Fibroinflammation refers to the highly integrated fibrogenic and inflammatory responses mediated by the concerted function of fibroblasts and innate immune cells in response to tissue perturbation. This process underlies the desmoplastic remodelling of the tumour microenvironment and thus plays an important role in tumour initiation, growth and metastasis. More specifically, fibroinflammation alters the biochemical and biomechanical signalling in malignant cells to promote their proliferation and survival and further supports an immunosuppressive microenvironment by polarizing the immune status of tumours. Additionally, the presence of fibroinflammation is often associated with therapeutic resistance. As such, there is increasing interest in targeting this process to normalize the tumour microenvironment and thus enhance the treatment of solid tumours. Herein, we review advances made in unravelling the complexity of cancer-associated fibroinflammation that can inform the rational design of therapies targeting this.

Fibroblasts play essential roles in cancer progression, exhibiting activation states that can either promote or inhibit tumor growth. Understanding these differential activation states is critical for targeting the tumor microenvironment (TME) in cancer therapy. However, traditional molecular markers used to identify cancer-associated fibroblasts are limited by their co-expression across multiple fibroblast subtypes, making it difficult to distinguish specific activation states. Morphological and motility characteristics of fibroblasts reflect their underlying gene expression patterns and activation states, making these features valuable descriptors of fibroblast behavior. This study proposes an artificial intelligence-based classification framework to identify and characterize differentially activated fibroblasts by analyzing their morphodynamic and motile features. We extract these features from label-free live-cell imaging data of fibroblasts co-cultured with breast cancer cell lines using deep learning and machine learning algorithms. Our findings show that morphodynamic and motile features offer robust insights into fibroblast activation states, complementing molecular markers and overcoming their limitations. This biophysical state-based cellular classification framework provides a novel, comprehensive approach for characterizing fibroblast activation, with significant potential for advancing our understanding of the TME and informing targeted cancer therapies.

Single-cell RNA sequencing measures individual cell transcriptomes in a sample. In the past decade, this technology has motivated the development of hundreds of clustering methods. These methods attempt to group cells into populations by leveraging the similarity of their transcriptomes. Because each method relies on specific hypotheses, their predictions can vary drastically. To address this issue, ensemble algorithms detect cell populations by integrating multiple clustering methods, and minimizing the differences of their predictions. While this approach is sensible, it has yet to address some conceptual challenges in single-cell data science; namely, ensemble algorithms have yet to generate clustering results with uncertainty values and multiple resolutions. In this work, we present an original approach to ensemble clustering that addresses these challenges, by describing the differences between clustering results, rather than minimizing them. We present the scEVE algorithm, and we evaluate it on 15 experimental datasets, and up to 1200 synthetic datasets. Our results reveal that scEVE outperforms the state of the art, and addresses both conceptual challenges. We also highlight how biological downstream analyses will benefit from addressing these challenges. We expect that this work will provide an alternative direction for developing single-cell ensemble clustering algorithms.

Colorectal cancer (CRC) continues to be a major worldwide health issue, with elevated death rates linked to late stages of the illness. Immunotherapy has made significant progress in developing effective techniques to improve the immune system's capacity to identify and eradicate cancerous cells. This study examines the most recent advancements in CAR-T cell treatment and exosome-based immunotherapy for CRC. CAR-T cell therapy, although effective in treating blood cancers, encounters obstacles when used against solid tumours such as CRC. These obstacles include the presence of an immunosuppressive tumour microenvironment and a scarcity of tumour-specific antigens. Nevertheless, novel strategies like dual-receptor CAR-T cells and combination therapy involving cytokines have demonstrated promise in surmounting these obstacles. Exosome-based immunotherapy is a promising approach for targeted delivery of therapeutic drugs to tumour cells, with high specificity and minimal off-target effects. However, there are still obstacles to overcome in the field, such as resistance to treatment, adverse effects associated with the immune system, and the necessity for more individualised methods. The current research is focused on enhancing these therapies, enhancing the results for patients, and ultimately incorporating these innovative immunotherapeutic approaches into the standard treatment protocols for CRC.

Colorectal cancer (CRC) ranks among the leading causes of cancer-related morbidity and mortality worldwide. Despite progress in understanding its molecular intricacies, the management of CRC, especially in advanced stages, remains a significant clinical hurdle. This review delves into the evolving landscape of stem cell-based therapeutic strategies in CRC, with a specific focus on the interplay between cancer stem cells (CSCs) and CRC pathogenesis and treatment resistance. Highlighting the pivotal roles of CSCs in tumor initiation, progression, metastasis, and recurrence, the review comprehensively examines their involvement in CRC, ranging from normal colonic tissue to cancer initiation. The potential of stem cells for medicinal purposes in CRC management is explored, encompassing diverse modalities such as transplantation, differentiation therapy, immunotherapy, and gene/cell-based approaches. Challenges and opportunities associated with these strategies are also evaluated, providing insights into their clinical potential and limitations. The review also appraises preclinical investigations contributing to the understanding of CRC and stem cells. Current clinical trials, patient stratification strategies, and regulatory considerations related to stem cell-based therapies in CRC are scrutinized. Furthermore, the review explores emerging trends and future directions, including developments in stem cell technologies and ethical considerations. It highlights the transformative potential of stem cell-based therapeutic strategies in CRC.

Cancer-associated fibroblasts (CAFs) are a multifaceted cell population essential for shaping the tumor microenvironment (TME) and influencing therapy responses. Characterizing the spatial organization and interactions of CAFs within complex tissue environments provides critical insights into tumor biology and immunobiology. In this study, through integrative analyses of over 14 million cells from 10 cancer types across 7 spatial transcriptomics and proteomics platforms, we discover, validate, and characterize four distinct spatial CAF subtypes. These subtypes are conserved across cancer types and independent of spatial omics platforms. Notably, they exhibit distinct spatial organizational patterns, neighboring cell compositions, interaction networks, and transcriptomic profiles. Their abundance and composition vary across tissues, shaping TME characteristics, such as levels, distribution, and state composition of tumor-infiltrating immune cells, tumor immune phenotypes, and patient survival. This study enriches our understanding of CAF spatial heterogeneity in cancer and paves the way for novel approaches to target and modulate CAFs.

Molecular heterogeneity exists in many biological systems, such as major malignancies or diverse cell populations. Clustering of gene expression profiles has been widely used to dissect molecular heterogeneity. One drawback common to most clustering methods is that they often suffer from high dimensionality and noise, as well as feature redundancy. To address these challenges, we propose Extreme learning machine self-diffusion (ELMSD), an auto-encoder extreme learning machine feature representation method that incorporates a self-diffusion graph denoising framework to effectively dissect molecular heterogeneity. Our method, ELMSD, first learns a compressed representation of gene expression profiles from the hidden layer of the autoencoder extreme learning machine, followed by an iterative graph diffusion process to enhance the sample-to-sample similarity. The enhanced graph can largely facilitate the downstream clustering analysis, making it more efficient to analyze molecular properties. To demonstrate the utility of ELMSD, we applied it on one simulation dataset, five single-cell datasets, and 20 cancer datasets. Experiment results show that the ELMSD approach outperforms several state-of-the-art clustering methods and cancer subtypes, cell types identified by ELMSD reveal strong clinical relevance and biological interpretation. The ELMSD code is available at: https://github.com/DXCODEE/ELMSD.

Understanding the proteomic-level heterogeneity of the tumor microenvironment (TME) in colorectal cancer (CRC) is crucial due to its well-known heterogeneity. While heterogenous CRC has been extensively characterized at the molecular subtype level, research into the functional heterogeneity of fibroblasts, particularly their relationship with extracellular matrix (ECM) alterations, remains limited. Addressing this gap is essential for a comprehensive understanding of CRC progression and the development of targeted therapies.

24 tissue samples from 21 CRC patients, along with adjacent normal tissues (NAT), were collected and decellularized using a detergent-based method to enrich the ECM component. Proteomic analysis of ECM-enriched samples was performed using tandem mass tag (TMT) spectrometry, followed by statistical analysis including differential expression protein (DEP) analysis. Single-cell RNA sequencing (scRNA-Seq) data from public datasets were integrated and analyzed to delineate cell states within the TME. Bulk tissue RNA-Seq and bioinformatics analysis, including consensus molecular subtype (CMS) classification and single-cell level deconvolution of TCGA bulk RNA-seq data, were conducted to further explore gene expression patterns and TME composition.

Differential cellular origin of the NAT and tumorous ECM proteins were identified, revealing 110 ECM proteins enriched in NAT and 28 ECM proteins in tumor tissues. Desmoplastic and WNT5A+ inflammatory fibroblasts were indicated as the sources of tumor-enriched ECM proteins, while ADAMDEC1+ expressing fibroblasts and PI16+ expressing fibroblast were identified as the sources of NAT-enriched ECM proteins. Deconvolution of bulk RNA-seq of CRC tissues discriminated CMS-specific fibroblast state, reflecting the biological traits of each CMS subtype. Specially, seven ECM genes specific to mesenchymal subtype (CMS4), including PI16+ fibroblast-related 4 genes (SFRP2, PRELP, OGN, SRPX) and desmoplastic fibroblast-related 3 genes (THBS2, CTHRC1, BGN), showed a significant association with poorer survival in patient with CRC.

We conducted an extracellular matrix (ECM)-focused profiling of the TME by integrating quantitative proteomics with single-cell RNA sequencing (scRNA-seq) data from CRC patients. We identified the ECM proteins of NAT and tumor tissue, and established a cell-matrisome database. We defined mesenchymal subtype-specific molecules associated with specific fibroblast subtypes showing a significant association with poorer survival in patients with CRC. Our ECM-focused profiling of tumor stroma provides new insights as indicators for biological processes and clinical endpoints.

Head and neck cancer (HNC) is the sixth most common disease in the world. The recurrence rate of patients is relatively high, and the heterogeneity of tumor immune microenvironment (TIME) cells may be an important reason for this. Single-cell sequencing (SCS) is currently the most promising and mature application in cancer research. It can identify unique genes expressed in cells and study tumor heterogeneity. According to current research, the heterogeneity of immune cells has become an important factor affecting the occurrence and development of HNC. SCSs can provide effective therapeutic targets and prognostic factors for HNC patients through analyses of gene expression levels and cell heterogeneity. Therefore, this study analyzes the basic theory of HNC and the development of SCS technology, elaborating on the application of SCS technology in HNC and its potential value in identifying HNC therapeutic targets and biomarkers.

Cancer-associated fibroblasts (CAFs) are tissue residing cells within the tumor microenvironment (TME). Stromal CAFs have been shown to be associated with poor prognosis and tumor progression in several solid tumor entities. Although the molecular mechanisms are not fully understood yet, a critical role within the TME through direct interaction with the tumor cells as well as other cells has been proposed. While most studies on CAFs focus on stromal CAFs, recent reports highlight the possibility of detecting circulating CAFs (cCAFs) in the blood. In contrast to invasive tissue biopsies for stromal CAF characterization, liquid biopsy allows a minimally invasive isolation of cCAFs. Furthermore, liquid biopsy methods could enable continuous monitoring of cCAFs in cancer patients and therefore may present a novel biomarker for solid tumors. In this work, we present an overview of cCAF studies currently available and summarize the liquid biopsy techniques for cCAF isolation and detection. Moreover, the future research directions in the emerging field are highlighted and the potential applications of cCAFs as novel biomarkers for solid tumor patients discussed.

We present hypergraph variational autoencoder (HyperG-VAE), a Bayesian deep generative model that leverages hypergraph representation to model single-cell RNA sequencing (scRNA-seq) data. The model features a cell encoder with a structural equation model to account for cellular heterogeneity and construct gene regulatory networks (GRNs) alongside a gene encoder using hypergraph self-attention to identify gene modules. The synergistic optimization of encoders via a decoder improves GRN inference, single-cell clustering, and data visualization, as validated by benchmarks. HyperG-VAE effectively uncovers gene regulation patterns and demonstrates robustness in downstream analyses, as shown in B cell development data from bone marrow. Gene set enrichment analysis of overlapping genes in predicted GRNs confirms the gene encoder's role in refining GRN inference. Offering an efficient solution for scRNA-seq analysis and GRN construction, HyperG-VAE also holds the potential for extending GRN modeling to temporal and multimodal single-cell omics.

Wnt5a, a representative Wnt ligand that activates the β-catenin-independent pathway, has been shown to promote tumorigenesis. However, it is unclear where Wnt5a is produced and how it affects colon cancer aggressiveness. In this study, we demonstrate that Wnt5a is expressed in fibroblasts near the luminal side of the tumor, and its depletion suppresses mouse colon cancer formation. To characterize the specific fibroblast subtype, a meta-analysis of human and mouse colon fibroblast single-cell RNA-seq data is performed. The results show that Wnt5a is expressed in hypoxia-induced inflammatory fibroblast (InfFib), accompanied by the activation of HIF2. Moreover, Wnt5a maintains InfFib through the suppression of angiogenesis mediated by soluble VEGF receptor1 (Flt1) secretion from endothelial cells, thereby inducing further hypoxia. InfFib also produces epiregulin, which promotes colon cancer growth. Here, we show that Wnt5a acts on endothelial cells, inducing a hypoxic environment that maintains InfFib, thereby contributing to colon cancer progression through InfFib.

The relationships of human diversity with biomedical phenotypes are pervasive yet remain understudied, particularly in a single-cell genomics context. Here, we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 donors, spanning 7 population groups across 5 Asian countries, and 6 controls. Though population groups are frequently compared at the continental level, we found that sub-continental diversity, age, and sex pervasively impacted cellular and molecular properties of immune cells. These included differential abundance of cell neighborhoods as well as cell populations and genes relevant to disease risk, pathogenesis, and diagnostics. We discovered functional genetic variants influencing cell-type-specific gene expression, which were under-represented in non-Asian populations, and helped contextualize disease-associated variants. AIDA enables analyses of multi-ancestry disease datasets and facilitates the development of precision medicine efforts in Asia and beyond.

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

Breast cancer bone metastasis is a major cause of mortality in patients with advanced breast cancer. Although decreased mineral density is a known risk factor for bone metastasis, the underlying mechanisms remain poorly understood because studying the isolated effect of bone mineral density on tumor heterogeneity is challenging with conventional approaches. Moreover, mineralized biomaterials are commonly utilized for clinical bone defect repair, but how mineralized biomaterials affect the foreign body response and wound healing is unclear. Here, we investigate how bone mineral affects tumor growth and microenvironmental complexity in vivo by combining single-cell RNA-sequencing with mineral-containing or mineral-free decellularized bone matrices. We discover that the absence of bone mineral significantly influences fibroblast and immune cell heterogeneity, promoting phenotypes that increase tumor growth and alter the response to injury or disease. Importantly, we observe that the stromal response to bone mineral content depends on the murine tumor model used. While lack of bone mineral induces tumor-promoting microenvironments in both immunocompromised and immunocompetent animals, these changes are mediated by altered fibroblast phenotype in immunocompromised mice and macrophage polarization in immunocompetent mice. Collectively, our findings suggest that bone mineral density affects tumor growth by impacting microenvironmental complexity in an organism-dependent manner.

Cancer-associated fibroblasts (CAFs) significantly influence tumor progression and therapeutic resistance in colorectal cancer (CRC). However, the distributions and functions of CAF subpopulations vary across the four consensus molecular subtypes (CMSs) of CRC. This study performed single-cell RNA and bulk RNA sequencing and revealed that myofibroblast-like CAFs (myCAFs), tumor-like CAFs (tCAFs), inflammatory CAFs (iCAFs), CXCL14+CAFs, and MT+CAFs are notably enriched in CMS4 compared with other CMSs of CRC. Multiplex immunohistochemistry was used to validate the distribution of CAF subtypes in patients with different CMSs. Prognosis-related CAF subtypes were identified, leading to the selection of four key genes (COL3A1, COL1A2, GEM, and TMEM47). Through machine learning, we developed a CAF poor-prognosis gene (CAFPRG) model to predict outcomes of patients with CMS4. High levels of CAFPRGs were identified as independent poor-risk factors for prognosis (p < 0.001). Tumors with elevated CAFPRGs exhibited increased infiltration of immune-suppressive cells and resistance to chemotherapy. The expression of these key genes was confirmed to be significantly higher in CAFs than in normal fibroblasts (NFs). Therefore, CAFPRGs may be valuable for precisely predicting patient survival and may present potential therapeutic opportunities for CMS4 CRC.

In the course of tumor treatment, radiation therapy (RT) not only kills cancer cells, but also induces complex biological effects in non-malignant cells around cancer cells. These biological effects such as angiogenesis, changes in stromal composition and immune cell infiltration remodel the tumor microenvironment (TME). As one of the major components of the TME, Cancer‑associated fibroblasts (CAFs) are not only involved in tumorigenesis, progression, recurrence, and metastasis but also regulate the tumor-associated immune microenvironment. CAFs and tumor cells or immune cells have complex intercellular communication in the context of tumor radiation.

Different cellular precursors, spatial location differences, absence of specific markers, and advances in single-cell sequencing technology have gradually made the abundant heterogeneity of CAFs well known. Due to unique radioresistance properties, CAFs can survive under high doses of ionizing radiation. However, radiation can induce phenotypic and functional changes in CAFs and further act on tumor cells and immune cells to promote or inhibit tumor progression. To date, the effect of RT on CAFs and the effect of irradiated CAFs on tumor progression and TME are still not well defined.

In this review, we review the origin, phenotypic, and functional heterogeneity of CAFs and describe the effects of RT on CAFs, focusing on the mutual crosstalk between CAFs and tumor or immune cells after radiation. We also discuss emerging strategies for targeted CAFs therapy.

The ARP2/3 complex, which orchestrates actin cytoskeleton organization and lamellipodia formation, has been implicated in the initiation of pancreatic ductal adenocarcinoma (PDAC). This study aims to clarify its impact on the activity of cancer-associated fibroblasts (CAFs), key players in PDAC progression, and patient outcomes. Early pancreatic carcinogenesis was modeled in p48Cre; LSL-KrasG12D mice with caerulein-induced pancreatitis, complemented by in vitro studies on human immortalized pancreatic stellate cells (PSCs) and primary PDAC-derived CAFs. Data were gained from microarray analysis, RNA sequencing (RNA-seq), and single-cell RNA sequencing (sc-RNA-seq), with subsequent bioinformatics analysis. We uncovered a specific transcriptional signature associated with fibroblast migration in early pancreatic carcinogenesis and linked it to poor survival in patients with PDAC. A pivotal role of the ARP2/3 complex in CAF migration was identified. Inhibition of the ARP2/3 complex markedly decreased CAF motility and induced significant morphological changes in vitro. Furthermore, its inhibition also hindered TGFβ1-mediated myofibroblastic CAF differentiation but had no effect on IL-1-mediated inflammatory CAF differentiation. Our findings position the ARP2/3 complex as central to the migration and differentiation of myofibroblastic CAF. Targeting this complex presents a promising new therapeutic avenue for PDAC treatment.

Lung squamous cell carcinoma (LUSC) is basal-like subtype of lung cancer with limited treatment options. While prior studies have identified tumor-propagating cell states in squamous tumors, the broader landscape of intra-tumoral heterogeneity within LUSC remains poorly understood. Here, we employ Sox2-driven mouse models, organoid cultures, and single-cell transcriptomic analyses to uncover previously unrecognized levels of cell fate diversity within LUSC. Specifically, we identify a KRT13+ hillock-like population of slower-dividing tumor cells characterized by immunomodulatory gene expression signatures. The tumor hillock-like state is conserved across multiple animal models and is present in the majority of human LUSCs as well as head and neck and esophageal squamous tumors. Our findings shed light on the cellular origins of lung hillock-like states: normal club cells give rise to tumors with luminal hillock-like populations, while basal-like tumor-propagating cells transition into basal hillock-like states, resembling homeostatic cellular responses to lung injury. Mechanistically, we identify KLF4 as a key transcriptional regulator of the hillock-like state, both necessary and sufficient to induce KRT13 expression. Together, these results provide new molecular insights into cell fate plasticity that underlies intra-tumoral heterogeneity in LUSC, offering potential avenues for new therapeutic strategies.

The tumor microenvironment is a dynamic and complex three-dimensional network comprising the extracellular matrix and diverse non-cancerous cells, including fibroblasts, adipocytes, endothelial cells and various immune cells (lymphocytes T and B, NK cells, dendritic cells, monocytes/macrophages, myeloid-derived suppressor cells, and innate lymphoid cells). A constantly and rapidly growing number of studies highlight the critical role of these cells in shaping cancer survival, metastatic potential and therapy resistance. This review provides a synthesis of current knowledge on the modulating role of the cellular microenvironment in cancer progression and response to treatment.

The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a large scale. While these datasets offer opportunities for subtype discovery, they also pose challenges in finding subtype signatures due to their high dimensionality. Feature selection, a key step in the machine learning pipeline, involves selecting signatures that reduce feature size for more efficient downstream computational analysis. Although many existing methods focus on selecting features that differentiate known diseases or cell states, they often struggle to identify features that both preserve heterogeneity and reveal subtypes. To address this, we utilized deep metric learning-based feature embedding to explore the statistical properties of features crucial for preserving heterogeneity. Our analysis indicated that features with a notable difference in interquartile range (IQR) between classes hold important subtype information. Guided by this insight, we developed a statistical method called PHet (Preserving Heterogeneity), which employs iterative subsampling and differential analysis of IQR combined with Fisher's method to identify a small set of features that preserve heterogeneity and enhance subtype clustering quality. Validation on public single-cell RNA-seq and microarray datasets demonstrated PHet's ability to maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods. Furthermore, an analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells identified two distinct basal cell subtypes differentiating towards a luminal secretory phenotype using PHet-based features, demonstrating promising results in a real-data application. These results highlight PHet's potential to enhance our understanding of disease mechanisms and cell differentiation, contributing significantly to the field of personalized medicine.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by providing gene expression data at the single-cell level. Unlike bulk RNA-seq, scRNA-seq allows identification of different cell types within a given tissue, leading to a more nuanced comprehension of cell functions. However, the analysis of scRNA-seq data presents challenges due to its sparsity and high dimensionality. Since bioinformatics plays an important role in the analysis of big data and its utility for the welfare of living beings, it has been widely applied in analyzing scRNA-seq data. To address these challenges, we introduce the scMUG computational pipeline, which incorporates gene functional module information to enhance scRNA-seq clustering analysis. The pipeline includes data preprocessing, cell representation generation, cell-cell similarity matrix construction, and clustering analysis. The scMUG pipeline also introduces a novel similarity measure that combines local density and global distribution in the latent cell representation space. As far as we can tell, this is the first attempt to integrate gene functional associations into scRNA-seq clustering analysis. We curated nine human scRNA-seq datasets to evaluate our scMUG pipeline. With the help of gene functional information and the novel similarity measure, the clustering results from scMUG pipeline present deep insights into functional relationships between gene expression patterns and cellular heterogeneity. In addition, our scMUG pipeline also presents comparable or better clustering performances than other state-of-the-art methods. All source codes of scMUG have been deposited in a GitHub repository with instructions for reproducing all results (https://github.com/degiminnal/scMUG).

Cancer-associated fibroblasts (CAFs) are key components of the tumor microenvironment (TME). Given their various roles in tumor progression and treatment resistance, CAFs are promising therapeutic targets in cancer. The elimination of tumor-promoting CAFs has been investigated in various animal models to determine whether it effectively suppresses tumor growth. Based on recent evidence, several simple strategies have been proposed to eliminate tumor-promoting CAFs and attenuate these features. In addition, attention has focused on the critical role that CAFs play in the immunosuppressive TME. Therefore, the functional reprogramming of CAFs in combination with immune checkpoint inhibitors has also been investigated as a possible therapeutic approach. However, although potential targets in CAFs have been widely characterized, the plasticity and heterogeneity of CAFs complicate the understanding of their properties and present difficulties for clinical application. Moreover, the identification of tumor-suppressive CAFs highlights the necessity for the development of therapeutic approaches that can distinguish and switch between tumor-promoting and tumor-suppressive CAFs in an appropriate manner. In this review, we introduce the origins and diversity of CAFs, their role in cancer, and current therapeutic strategies aimed at targeting CAFs, including ongoing clinical evaluations.

Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement of single-cell RNA sequencing (scRNA-seq) technology has made it possible to address the problem of cancer cell identification at the single-cell level more efficiently with computational methods, as opposed to the time-consuming and less reproducible manual identification methods. However, existing computational methods have shown suboptimal identification performance and a lack of capability to incorporate external reference data as prior information. Here, we propose scCrab, a reference-guided automatic cancer cell identification method, which performs ensemble learning based on a Bayesian neural network (BNN) with multi-head self-attention mechanisms and a linear regression model. Through a series of experiments on various datasets, we systematically validated the superior performance of scCrab in both intra- and inter-dataset predictions. Besides, we demonstrated the robustness of scCrab to dropout rate and sample size, and conducted ablation experiments to investigate the contributions of each component in scCrab. Furthermore, as a dedicated model for cancer cell identification, scCrab effectively captures cancer-related biological significance during the identification process.

Essential genes are indispensable for the survival of cancer cell. CRISPR/Cas9-based pooled genetic screens have distinguished the essential genes and their functions in distinct cellular processes. Nevertheless, the landscape of essential genes at the single cell levels and the effect on the tumor microenvironment (TME) remains limited. Here, we identified 396 essential protein-coding genes (ESPs) by integration of 8 genome-wide CRISPR loss-of-function screen datasets of colorectal cancer (CRC) cell lines and single-cell RNA sequencing (scRNA-seq) data of CRC tissues. Then, 29 essential long non-coding genes (ESLs) were predicted using Hypergeometric Test (HT) and Personalized PageRank (PPR) algorithms based on ESPs and co-expressed network constructed from scRNA-seq. CRISPR/Cas9 knockout experiment verified the effect of several ESPs and ESLs on the survival of CRC cell line. Furthermore, multi-omics features of ESPs and ESLs were illustrated by examining their expression patterns and transcription factor (TF) regulatory network at the single cell level, as well as DNA mutation and DNA methylation events at bulk level. Finally, through integrating multiple intracellular regulatory networks with cell-cell communication network (CCN), we elucidated that CD47 and MIF are regulated by multiple CRC essential genes, and the anti-cancer drugs sunitinib can interfere the expression of them potentially. Our findings provide a comprehensive asset of CRC ESPs and ESLs, sheding light on the mining of potential therapy targets for CRC.

Cancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment by promoting tumor growth, immune evasion, and metastasis. Recently, drug delivery systems targeting CAFs have emerged as a promising long-term and effective approach to cancer treatment. Advances in nanotechnology, in particular, have led to the development of nanomedicine delivery systems designed specifically to target CAFs, offering new possibilities for precise and personalized cancer therapies. This article reviews recent progress in drug delivery using nanocarriers that target CAFs. Additionally, we explore the potential of combining multiple therapies, such as chemotherapy and immunotherapy, with nanocarriers to enhance efficacy and overcome drug resistance. Although many preclinical studies show promise, the clinical application of nanomedicine still faces considerable challenges, especially in terms of drug penetration and large-scale production. Therefore, this review aims to provide a fresh perspective on CAF-targeted drug delivery systems and highlight potential future research directions and clinical applications.

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment.

Patients with colorectal cancer (CRC) following renal transplantation require long-term immunosuppressants to prevent graft rejection. However, the impact of these immunosuppressants on the tumor immune microenvironment and the roles of immune cells within it remain poorly understood. We conducted comprehensive single-cell RNA sequencing on tumor and normal tissues from four CRC patients post renal transplantation and compared these with published data from 23 non-transplant CRC patients. We set four groups for detailed comparative analysis based on the renal transplantation status and tissue origin: non-renal transplantation normal (nRT_Normal), non-renal transplantation tumor (nRT_Tumor), renal transplantation normal (RT_Normal), renal transplantation tumor (RT_Tumor). Our analysis revealed significant tumor immune microenvironment landscape alterations in the transplantation group. CD8+effector T cells of RT_Tumor showed significantly diminished cytotoxicity and tumor neoantigen recognition (p < 0.0001), while CD4+FOXP3 regulatory T cells of RT_Tumor displayed a higher inhibitory score (p < 0.05), indicating preserved immunomodulatory potential compared with non-transplant CRC. Notably, significantly increased CTLA4 expression in T cells of RT_Tumor was found and testified (p < 0.05). Our findings provide novel mechanistic insights for understanding the immune landscape in renal transplant recipients with CRC and pave the way for potential immunotherapeutic strategies that may improve survival and quality of life for this patient population.

Pancreatic ductal adenocarcinoma (PDAC) carries an extremely poor prognosis, in part resulting from cellular heterogeneity that supports overall tumorigenicity. Cancer associated fibroblasts (CAF) are key determinants of PDAC biology and response to systemic therapy. While CAF subtypes have been defined, the effects of patient-specific CAF heterogeneity and plasticity on tumor cell behavior remain unclear. Here, multi-omics was used to characterize the tumor microenvironment (TME) in tumors from patients undergoing curative-intent surgery for PDAC. In these same patients, matched tumor organoid and CAF lines were established to functionally validate the impact of CAFs on the tumor cells. CAFs were found to drive epithelial-mesenchymal transition (EMT) and a switch in tumor cell classificiaton from classical to basal subtype. Furthermore, we identified CAF-specific interleukin 8 (IL-8) as an important modulator of tumor cell subtype. Finally, we defined neighborhood relationships between tumor cell and T cell subsets.

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. In this study, we investigated how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single-cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory and cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveals co-localization of aged immune and epithelial cells in situ. Lastly, we found transcriptional signatures of aging mammary cells in human breast tumors, suggesting possible links between aging and cancer. Together, these data uncover that epithelial, immune and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment and neoplasia risk.

As cancers progress, they become increasingly aggressive-metastatic tumours are less responsive to first-line therapies than primary tumours, they acquire resistance to successive therapies and eventually cause death1,2. Mutations are largely conserved between primary and metastatic tumours from the same patients, suggesting that non-genetic phenotypic plasticity has a major role in cancer progression and therapy resistance3-5. However, we lack an understanding of metastatic cell states and the mechanisms by which they transition. Here, in a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that, although primary tumours largely adopt LGR5+ intestinal stem-like states, metastases display progressive plasticity. Cancer cells lose intestinal cell identities and reprogram into a highly conserved fetal progenitor state before undergoing non-canonical differentiation into divergent squamous and neuroendocrine-like states, a process that is exacerbated in metastasis and by chemotherapy and is associated with poor patient survival. Using matched patient-derived organoids, we demonstrate that metastatic cells exhibit greater cell-autonomous multilineage differentiation potential in response to microenvironment cues compared with their intestinal lineage-restricted primary tumour counterparts. We identify PROX1 as a repressor of non-intestinal lineage in the fetal progenitor state, and show that downregulation of PROX1 licenses non-canonical reprogramming.