growth is fueled by several key drivers, including the rising prevalence of chronic diseases like cancer and neurological disorders, increasing demand for personalized medicine, and the growing adoption of multiomics approaches in drug discovery and development. Major segments like single-cell multiomics are experiencing particularly high growth due to their ability to provide unparalleled resolution in understanding cellular heterogeneity. The application segments—cell biology, oncology, neurology, and immunology—all contribute significantly to the overall market value, reflecting the broad applicability of multiomics technologies. However, the market also faces certain restraints such as the high cost of instrumentation and data analysis, the complexity of integrating data from multiple omics platforms, and the need for standardized data analysis pipelines. Despite these challenges, the multiomics market is projected to maintain a strong growth trajectory throughout the forecast period. The continued development of more affordable and user-friendly technologies, coupled with the increasing availability of bioinformatics tools and expertise, will contribute to wider adoption across research institutions, pharmaceutical companies, and clinical diagnostic labs. Further advancements in data integration and interpretation techniques will unlock a deeper understanding of complex biological systems, leading to improved diagnostics, therapeutics, and disease prevention strategies. The competitive landscape is characterized by a mix of established players like BD, Thermo Fisher Scientific, Illumina, and Danaher (Beckman Coulter), alongside emerging companies specializing in specific multiomics platforms and analytical solutions. Geographic regions such as North America and Europe currently hold the largest market share, driven by robust research infrastructure and funding, however, Asia Pacific is expected to experience significant growth in the coming years due to rising investments in healthcare and life sciences research.

Market Size and Growth: The global single-cell genome sequencing (SCGS) market size was valued at USD 996.8 million in 2022 and is projected to expand at a CAGR of 8.3% over the forecast period (2023-2030). The rising prevalence of chronic diseases, advancements in sequencing technologies, and increased funding for genomic research drive market growth. Key Drivers and Trends: The SCGS market is primarily driven by the growing need for precision medicine and personalized healthcare. SCGS enables researchers and clinicians to study individual cells, providing insights into disease mechanisms and drug responses. Additionally, technological advancements, such as the development of microfluidic systems and computational tools, are enhancing the accuracy and efficiency of sequencing, further fueling market growth. Key trends include the integration of artificial intelligence (AI) in data analysis, the emergence of single-cell multi-omics approaches, and the development of new sequencing platforms.

This is a collection of datasets presented in the manuscript "Multi-omics data analysis identifies correlations between tumor biology features and predicted behaviors in early lung adenocarcinoma". This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

BackgroundClear cell renal cell carcinoma (ccRCC) relies on pro-survival autophagy for therapy resistance and progression, yet upstream regulators of autophagy initiation, particularly the catalytic core Vps34 (PIK3C3), remain poorly defined. We hypothesized that multi-omics integration could identify novel master regulators of Vps34-driven autophagy in ccRCC.Materials and MethodsWe systematically integrated bulk transcriptomics (TCGA, ICGC, ArrayExpress, Target), single-cell RNA-seq (GSE156632), and spatial transcriptomics (GSE210041). Weighted Gene Co-expression Network Analysis (WGCNA) identified Vps34-correlated modules. Ten machine learning algorithms were employed (including Lasso, RSF, CoxBoost) with 10x cross-validation to build prognostic models. In vitro validation used ccRCC cell lines (769-P, Caki-1, ACHN) and normal renal HK-2 cells. Functional assays included qPCR, Western blot, CCK-8 proliferation, wound healing, and Transwell migration/invasion. siRNA knockdown and lentiviral overexpression manipulated ZBED6 expression.ResultsMulti-omics convergence identified the transcription factor ZBED6 as a top upstream regulator strongly correlated with Vps34 expression. Spatial transcriptomics confirmed co-localization of ZBED6 and Vps34 expression domains. ZBED6 directly promoted Vps34 expression and autophagic flux (increased LC3, decreased p62). WGCNA of scRNA-seq data identified a Vps34-associated blue module; intersecting these genes with differential ccRCC genes yielded 23 candidates. Machine learning (Lasso-SuperPC algorithm) constructed a robust 6-gene prognostic signature (ASAH1, ATP1A1, DSTN, EIF1B, PGK1, SCP2) validated across TCGA, ArrayExpress, and RECA cohorts (AUCs up to 0.88). High-risk patients exhibited enriched EMT, inflammatory pathways, immunosuppression (increased Tregs), and poorer response to anti-PD-1 therapy. Functionally, ZBED6 overexpression significantly enhanced ccRCC cell proliferation, migration, invasion, and autophagy, while knockdown suppressed these phenotypes.ConclusionWe identify ZBED6 as a novel master regulator of autophagy initiation in ccRCC via direct transcriptional control of Vps34. The ZBED6-Vps34 axis is critical for ccRCC survival and aggressiveness. Furthermore, the ZBED6/Vps34-derived 6-gene signature provides a powerful prognostic tool and predicts immunotherapy response. Targeting the ZBED6-Vps34 axis represents a promising therapeutic strategy to disrupt pro-tumorigenic autophagy in ccRCC.