Prognostic value of eight immune gene signatures in pancreatic cancer patients

Wang, Wenting; Xu, Zhijian; Wang, Ning; Yao, Ruyong; Qin, Tao; Lin, Hao; Yue, Lu

doi:10.1186/s12920-020-00868-w

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…The infiltration levels of diverse immune cells, including DC, iDC, pDC, B cells, T cells, Tcm, Tem, Tfh, Th17 cells, and cytotoxic cells, in the low-risk group were significantly higher than those in the high-risk group. 78 A low-risk group of squamous cell carcinoma (SCC) patients, who had high proportions of naïve B cells and resting mast cells, experienced better OS than those of a high-risk group. 61 Genes-immune checkpoint modulators (CD27, TNFRSF14, CD276, IDO1, TIGIT, CTLA4, ICOS, and LAG3) have prognostic value for SCC patients.…”

Section: Therapeutic Advances In Medical Oncologymentioning

confidence: 99%

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Immune gene signatures as prognostic criteria for cancer patients

Larionova

Таширева

2023

Ther Adv Med Oncol

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Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

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Section: Therapeutic Advances In Medical Oncologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Immune gene signatures as prognostic criteria for cancer patients

Larionova

Таширева

2023

Ther Adv Med Oncol

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“…A part of patients with PDAC could benefit from immune checkpoint inhibitors (PD-L1 and CTLA4), chimeric antigen receptor T (CAR) cells, immunomodulators, and vaccines. 6 In addition, the tumor is surrounded by a dense stroma and a tumor microenvironment (TME) with persistent mild inflammation, which leads to the evasion of immune attack, the ineffectiveness of chemotherapy, and poor prognosis. Among them, tumor-associated macrophages (TAMs) are one of the main components of the TME, accounting for about 15% to 20% of the total cellular tumor mass.…”

Section: Introductionmentioning

confidence: 99%

Identification of Transcriptomic Signatures of Pancreatic Ductal Adenocarcinoma–Derived Exosomes That Promote Macrophage M2 Polarization and Predict Prognosis: S100A9 Reveals Tumor Progression

Tan,

Tang,

Zhang

et al. 2024

Clin Med Insights Oncol

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Background: Exosomes play a role in intercellular communication and participate in the interaction between pancreatic ductal adenocarcinoma (PDAC) cells and immune cells. Macrophages can receive tumor cell–derived exosomes to polarize into M2-type macrophages, which can enhance the invasion and metastasis of pancreatic cancer, leading to poor prognosis. However, the mechanism by which pancreatic cancer cell–derived exosomes promote M2-type macrophages is still unclear. Methods: M2 macrophage–associated exosome-derived key module genes were identified by differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) analysis using exoRbase 2.0, The Cancer Genome Atlas (TCGA), and The International Cancer Genome Consortium (ICGC) databases. Multivariate Cox regression analysis was used to identify key prognostic genes and obtain regression coefficients to establish prognostic signature. Immune infiltration, tumor mutations, and GSEA among different risk groups were compared. exoRbase 2.0, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), HPA, and TISCH2 databases were used to further analyze the expression pattern of S100A9 in pancreatic cancer. In vitro experiments, cell-derived exosome isolation, quantitative polymerase chain reaction (qPCR), western blot, flow cytometry analysis, cell transfection, transwell assay, and CCK-8 assay were applied to investigate the roles of S100A9 in macrophage M2 polarization and tumor progression. Results: The key genes of PDAC-derived exosomes promoting M2-type macrophage polarization were identified, and a risk score model was established. The risk score is related to the expression of common immune checkpoints, immune score, and stromal score, and the tumor mutational burden and biological function of high- and low-risk groups were also different. S100A9 was positively correlated with M2-type macrophage marker. In addition, scRNA-seq data from the TISCH2 database revealed that S100A9 is predominantly expressed in pancreatic cancer cells and mono/macrophage cells, suggesting that S100A9 in pancreatic cancer cells could be received by macrophages, thereby inducing macrophage polarization. In vitro, we used exosomes from BxPC-3 cell lines to coculture macrophages and found that macrophages were mainly polarized toward M2 type, which further promoted the proliferation and metastasis of PDAC. Conclusions: Our study established a reliable risk score model for PDAC-derived exosomes and M2 macrophages, identified the important role of S100A9 in macrophage M2 polarization, which provides a new strategy for the diagnosis and treatment of PDAC, and strengthened the understanding of the mechanism of tumor development and metastasis.

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Occlusion enhanced pan-cancer classification via deep learning

Zhao,

Chen,

Wang

et al. 2024

BMC Bioinformatics

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Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces “GENESO,” a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called “Symmetrical Occlusion (SO)”. It collaborates with the baseline LSTM network, mimicking the “gain of function” and “loss of function” of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

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Prognostic value of eight immune gene signatures in pancreatic cancer patients

Cited by 3 publications

References 43 publications

Immune gene signatures as prognostic criteria for cancer patients

Immune gene signatures as prognostic criteria for cancer patients

Identification of Transcriptomic Signatures of Pancreatic Ductal Adenocarcinoma–Derived Exosomes That Promote Macrophage M2 Polarization and Predict Prognosis: S100A9 Reveals Tumor Progression

Occlusion enhanced pan-cancer classification via deep learning

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