The expression characteristics of transmembrane protein genes in pancreatic ductal adenocarcinoma through comprehensive analysis of bulk and single-cell RNA sequence

Wang, Hongzhi; Ren, Siqian; Sadula, Abuduhaibaier; Guo, Xueyi; Ye, Meng; Meng, Meng; Li, Gang; Zhang, Xiaowei; Yuan, Chunhui

doi:10.3389/fonc.2023.1047377

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…Notably, during the MGUS stage, there was a significant upregulation of Cd74, Tmem176a, and Nme2 (Figure S5C). While these genes are known to regulate hematopoiesis and have documented roles in various pathologic conditions, their implication in MGUS pathogenesis within the endothelium has not been previously reported [19][20][21] .…”

Section: Ec Are Characterized By a Stress Pre-vascular State During M...mentioning

confidence: 99%

Transcriptional Remodeling of the Stromal and Endothelial Microenvironment in MGUS to Multiple Myeloma Progression

Cenzano,

Cócera,

Bantan

et al. 2024

Preprint

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Despite therapeutic advancements, Multiple Myeloma (MM) remains an incurable disease. To characterize the role that deregulation of the Bone Marrow (BM) microenvironment may have in the transition from healthy to Monoclonal Gammopathy of Undetermined Significance (MGUS), and from MGUS to MM, we performed single-cell RNA sequencing (scRNA-seq) of mesenchymal stromal cells (MSC) and endothelial cells (EC) from two mouse models, BIcgamma and MIcgamma, that recapitulate the principal clinical, genetic, and immunological characteristics of MGUS and MM patients. Our results identify distinct transcriptional dynamics between MSC and EC. While EC acquires a stress state during MGUS, a proliferating and angiogenic profile characterizes MM. On the other hand, MSC compromised their differentiation potential, exhibiting a more inflammatory profile that initiates from the MGUS stage. Interestingly, we identified interferon (IFN)-related myeloma signature in malignant EC of the BIcgamma model, which is also expressed in MSC but not observed in the most aggressive MIcgamma model and can be identified in a subgroup of MM patients. The analysis of the MSC and EC interactions with PC revealed stage-specific interactions of relevance for angiogenesis, immunomodulation, and MM extravasation. Finally, the translational relevance of our results in humans was confirmed on MSC from newly diagnosed patients at different stages of the disease. In summary, these results indicate a remodeling of the non-hematopoietic BM microenvironment in myeloma progression, providing potential targets at the tumor-niche interface that may hold clinical significance and complement existing immunotherapies.

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Section: Ec Are Characterized By a Stress Pre-vascular State During M...mentioning

confidence: 99%

Transcriptional Remodeling of the Stromal and Endothelial Microenvironment in MGUS to Multiple Myeloma Progression

Cenzano,

Cócera,

Bantan

et al. 2024

Preprint

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“…This method ranks the variables based on minimal depth and determines the optimal number of features with the lowest out-of-bag (OOB) error. The RF radiomics score was obtained by summing the selected radiomics features and weighting them according to their importance derived from the RF-SRC method [23][24][25][26]. Additionally, the SVM-based Recursive Feature Elimination (SVM-RFE) algorithm was utilized to determine the optimal subset of features.…”

Section: Feature Selection and Radscore Constructionmentioning

confidence: 99%

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Li,

Chen,

Zhang

et al. 2023

Cancers

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The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.

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Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

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BackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only ∼17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible.MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets.ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such asEPCAM, UBE2C, CHD1L, TP53,CD24,WFDC2, andFANCI,were confirmed. We also identified novel predictive coregulating weak genes includingGIGYF2, GNPAT, RAD54L, andELL.Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGGtight junction, ribosome, andcell cyclepathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types.ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.

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The expression characteristics of transmembrane protein genes in pancreatic ductal adenocarcinoma through comprehensive analysis of bulk and single-cell RNA sequence

Cited by 3 publications

References 53 publications

Transcriptional Remodeling of the Stromal and Endothelial Microenvironment in MGUS to Multiple Myeloma Progression

Transcriptional Remodeling of the Stromal and Endothelial Microenvironment in MGUS to Multiple Myeloma Progression

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

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