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Integrative profiling of diverse post-translational modifications for prognostic stratification and personalized therapy in papillary thyroid cancer.
Post-translational modifications (PTMs) are pivotal in tumor biology, yet their role in papillary thyroid cancer (PTC) remains unclear. We integrated bulk and single-cell transcriptomes with clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to analyze 17 PTMs and construct a prognostic model using 12 machine learning algorithms for predicting the thyroid cancer-free interval (TCFi). Enrichment analysis, single-cell analysis, and immune-related analysis were performed to elucidate the biological role of PTMs. Therapeutic responses of PTC patients were predicted based on the model. We validated the expression of model genes and identified the key signature associated with the malignant phenotypes of PTC. We filtered out 12 genes to construct a post-translational modification index (PTMI) and identified three molecular clusters of PTC. Shapley additive explanations (SHAP) and nomogram models confirmed the predictive efficacy of PTMI. Integrated analyses revealed significant associations between PTMI and immune features. High-PTMI patients showed sensitivity to FDA-approved drugs and chemotherapeutics but resistance to radioactive iodine therapy. Notably, we identified TYMS as a key functional PTMI signature. The PTMI proposed in this study holds strong potential as a prognostic biomarker and therapeutic predictor, offering valuable insights for personalized management of PTC patients.
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Integrated mendelian randomization and bioinformatics approach unveiling the immunological and prognostic significance of FBXO2 in breast cancer.
BACKGROUND: Breast cancer (BRCA) is a typical females' malignant tumors. Ubiquitin-proteasome system (UPS) is a critical pathway for pathogenesis of BRCA. Activation of UPS is determined by SKP1-cullin 1-F-box protein (SCF) E3 ligase complexes. F-Box Protein 2 (FBXO2), a pivotal member of F-box proteins family, is associated with UPS. FBXO2 has attracted increasing attention in various cancers, yet its relationship with many cancer types is still unclear. Therefore, systematic research on FBXO2 and pan-cancer is very important for understanding BRCA progression and drug resistance. METHOD: Transcriptome expression data of 33 cancer types were acquired from the Cancer Genome Atlas (TCGA) database. Wilcoxon test was performed to estimate the molecular characteristic of FBOX2 across human pan-cancer analyses. To confirmed the relationship between FBXO2 and BRCA, mendelian randomization (MR) was conducted. Kaplan-Meier (KM) analyses was used to evaluated the survival features of FBXO2. Additionally, protein and methylation level of FBXO2 were examined using Wilcoxon test. Single-cell sequence analysis was further applied to confirm the importance of FBXO2. The underlying mechanism of FBXO2 was explored from various perspectives, including gene function, immune checkpoint and tumor microenvironment. Finally, stable FBXO2-knockdown and overexpression models in MDA-MB-231 and MCF-7 were established, and functional assays including CCK-8, Transwell, and wound healing experiments were conducted to validate the role of FBXO2 in vitro. RESULT: Our pan-cancer analysis revealed that FBXO2 was up-regulated in multiple cancer types, but was significantly down-regulated in BRCA. MR analysis confirmed a caused relationship between reduced FBXO2 expression and increased BRCA risk. Consistent with transcriptional findings, FBXO2 protein levels were also decreased in BRCA. KM analysis indicated that low FXBO2 expression was associated with poor overall survival (OS) and recurrence free survival (RFS) in BRCA patients. Single-cell sequence analysis further revealed that FBXO2 obviously enriched in malignant epithelial cells. In vitro functional experiments demonstrated that knockdown of FBXO2 significantly promoted cell proliferation, migration and invasion, whereas its overexpression suppressed these malignant phenotypes in BRCA. CONCLUSION: In conclusion, our comprehensive analysis revealed that FBXO2 plays a critical tumor-suppression role in BRCA, with its down-regulation contributing to poor prognosis. Furthermore, FBXO2 was implicated in immune regulation and tumor microenvironment remodeling. Our findings provided novel insights into the pathogenesis of BRCA.
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High SIGLEC10-Expressing Tumor-Associated Macrophages Participate in Remodeling the Tumor Immune Microenvironment and Predict Poor Prognosis in Colorectal Cancer.
BACKGROUND: Sialic acid-binding immunoglobulin-like lectin 10 (SIGLEC10), a member of the SIGLEC family, is selectively expressed on multiple immune cell subsets and plays a key role in immune regulation through the recognition of sialylated ligands. Recent studies suggest that SIGLEC10 functions as an emerging immune checkpoint that contributes to the regulation of tumor-associated macrophages (TAMs) within the tumor microenvironment (TME). METHODS: Immunohistochemistry was performed to assess SIGLEC10 expression in a CRC cohort comprising 202 patients, and the association between SIGLEC10 expression and patient survival and prognosis was evaluated. Immunofluorescence staining and flow cytometry were used to characterize the localization and phenotypic features of SIGLEC10-expressing cells within tumor tissues. The Cancer Genome Atlas (TCGA) and RNA sequencing (RNA-seq) datasets were analyzed to investigate SIGLEC10-associated tumor immune signaling pathways. Additionally, single-cell datasets from Tumor Immune Single-cell Hub 2 (TISCH2) were used to further investigate the regulatory mechanisms of SIGLEC10high TAMs within the TME. Finally, the therapeutic potential of SIGLEC10 as an immunotherapeutic target in CRC was evaluated. RESULTS: High SIGLEC10 expression predicts poor prognosis in CRC patients and serves as an independent prognostic factor. SIGLEC10 is predominantly expressed in CD68+ macrophages and is associated with TAMs phenotypes. Functional enrichment analyses indicate that SIGLEC10high TAMs are involved in the regulation of multiple inflammatory and tumor immune signaling pathways. Immune infiltration and differential analyses suggest that SIGLEC10high TAMs may facilitate tumor immune evasion by promoting increased infiltration of immune cells and recruiting various immune chemokines. Single-cell analyses reveal that SIGLEC10high TAMs may remodel the TME through TNFSF13B-mediated regulation of multiple immune cell populations, including B cells. Finally, SIGLEC10 exhibits considerable potential as an immunotherapeutic target for CRC. CONCLUSION: This study highlights SIGLEC10 as an independent prognostic factor in patients with CRC and its potential as a promising target for immunotherapy in CRC.
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Mapping the developing human immune system across organs.
Single-cell genomics studies have decoded the immune cell composition of several human prenatal organs but were limited in describing the developing immune system as a distributed network across tissues. We profiled nine prenatal tissues combining single-cell RNA sequencing, antigen-receptor sequencing, and spatial transcriptomics to reconstruct the developing human immune system. This revealed the late acquisition of immune-effector functions by myeloid and lymphoid cell subsets and the maturation of monocytes and T cells before peripheral tissue seeding. Moreover, we uncovered system-wide blood and immune cell development beyond primary hematopoietic organs, characterized human prenatal B1 cells, and shed light on the origin of unconventional T cells. Our atlas provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine, and disease understanding.
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Machine Learning-based Gene Biomarker Identification for Improving Prognosis and Therapy in Hepatocellular Carcinoma.
INTRODUCTION: Traditional clinical evaluations based on pathological classification have shown limited effectiveness in predicting prognosis and guiding treatment for patients with hepatocellular carcinoma (HCC). This study aims to identify a robust molecular biomarker for improving prognosis and therapy in HCC. METHODS: The International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) provided expression data and clinicopathological information for several cohorts. First, Cox regression analysis and differentially expressed analysis were performed to identify robust prognostic genes. Next, machine learning algorithms, including 101 statistical models, were employed to pinpoint key genes in HCC. Single-cell sequencing analysis was conducted to explore the potential subcellular functions of each key gene. Based on these findings, an HCC Prognosis-Related Index (HPRI) was developed from the identified key genes, and HPRI-based nomogram models were validated across multiple cohorts. Additionally, tumor microenvironment analysis and drug sensitivity analysis were performed further to assess the clinical significance of the HPRI in HCC. RESULTS: A total of 36 robust prognostic genes with differential expression in HCC were identified, from which seven key genes-DCAF13, EEF1E1, GMPS, OLA1, PLOD2, PABPC1, and PPARGC1A-were filtered using machine learning algorithms. Except for PPARGC1A, all these genes were highly expressed in malignant cells, followed by fibroblasts. Notably, we developed the HPRI based on the key genes and validated its clinical relevance. Results demonstrated that the HPRI and HPRI-derived nomogram models had good predictive performance across multiple cohorts. Following tumor microenvironment analysis revealed that a high HPRI was linked to a higher likelihood of immune evasion. Drug sensitivity analysis suggested that patients with a high HPRI might benefit from chemotherapeutic agents like sorafenib, as well as novel compounds such as ML323 and MK-1775. CONCLUSION: Our study demonstrates a well-rounded approach by integrating gene expression, machine learning, tumor microenvironment analysis, and drug sensitivity profiling. HPRI may serve as a promising predictor for guiding prognosis and personalized treatment in HCC.
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The Sex and Age-Associated Infiltration of B Cells May Result in the Dimorphic Behaviors Observed in Papillary Thyroid Carcinomas.
BACKGROUND AND PURPOSE: Sex and age show a dimorphism role in the pathogenesis, lymph node metastasis, and prognostic outcomes of papillary thyroid carcinoma. This investigation endeavors to elucidate the mechanisms underlying these disparities. METHODS: The clinicopathological characteristics and risk factors of lymph node metastasis were explored by analyzing the 2261 patients. The gene expression information of 497 samples from The Cancer Genome Atlas Thyroid Cancer database was used to explore the differentially expressed genes in different phenotypes. What's more, the single-cell RNA sequencing data obtained from the Gene Expression Omnibus database was used to explore the gene expression in specific cells. RESULTS: Multivariate logistic regression analysis showed that in male patients, a larger tumor size, extrathyroidal extension, younger age, and the presence of calcification emerged as significant predictors for lymph node metastasis (LNM)(p < 0.05). Conversely, female patients exhibited a different profile, with larger tumor size, extrathyroidal extension, younger age, calcification, and bilateral tumors being identified as key risk factors (p < 0.05). Further stratification by age demonstrated distinct patterns: among the younger cohort, a larger tumor size, extrathyroidal extension, male gender, calcification, multifocality, and the presence of Hashimoto's thyroiditis held statistical significance (p < 0.05). In contrast, the older subgroup was characterized by a larger tumor size, extrathyroidal extension, male gender, calcification, bilateral tumors, and unclear margins as salient indicators of risk (p < 0.05). In the bulk gene analysis, there were two sex-age-related differentially expressed genes with a contrary trend in tissue sources and LNM status: TCL1A and CR2. The analysis of single-cell RNA sequencing showed that the infiltration of TCL1A- and CR2-related B cells varied in different clinical subtypes. CONCLUSION: Lymph node metastasis of papillary thyroid carcinoma in different sexes and ages may have distinct patterns, and the ages-sex-related B cell infiltration might explain the dimorphism biological behavior.
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S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.
Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.
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Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium.
Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and non-smokers, we generate a comprehensive atlas of epithelial cell types and states, connect these into lineages, and define cell-specific responses to smoking. Our analysis infers multi-state lineages that develop into surface mucus secretory and ciliated cells and then contrasts these to the unique specification of submucosal gland (SMG) cells. Accompanying knockout studies reveal that tuft-like cells are the likely progenitor of both pulmonary neuroendocrine cells and CFTR-rich ionocytes. Our smoking analysis finds that all cell types, including protected stem and SMG populations, are affected by smoking through both pan-epithelial smoking response networks and hundreds of cell-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.
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Disease characterization using a partial correlation-based sample-specific network.
A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.
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Utilization of machine learning algorithms for the identification of the RLN associated prognostic model and feature biomarkers of RLN-related subtypes in breast cancer.
BACKGROUND: Breast cancer (BC) is the most common malignancy afflicting women worldwide, yet the role of relaxin-related genes (RLN) in BC progression remains unclear. This study aims to elucidate the relationship between RLN and BC outcomes through immune microenvironment and metabolic pathway analysis. METHODS: Gene expression and clinical data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Relaxin-related genes were identified using KEGG and Genecard databases. A prognostic model, the RLN Associated Prognostic Model (TRAPM), was established using 101 combinations of 10 machine learning algorithms and validated at the single-cell level. Multi-omics analysis, including the IMvigor210 cohort, was performed to assess TRAPM's applicability in immunotherapy and drug selection. RESULTS: TRAPM, comprising nine prognostic genes (MMP1, RXFP1, PRKCZ, JUN, NFKBIA, GNAI2, NOS2, MMP9, and MMP13), showed significant associations with immune and metabolic profiles. Using TRAPM, a novel BC subtype RC3 and its key marker genes (MTHFD1L, CAVIN4, MMP1, ADGRG6, B3GNT5, SMYD2, and TFRC) were identified. Experimental validation through RT-qPCR and Western Blot confirmed the role of these markers in six BRCA cell lines. CONCLUSIONS: The identification of TRAPM and the RC3 subtype enhances our understanding of BC heterogeneity and highlights potential therapeutic targets. This study provides a foundation for personalized treatment strategies by clarifying the biological significance and clinical relevance of the RC3 subtype.