Pancreatic Cancer Molecular Classifications: From Bulk Genomics to Single Cell Analysis

Pompella, Luca; Tirino, Giuseppe; Pappalardo, Annalisa; Caterino, Marianna; Ventriglia, Anna; Nacca, Valeria; Orditura, Michele; Vita, Ferdinando De

doi:10.3390/ijms21082814

Cited by 21 publications

(29 citation statements)

References 72 publications

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“…However, due to the complex components of tumor and tumor environment, it is challenging to detailly identify these ectopic genetic accumulations in cancer ( 11 – 13 ). Thanks to the application of single cell omics technologies in cancer study in recent years, increasing studies are profiling and landscaping the components of solid organ diseases including tumors and validating canonical gene markers and revealing novel targets and mutations ( 14 , 15 ). By using of the single cell RNA-seq, PDAC tumor mass is found to be highly heterogeneous and is composed of diverse malignant and stromal cell types, which is true in both human diseases and mouse models ( 16 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

A Cancer Cell Cluster Marked by LincRNA MEG3 Leads Pancreatic Ductal Adenocarcinoma Metastasis

et al. 2021

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Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with poor prognosis and rising incidence worldwide. Late detection and particularly aggressive characteristics are the major challenges that lead to therapeutic failure of this disease. A well described gene program and core regulators are yet to be discovered to drive the metastasis of the PDAC cells. As the development of single cell omics technologies including single cell RNA-sequencing (scRNA-seq), detailed characterization of the cellular composition of solid tumors and their microenvironments are well elaborated. In the current study, we accessed a recently published scRNA-seq dataset on primary and metastatic PDAC tissues and subset the tumor cells. By comparative analysis, we profiled the differentially expressed gene programs of primary and metastatic PDAC and found several long intergenic non-coding RNAs (LincRNAs) in top genes. The PDAC cancer cells showed some heterogeneity and were divided into four major subclusters based on gene profiles, one of which was mostly contributed by metastatic PDAC. Interestingly, this subcluster was remarkably marked by one of the above LincRNAs, MEG3, and exhibited significantly increased Epithelial–Mesenchymal-Transition (EMT) signatures. Ingenuity Pathway Analysis (IPA) on the signature genes of this subcluster gave multiple cancer metastasis associated and EMT signaling pathways, suggesting a critical role of this cluster in leading tumor cell metastasis. Taken together, this study displayed a PDAC cancer subcluster and its marker gene, biologically targeting of which might significantly attenuate the metastasis of tumor and might be a potential strategy for the therapeutic treatment of cancer.

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Section: Introductionmentioning

confidence: 99%

A Cancer Cell Cluster Marked by LincRNA MEG3 Leads Pancreatic Ductal Adenocarcinoma Metastasis

et al. 2021

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“…These molecular classifications provided novel insights into the initiation and progression of PC from another perspective, revealing the existence of internal heterogeneity and the complexity of the tumour microenvironment. Therefore, understanding tumour heterogeneity and achieving a proper stratification of patients with cancer is still a major impediment to develop effective cancer therapy and to understand late and acquired therapy resistance [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of prognosis-related molecular subgroups based on DNA methylation in pancreatic cancer

2021

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Background Pancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies. The lethality of PC is associated with delayed diagnosis, presence of distant metastasis, and its easy relapse. It is known that clinical treatment decisions are still mainly based on the clinical stage and pathological grade, which are insufficient to determine an appropriate treatment. Considering the significant heterogeneity of PC biological characteristics, the current clinical classificatory pattern relying solely on classical clinicopathological features identification needs to be urgently improved. In this study, we conducted in-depth analyses to establish prognosis-related molecular subgroups based on DNA methylation signature. Results DNA methylation, RNA sequencing, somatic mutation, copy number variation, and clinicopathological data of PC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. A total of 178 PC samples were used to develop distinct molecular subgroups based on the 4227 prognosis-related CpG sites. By using consensus clustering analysis, four prognosis-related molecular subgroups were identified based on DNA methylation. The molecular characteristics and clinical features analyses based on the subgroups offered novel insights into the development of PC. Furthermore, we built a risk score model based on the expression data of five CpG sites to predict the prognosis of PC patients by using Lasso regression. Finally, the risk score model and other independent prognostic clinicopathological information were integrative utilised to construct a nomogram model. Conclusion Novel prognosis-related molecular subgroups based on the DNA methylation signature were established. The specific five CpG sites model for PC prognostic prediction and the derived nomogram model are effective and intuitive tools. Moreover, the construction of molecular subgroups based on the DNA methylation data is an innovative complement to the traditional classification of PC and may contribute to precision medicine development, therapeutic efficacy prediction, and clinical decision guidance.

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“…Interestingly, Ohlund D. et al showed that inflammatory CAFs (iCAF) form a pro-tumoral population and facilitate invasion and metastasis, as well as immune suppression [ 34 ]. On the contrary, myofibroblasts (myCAF) are probably involved in stromal and endothelial growth factors secretion [ 35 ]. When comparing genes typically expressed in iCAF and myCAF with our microarray data, CAF-P had commonalities with iCAF and CAF-D with myCAF.…”

Section: Discussionmentioning

confidence: 99%

Cancer-Associated Fibroblast Subgroups Showing Differential Promoting Effect on HNSCC Progression

Kang

Lee

et al. 2021

Cancers

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Background: The critical effect of the tumor microenvironment on cancer progression is well recognized. Recent research suggests that the cancer-promoting properties of the tumor stroma may be attributed to fibroblasts. However, the effect of cancer-associated fibroblast (CAF) on the progression of head and neck squamous cell carcinoma (HNSCC) is not well known. Methods: From the immunohistochemical analysis of head and neck squamous cell carcinoma (HNSCC) tissues, we divided CAF into two groups depending on the presence or absence of a well-demarcated boundary between epithelial cancer cells and the surrounding extracellular matrix (ECM). Primary culture of CAF was performed, followed by co-transplantation with HNSCC cells into mice oral mucosa, and the tumorigenesis was compared. The mRNA expression patterns between these two CAF groups were compared using DNA microarray analysis. Results: CAFs from cancer tissues that showed no demarcation between ECM and epithelial cancer cells (CAF-Promote) tended to stimulate Matrigel invasion of HNSCC cells. Conversely, CAFs from cancer tissues that showed a boundary with epithelial cancer cells (CAF-Delay) caused no remarkable increase in Matrigel invasion. Compared with CAF-P, CAF-D is less effective in promoting FaDu tumorigenicity in the mouse model. In DNA microarray analysis, COL3A1 and COL6A6 showed particularly high expression in the CAF-D group. Conclusions: These cancer stroma-derived collagen proteins might delay the HNSCC progression. These findings are expected to provide vital information for predicting HNSCC prognosis and developing drug targets in the future.

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Pancreatic Cancer Molecular Classifications: From Bulk Genomics to Single Cell Analysis

Cited by 21 publications

References 72 publications

A Cancer Cell Cluster Marked by LincRNA MEG3 Leads Pancreatic Ductal Adenocarcinoma Metastasis

A Cancer Cell Cluster Marked by LincRNA MEG3 Leads Pancreatic Ductal Adenocarcinoma Metastasis

Identification of prognosis-related molecular subgroups based on DNA methylation in pancreatic cancer

Cancer-Associated Fibroblast Subgroups Showing Differential Promoting Effect on HNSCC Progression

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