Robust Gene Signature Research Articles

DOP029 SPP1 in colitis-associated colon cancer

Abstract Background Colitis-associated carcinoma (CAC) is a major complication that arises in patients with a long-standing ulcerative colitis (UC) and Crohn’s disease (CD). Albeit numerous studies report mutational landscape of CAC, its transcriptomic landscape is poorly understood. Thus, to interrogate the tumour ecology & mechanisms behind CAC development, we adopted a muti-modal approach utilizing FFPE tissues from human CAC patients and colon organoids. We leveraged immunology bulk gene expression and spatial gene expression to derive a robust gene signature of CAC at a single cell resolution. Methods To comprehensively profile the CAC immune microenvironment, we created a comprehensive dataset (Nanostring) covering relevant clinical FFPE samples encompassing 40 patients suffering from either IBD or CAC, alongside inflammation-free healthy controls (10 patients/group). We observed a robust upregulation of the SPP1 (Secreted Phosphoprotein 1) gene encoding osteopontin (OPN) in CAC. Subsequently, we extended our methodology to spatially resolved transcriptomics (Xenium) using 5μm FFPE sections from CAC & UC patients (3 patients/group). This provided insights into the spatial and single cell expression of SPP1 and its target receptors at a subcellular resolution. Furthermore, we mapped the ligand-receptor pairs onto cell types to uncover SPP1 signalling in CAC. These findings were validated at a protein level by whole FFPE section confocal microscopy. Results Immunology gene expression (Nanostring) dataset revealed a strong upregulation of SPP1 gene encoding OPN in CAC as compared to UC and CD. Spatially resolved transcriptomics (Xenium) unveiled the spatial heterogeneity of SPP1 expression in CAC, that is primarily arising from CD68+ CD163+ macrophages in CAC (Fig 1B), validated through OPN/CD68 immunostainings. Ligand-receptor (LR) analysis revealed a distinct LR pairing focusing on SPP1, that is secreted (outgoing signal) macrophages to T cells (as incoming signal) via upregulated integrin and CD44-receptors (Fig 1D/E). Furthermore, CAC tumour cells surrounding the SPP1+ macrophages exhibited a distinct gene expression signature associated with T cell exclusion and immunosuppression in CAC (Fig 1C/FG). Conclusion We identified a CAC specific upregulation of SPP1 at both RNA and protein levels, expressed by CD68+ CD163+ macrophages. Mutually exclusive spatial location of SPP1/OPN+ macrophages and CD8+ T cells suggests a crucial indirect role of SPP1/OPN in mediating an immunosuppressive tumour microenvironment. We further uncovered CAC specific, single cell gene expression signature that has the potential to identify pre-malignant stages of CAC.

Novel Assignment of Gene Markers to Hematological and Immune Cells Based on Single-Cell Transcriptomics.

There are many different cells that perform highly specialized functions in the human hematological and immune systems. Due to the relevance of their activity, in this work we investigated the cell types and subtypes that form this complex system, using single-cell RNA sequencing (scRNA-seq) to dissect and assess the markers that best define each cell population. We first developed an optimized computational workflow for analyzing large scRNA-seq datasets. We then used it to find gene markers of the different cell types present in bone marrow (BM) and peripheral blood (PB). We analyzed three different single-cell datasets to find specific cell markers using this strategy: first, we searched in the CD marker genes and then in the genes encoding membrane proteins and finally in all detected protein-coding genes. This allowed us not only to confirm known CDs that best mark some cell types (e.g., monocytes, B cells, NK cells, etc.) but also to test the ability of new genes to distinguish specific cell types. Finally, we applied a machine learning method (Random Forest) to test the accuracy of the different markers we found. As a result of all this work, we have found and propose specific and robust gene signatures to identify different types and subtypes of hematological and immune cells.

Combined PARP14 inhibition and PD-1 blockade promotes cytotoxic T cell quiescence and modulates macrophage polarization in relapsed melanoma

BackgroundProgrammed cell death 1 (PD-1) signaling blockade by immune checkpoint inhibitors (ICI) effectively restores immune surveillance to treat melanoma. However, chronic interferon-gamma (IFNγ)-induced immune homeostatic responses in melanoma cells contribute...

Single cell transcriptional analysis of human adenoids identifies molecular features of airway microfold cells.

The nasal, oropharyngeal, and bronchial mucosa are primary contact points for airborne pathogens like Mycobacterium tuberculosis (Mtb), SARS-CoV-2, and influenza virus. While mucosal surfaces can function as both entry points and barriers to infection, mucosa-associated lymphoid tissues (MALT) facilitate early immune responses to mucosal antigens. MALT contains a variety of specialized epithelial cells, including a rare cell type called a microfold cell (M cell) that functions to transport apical antigens to basolateral antigen-presenting cells, a crucial step in the initiation of mucosal immunity. M cells have been extensively characterized in the gastrointestinal (GI) tract in murine and human models. However, the precise development and functions of human airway M cells is unknown. Here, using single-nucleus RNA sequencing (snRNA-seq), we generated an atlas of cells from the human adenoid and identified 16 unique cell types representing basal, club, hillock, and hematopoietic lineages, defined their developmental trajectories, and determined cell-cell relationships. Using trajectory analysis, we found that human airway M cells develop from progenitor club cells and express a gene signature distinct from intestinal M cells. Surprisingly, we also identified a heretofore unknown epithelial cell type demonstrating a robust interferon-stimulated gene signature. Our analysis of human adenoid cells enhances our understanding of mucosal immune responses and the role of M cells in airway immunity. This work also provides a resource for understanding early interactions of pathogens with airway mucosa and a platform for development of mucosal vaccines.

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning

Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenvironment (TME). Nevertheless, elucidating the precise effect of ICD on GC remains a challenging endeavour. ICD-related genes were identified in single-cell sequencing datasets and bulk transcriptome sequencing datasets via the AddModuleScore function, weighted gene co-expression network (WGCNA), and differential expression analysis. A robust signature associated with ICD was constructed using a machine learning computational framework incorporating 101 algorithms. Furthermore, multiomics analysis, including single-cell sequencing analysis, bulk transcriptomic analysis, and proteomics analysis, was conducted to verify the correlation of these hub genes with the immune microenvironment features of GC and with GC invasion and metastasis. We screened 59 genes associated with ICD and developed a robust ICD-related gene signature (ICDRS) via a machine learning computational framework that integrates 101 different algorithms. Furthermore, we identified five key hub genes (SMAP2, TNFAIP8, LBH, TXNIP, and PIK3IP1) from the ICDRS. Through single-cell analysis of GC tumor s, we confirmed the strong correlations of the hub genes with immune microenvironment features. Among these five genes, LBH exhibited the most significant associations with a poor prognosis and with the invasion and metastasis of GC. Finally, our findings were validated through immunohistochemical staining of a large clinical sample set, and the results further supported that LBH promotes GC cell invasion by activating the epithelial–mesenchymal transition (EMT) pathway.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Abstract 7351: Delineating the prostate cancer tumor microenvironment through scRNA-seq profiling at different timepoints

Abstract Distant metastasis remains the leading cause of death in patients with prostate cancer (PCa), however, the underlying mechanisms of this process remain unclear. Using samples from genetically-engineered mouse models (GEMMs) of PCa, we have identified eight different stromal cell states linked to epithelial mutations that may impact PCa progression to metastatic stages. To further investigate the unique intercellular interactions in the tumor microenvironment (TME) involved in mediating disease progression, we use single cell RNA sequencing (scRNA-seq) to profile primary PCa samples from different GEMMs including TRAMP, Hi-MYC, and FVBN, at different timepoints. Analysis of the scRNA-seq data revealed a significant enrichment of three clusters, designated as stromal c6 (s6), epithelial c7 (e7), and epithelial c10 (e10), exclusively in TRAMP, each expressing proliferative markers such as Mki67 and Top2a. To investigate the intercellular interactions among these clusters, we conducted ligand-receptor (LR) interaction analysis using CellChat. Notably, s6 is expressing the genes encoding ligands Bmp4, Fn1, and Igf1, which interact with the receptors Bmpr1b/Bmpr2, Itga3/Itgb1, and Igf1r, respectively expressed in e7. Subsequently, the transcriptomic profiles of these cells were used to build and validate a robust gene signature that can predict metastasis and survival in patients with PCa using a large cohort of 1239 samples, 930 of which were used for training while the remaining 309 samples were used for testing the model’s performance. In conclusion, these results underscore the role of the TME mediating PCa progression and highlight the potential of leveraging the TME to extract predictive and prognostic features which can be later employed to build predictive and prognostic models for patients with PCa. Citation Format: Itzel Valencia, Hubert Pakula, Mohamed Omar, Massimo Loda, Luigi Marchionni. Delineating the prostate cancer tumor microenvironment through scRNA-seq profiling at different timepoints [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7351.

Abstract A087: An ovarian cancer scRNA-seq atlas to dissect tumor-host interactions underlying metastatization and chemoresistance

Abstract The high tumor heterogeneity of high grade serous ovarian cancer (HGSOC) both intra-tumoral and inter-patient is one of the main factors hampering the identification of novel therapeutic treatment to improve survival of HGSOC patients. Single cell technologies are affording new inroads for the deep molecular characterization of such tumor heterogeneity, in both primary and metastatic samples. To this aim, we built a manually curated HGSOC transcriptomics atlas (~ 1,5 million cells, 79 patients) based on publicly available datasets and developed a data integration strategy specifically applicable to very heterogenous single cell data. scRNA-seq data integration is a central methodological challenge related to single cell data analysis, particularly in the context of highly heterogenous datasets. In this context, the main source of variability is the one existing among patients. Previously developed integration methods tend to perform badly on cancer data since they mainly rely on the assumption that cell populations present in different samples are similar, which might not apply for unknown, rare or patient-specific cell populations. Here we show a novel integration approach for patient-derived cancer data. To obtain a more faithful representation of the data, we performed identification of cell populations and metacells derivation separately for every sample to identify the distinct subpopulations characterizing each patient. In addition, to robustly represent the space describing the three main cell populations of the dataset, i.e., tumoral, immune and stromal, for each of the latter we selected the space identified by the union of highly variable genes, computed separately on every dataset, to preserve the variability of the system. Next, we fed a variational autoencoder with metacells data to integrate and characterize the different subpopulations that may be similar across samples. After integration, we were able to identify new robust gene signatures that would have been masked by datasets heterogeneity. The high degree of accuracy in the manual curation of metadata harmonization in the HGSOC atlas and the robustness and scalability of the data integration pipeline allowed us to obtain an in-depth characterization of the tumor subpopulations involved in therapy response by characterizing the chemotherapy induced transcriptional features. At the same time, thanks to the atlas-associated cell types labelling strategy, this atlas represents a transformative resource for the community by enabling the investigation of the interaction between tissue microenvironment (TME) cells and tumoral cells in shaping the metastatic process. This innovative HGSOC atlas will provide the scientific community of ovarian cancer research with a very powerful tool to investigate many aspects of this disease. The robust deep learning-based framework allows to expand the atlas easily and iteratively with newly generated datasets (out of sample extension), will constitute an invaluable resource for the HGSOC scientific field tackling HGSOC pathogenetic mechanism. Citation Format: Marta R. Sallese, Pietro Lo Riso, Carlo Emanuele Villa, Giuseppe Testa. An ovarian cancer scRNA-seq atlas to dissect tumor-host interactions underlying metastatization and chemoresistance [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A087.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

The value of subcutaneous xenografts for individualised radiotherapy in HNSCC: Robust gene signature correlates with radiotherapy outcome in patients and xenografts

PurposeTo assess the robustness of prognostic biomarkers and molecular tumour subtypes developed for patients with head and neck squamous cell carcinoma (HNSCC) on cell-line derived HNSCC xenograft models, and to develop a novel biomarker signature by combining xenograft and patient datasets. Materials and methodsMice bearing xenografts (n = 59) of ten HNSCC cell lines and a retrospective, multicentre patient cohort (n = 242) of the German Cancer Consortium-Radiation Oncology Group (DKTK-ROG) were included. All patients received postoperative radiochemotherapy (PORT-C). Gene expression analysis was conducted using GeneChip Human Transcriptome Arrays. Xenografts were stratified based on their molecular subtypes and previously established gene classifiers. The dose to control 50 % of tumours (TCD50) was compared between these groups. Using differential gene expression analyses combining xenograft and patient data, a gene signature was developed to define risk groups for the primary endpoint loco-regional control (LRC). ResultsTumours of mesenchymal subtype were characterized by a higher TCD50 (xenografts, p < 0.001) and lower LRC (patients, p < 0.001) compared to the other subtypes. Similar to previously published patient data, hypoxia- and radioresistance-related gene signatures were associated with high TCD50 values. A 2-gene signature (FN1, SERPINE1) was developed that was prognostic for TCD50 (xenografts, p < 0.001) and for patient outcome in independent validation (LRC: p = 0.007). ConclusionGenetic prognosticators of outcome for patients after PORT-C and subcutaneous xenografts after primary clinically relevant irradiation show similarity. The identified robust 2-gene signature may help to guide patient stratification, after prospective validation. Thus, xenografts remain a valuable resource for translational research towards the development of individualized radiotherapy.

Abstract C016: Evaluating clinical biomarkers of FHD-286, a potent and selective inhibitor of BRG1/BRM, in metastatic uveal melanoma

Abstract The BRG/Brahma-associated factors (BAF) family of chromatin remodeling complexes (also referred to as the mSWI/SNF complex) regulates chromatin accessibility and gene expression through its ATP-dependent remodeling activity. FHD-286, a first-in-class compound that potently and selectively inhibits the ATPase components of the BAF complex, BRG1 and BRM, was evaluated in a Phase I dose escalation in subjects with metastatic uveal melanoma (mUM) (FHD-286-001). The pharmacodynamics of FHD-286 were assessed to demonstrate proof-of-mechanism and understand the downstream molecular and clinical impact of BRG1/BRM inhibition. In addition, circulating tumor DNA (ctDNA) was evaluated as an early predictor of overall survival benefit. Seventy-three subjects were on a daily dosing regimen ranging from 2.5 to 10 mg or an intermittent regimen of 1-week on/1-week-off ranging from 10 to 22.5 mg. Tumor biopsies were collected at screening and either Cycle 3, or end of treatment. Biomarker changes in the tumor were observed by histological assessment, by IHC/IF biomarker assays, and RNA sequencing. Observations were suggestive of a differentiation effect by FHD-286 on the tumor cells, including a decrease in stemness genes and an increase in mature melanocytic markers. Evidence of necrosis and a trend of decreasing tumor cell density were also seen. In addition, ctDNA was measured in serial plasma samples using a targeted NGS panel. A reduction in ctDNA was shown in approximately 50% of subjects, which correlated with an increased overall survival benefit. Lastly, serial blood samples collected in Paxgene RNA tubes were analyzed by RNA sequencing. A robust dose-dependent gene signature was identified as a peripheral readout of target engagement at steady-state in both dosing regimens and across dose cohorts. In summary, we observed changes in the tumor cells at the biopsy site and ctDNA that suggest FHD-286 has a biological impact on uveal melanoma that may lead to clinical benefit. Given the limitations and challenges to obtain tumor biopsies from every subject and at a frequent rate, the identified PD gene signature in the blood is a valuable tool to measure target engagement in this study, and other potential solid tumor indications. Citation Format: Jessica Wan, Kim Horrigan, GiNell Elliott, Liv Johannessen, Mike Collins, Alexander J. Lazar, Dillon Corrigan, Caitlin Patriquin, Sam Agresta, Jessica Piel, Martin Hentemann. Evaluating clinical biomarkers of FHD-286, a potent and selective inhibitor of BRG1/BRM, in metastatic uveal melanoma [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C016.

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

BackgroundWhile the efficacy of neoadjuvant chemotherapy (NACT) in treating triple-negative breast cancer (TNBC) is generally accepted, not all patients derive benefit from this preoperative treatment. Presently, there are no validated biomarkers to predict the NACT response, and previous attempts to develop predictive classifiers based on gene expression data have not demonstrated clinical utility. However, predictive models incorporating biological constraints have shown increased robustness and improved performance compared to agnostic classifiers.MethodsWe used the preoperative transcriptomic profiles from 298 patients with TNBC to train and test a rank-based classifier, k-top scoring pairs, to predict whether the patient will have pathological complete response (pCR) or residual disease (RD) following NACT. To reduce overfitting and enhance the signature’s interpretability, we constrained the training process to genes involved in the Notch signaling pathway. Subsequently, we evaluated the signature performance on two independent cohorts with 75 and 71 patients. Finally, we assessed the prognostic value of the signature by examining its association with relapse-free survival (RFS) using Kaplan‒Meier (KM) survival estimates and a multivariate Cox proportional hazards model.ResultsThe final signature consists of five gene pairs, whose relative ordering can be predictive of the NACT response. The signature has a robust performance at predicting pCR in TNBC patients with an area under the ROC curve (AUC) of 0.76 and 0.85 in the first and second testing cohorts, respectively, outperforming other gene signatures developed for the same purpose. Additionally, the signature was significantly associated with RFS in an independent TNBC patient cohort even after adjusting for T stage, patient age at the time of diagnosis, type of breast surgery, and menopausal status.ConclusionWe introduce a robust gene signature to predict pathological complete response (pCR) in patients with TNBC. This signature applies easily interpretable, rank-based decision rules to genes regulated by the Notch signaling pathway, a known determinant in breast cancer chemoresistance. The robust predictive and prognostic performance of the signature make it a strong candidate for clinical implementation, aiding in the stratification of TNBC patients undergoing NACT.

Multi-cohort validation study of a four-gene signature for risk stratification and treatment response prediction in hepatocellular carcinoma

BackgroundThe intricate molecular landscape of hepatocellular carcinoma (HCC) presents a significant challenge to achieving precise risk stratification through clinical genetic testing. At present, there is a paucity of robust gene signatures that could assist clinicians in making clinical decisions for patients with HCC. MethodsWe obtained gene expression profiles of patients with HCC from 20 independent cohorts available in public databases. A gene signature was developed by employing two machine learning algorithms. In addition to validating the signature with high-throughput data in public cohorts, we external validated the signature in 64 HCC cases by RT-PCR method. We compared genomic, transcriptomic and proteomic features between different subgroups. We also compared our signature to 130 gene signatures that have already been published. ResultsWe developed a novel four-gene signature, designated as HCC4, that demonstrates significant potential for the prediction of survival outcomes in more than 1300 patients with HCC. The HCC4 also has potential for predicting recurrence and tumor volume doubling time, assessing transcatheter arterial chemoembolization and immunotherapy responses, and non-invasive detection of HCC. The high HCC4 score group shows a higher frequency of mutations in genes TP53, RB1 and TSC1/2, as well as increased activity of cell-cycle, glycolysis and hypoxia signaling pathways, higher cancer stemness score, and lower lipid metabolism activity. In seven HCC cohorts, HCC4 exhibited a higher average C-index in predicting overall survival compared to the 130 signatures previously published. Drug screening indicated that patients with high HCC4 scores were more sensitive to agents targeting AURKA, TUBB, JMJD6 and KIFC1. ConclusionsOur findings demonstrated that HCC4 is a powerful tool for improving risk stratification and for identifying HCC patients who are most likely to benefit from TACE treatment, immunotherapy, and other experimental therapies.

Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma.

Objective: Epithelial-mesenchymal transition (EMT) is linked to an unfavorable prognosis in oral squamous cell carcinoma (OSCC). Here, we aimed to develop an EMT gene signature for OSCC prognosis. Methods: In TCGA dataset, prognosis-related EMT genes with p < 0.05 were screened in OSCC. An EMT gene signature was then conducted with LASSO method. The efficacy of this signature in predicting prognosis was externally verified in the GSE41613 dataset. Correlations between this signature and stromal/immune scores and immune cell infiltration were assessed by ESTIMATE and CIBERSORT algorithms. GSEA was applied for exploring significant signaling pathways activated in high- and low-risk phenotypes. Expression of each gene was validated in 40 paired OSCC and normal tissues via RT-qPCR. Results: A prognostic 9-EMT gene signature was constructed in OSCC. High risk score predicted poorer clinical outcomes than low risk score. ROCs confirmed the well performance on predicting 1-, 3- and 5-year survival. Multivariate cox analysis revealed that this signature was independently predictive of OSCC prognosis. The well predictive efficacy was validated in the GSE41613 dataset. Furthermore, this signature was distinctly related to stromal/immune scores and immune cell infiltration in OSCC. Distinct pathways were activated in two subgroups. After validation, AREG, COL5A3, DKK1, GAS1, GPX7 and PLOD2 were distinctly upregulated and SFRP1 was downregulated in OSCC than normal tissues. Conclusion: Our data identified and verified a robust EMT gene signature in OSCC, which provided a novel clinical tool for predicting prognosis and several targets against OSCC therapy.

Global analysis of iron metabolism-related genes identifies potential mechanisms of gliomagenesis and reveals novel targets.

This study aimed to investigate key regulators of aberrant iron metabolism in gliomas, and evaluate their effect on biological functions and clinical translational relevance. We used transcriptomic data from multiple cross-platform glioma cohorts to identify key iron metabolism-related genes (IMRGs) based on a series of bioinformatic and machine learning methods. The associations between IMRGs and prognosis, mesenchymal phenotype, and genomic alterations were analyzed in silico. The performance of the IMRGs-based signature in predicting temozolomide (TMZ) treatment sensitivity was evaluated. In vitro and in vivo experiments were used to explore the biological functions of these key IMRGs. HMOX1, LTF, and STEAP3 were identified as the most essential IMRGs in gliomas. The expression levels of these genes were strongly related to clinicopathological and molecular features. The robust IMRG-based gene signature could be used for prognosis prediction. These genes facilitate mesenchymal transformation, driver gene mutations, and oncogenic alterations in gliomas. The gene signature was also associated with TMZ resistance. HMOX1, LTF, and STEAP3 knockdown in glioma cells significantly reduced cell proliferation, colony formation, migration, and malignant invasion. The study presented a comprehensive view of key regulators underpinning iron metabolism in gliomas and provided new insights into novel therapeutic approaches.

Gene signature reveals decreased SOX10-dependent transcripts in malignant cells from immune checkpoint inhibitor-resistant cutaneous melanomas

Evidence is mounting for cross-resistance between immune checkpoint and targeted kinase inhibitor therapies in cutaneous melanoma patients. Since the loss of the transcription factor, SOX10, causes tolerance to MAPK pathway inhibitors, we used bioinformatic techniques to determine if reduced SOX10 expression/activity is associated with immune checkpoint inhibitor resistance. We integrated SOX10 ChIP-seq, knockout RNA-seq, and knockdown ATAC-seq data from melanoma cell models to develop a robust SOX10 gene signature. We used computational methods to validate this signature as a measure of SOX10-dependent activity in independent single-cell and bulk RNA-seq SOX10 knockdown, cell line panel, and MAPK inhibitor drug-resistant datasets. Evaluation of patient single-cell RNA-seq data revealed lower levels of SOX10-dependent transcripts in immune checkpoint inhibitor-resistant tumors. Our results suggest that SOX10-deficient melanoma cells are associated with cross-resistance between targeted and immune checkpoint inhibitors and highlight the need to identify therapeutic strategies that target this subpopulation.

Single-Cell Transcriptome Analysis Identifies Subclusters with Inflammatory Fibroblast Responses in Localized Scleroderma.

Localized scleroderma (LS) is an autoimmune disease with both inflammatory and fibrotic components causing an abnormal deposition of collagen in the skin and underlying tissue, often leading to disfigurement and disability. Much of its pathophysiology is extrapolated from systemic sclerosis (SSc) since the histopathology findings in the skin are nearly identical. However, LS is critically understudied. Single-cell RNA sequencing (scRNA seq) technology provides a novel way to obtain detailed information at the individual cellular level, overcoming this barrier. Here, we analyzed the affected skin of 14 patients with LS (pediatric and adult) and 14 healthy controls. Fibroblast populations were the focus, since they are the main drivers of fibrosis in SSc. We identified 12 fibroblast subclusters in LS, which overall had an inflammatory gene expression (IFN and HLA-associated genes). A myofibroblast-like cluster (SFRP4/PRSS23) was more prevalent in LS subjects and shared many upregulated genes expressed in SSc-associated myofibroblasts, though it also had strong expression of CXCL9/10/11, known CXCR3 ligands. A CXCL2/IRF1 cluster identified was unique to LS, with a robust inflammatory gene signature, including IL-6, and according to cell communication analysis are influenced by macrophages. In summary, potential disease-propagating fibroblasts and associated gene signatures were identified in LS skin via scRNA seq.

Integrated analysis of robust sex-biased gene signatures in human brain

BackgroundSexual dimorphism is highly prominent in mammals with many physiological and behavioral differences between male and female form of the species. Accordingly, the fundamental social and cultural stratification factors for humans is sex. The sex differences are thought to emerge from a combination of genetic and environmental factors. It distinguishes individuals most prominently on the reproductive traits, but also affects many of the other related traits and manifest in different disease susceptibilities and treatment responses across sexes. Sex differences in brain have raised a lot of controversy due to small and sometimes contradictory sex-specific effects. Many studies have been published to identify sex-biased genes in one or several brain regions, but the assessment of the robustness of these studies is missing. We therefore collected huge amount of publicly available transcriptomic data to first estimate whether consistent sex differences exist and further explore their likely origin and functional significance.Results and conclusionIn order to systematically characterise sex-specific differences across human brain regions, we collected transcription profiles for more than 16,000 samples from 46 datasets across 11 brain regions. By systematic integration of the data from multiple studies, we identified robust transcription level differences in human brain across to identify male-biased and female-biased genes in each brain region. Firstly, both male and female-biased genes were highly conserved across primates and showed a high overlap with sex-biased genes in other species. Female-biased genes were enriched for neuron-associated processes while male-biased genes were enriched for membranes and nuclear structures. Male-biased genes were enriched on the Y chromosome while female-biased genes were enriched on the X chromosome, which included X chromosome inactivation escapees explaining the origins of some sex differences. Male-biased genes were enriched for mitotic processes while female-biased genes were enriched for synaptic membrane and lumen. Finally, sex-biased genes were enriched for drug-targets and more female-biased genes were affected by adverse drug reactions than male-biased genes. In summary, by building a comprehensive resource of sex differences across human brain regions at gene expression level, we explored their likely origin and functional significance. We have also developed a web resource to make the entire analysis available for the scientific community for further exploration, available at https://joshiapps.cbu.uib.no/SRB_app/

Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles.

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are the two highly prevalent debilitating and sometimes life-threatening systemic inflammatory autoimmune diseases. The etiology and pathogenesis of RA and SLE are interconnected in several ways, with limited knowledge about the underlying molecular mechanisms. With the motivation to better understand shared biological mechanisms and determine novel therapeutic targets, we explored common molecular disease signatures by performing a meta-analysis of publicly available microarray gene expression datasets of RA and SLE. We performed an integrated, multi-cohort analysis of 1088 transcriptomic profiles from 14 independent studies to identify common gene signatures. We identified sixty-two genes common among RA and SLE, out of which fifty-nine genes (21 upregulated and 38 downregulated) had similar expression profiles in the diseases. However, antagonistic expression profiles were observed for ACVR2A, FAM135A, and MAPRE1 genes. Thirty genes common between RA and SLE were proposed as robust gene signatures, with persistent expression in all the studies and cell types. These gene signatures were found to be involved in innate as well as adaptive immune responses, bone development and growth. In conclusion, our analysis of multicohort and multiple microarray datasets would provide the basis for understanding the common mechanisms of pathogenesis and exploring these gene signatures for their diagnostic and therapeutic potential.

Insights into pulmonary phosphate homeostasis and osteoclastogenesis emerge from the study of pulmonary alveolar microlithiasis

Pulmonary alveolar microlithiasis is an autosomal recessive lung disease caused by a deficiency in the pulmonary epithelial Npt2b sodium-phosphate co-transporter that results in accumulation of phosphate and formation of hydroxyapatite microliths in the alveolar space. The single cell transcriptomic analysis of a pulmonary alveolar microlithiasis lung explant showing a robust osteoclast gene signature in alveolar monocytes and the finding that calcium phosphate microliths contain a rich protein and lipid matrix that includes bone resorbing osteoclast enzymes and other proteins suggested a role for osteoclast-like cells in the host response to microliths. While investigating the mechanisms of microlith clearance, we found that Npt2b modulates pulmonary phosphate homeostasis through effects on alternative phosphate transporter activity and alveolar osteoprotegerin, and that microliths induce osteoclast formation and activation in a receptor activator of nuclear factor-κB ligand and dietary phosphate dependent manner. This work reveals that Npt2b and pulmonary osteoclast-like cells play key roles in pulmonary homeostasis and suggest potential new therapeutic targets for the treatment of lung disease.