Transcriptome analysis of the procession from chronic pancreatitis to pancreatic cancer and metastatic pancreatic cancer

Associations between RORγt and T-bet Expressions, clinicopathological indices and survival rate in oral Squamous cell carcinoma patients

BACKGROUND

Oral cancers are the sixth most common cancers around the world. According to the pivotal role of immune cells in the pathogenesis of oral squamous cell carcinoma (OSCC), as the frequent form of malignant epithelial neoplasm in the oral cavity, we investigated the association between the expression of RORγt and T-bet genes as two transcription factors, clinicopathologic indices, and survival rate.

METHODS AND MATERIALS

Forty-two OSCC paraffin embded-blocks tissue samples and their surgical healthy margins (as a control group) were collected. Demographic information like age and gender, and medical history including tumor stage/grade, and following-up time were registered. The RORγt and T-bet expression were assessed by qPCR. The overall survival (OS) and disease free survival (DFS) were analyzed by SPSS V.23 software.

RESULTS

The expression of RORγt and T-bet genes in OSCC patients were significantly higher than in surgical healthy margins (P < 0.001). Both expression demonstrated a significant difference between surgical healthy margins and tumor tissues related to gender and clinicopathological indices including stage and grade (P < 0.05). The expression of both genes in stage I patients was significant compared to stage IV (P < 0.05). The relation between expressions, OS, and DFS with clinical stage and histological grade of tumors was not statistically significant (P > 0.05).

CONCLUSION

Overexpression of RORγt and T-bet in OSCC patients with higher grade and stage in compare to surgical healthy margin highlighted their critical role in OSCC pathogenesis including oral epithelial cell differentiation, tumorigenesis process, and malignant transformation. Moreover, both mentioned genes can apply as prognostic biomarkers in OSCC patients. We suggest surgical healthy margin be considered as valuable biological area.

The research progress of anti-inflammatory and anti-fibrosis treatment of chronic pancreatitis

Chronic pancreatitis (CP) is a chronic progressive inflammatory disease of the pancreas, caused by multiple factors and accompanied by irreversible impairment of pancreatic internal and external secretory functions. Pathologically, atrophy of the pancreatic acini, tissue fibrosis or calcification, focal edema, inflammation, and necrosis are observed. Clinical manifestations include recurrent or persistent abdominal pain, diarrhea, emaciation, and diabetes. In addition, CP is prone to develop into pancreatic cancer(PC) due to persistent inflammation and fibrosis. The disease course is prolonged and the clinical prognosis is poor. Currently, clinical treatment of CP is still based on symptomatic treatment and there is a lack of effective etiological treatment. Encouragingly, experiments have shown that a variety of active substances have great potential in the etiological treatment of chronic pancreatitis. In this paper, we will review the pathogenesis of CP, as well as the research progress on anti-inflammatory and anti-fibrotic therapies, which will provide new ideas for the development of subsequent clinical studies and formulation of effective treatment programs, and help prevent CP from developing into pancreatic cancer and reduce the prevalence of PC as much as possible.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Distinct Murine Pancreatic Transcriptomic Signatures during Chronic Pancreatitis Recovery

We have previously demonstrated that the pancreas can recover from chronic pancreatitis (CP) lesions in the cerulein-induced mouse model. To explore how pancreatic recovery is achieved at the molecular level, we used RNA-sequencing (seq) and profiled transcriptomes during CP transition to recovery. CP was induced by intraperitoneally injecting cerulein in C57BL/6 mice. Time-matched controls (CON) were given normal saline. Pancreata were harvested from mice 4 days after the final injections (designated as CP and CON) or 4 weeks after the final injections (designated as CP recovery (CPR) and control recovery (CONR)). Pancreatic RNAs were extracted for RNA-seq and quantitative (q) PCR validation. Using RNA-seq, we identified a total of 3,600 differentially expressed genes (DEGs) in CP versus CON and 166 DEGs in CPR versus CONR. There are 132 DEGs overlapped between CP and CPR and 34 DEGs unique to CPR. A number of selected pancreatic fibrosis-relevant DEGs were validated by qPCR. The top 20 gene sets enriched from DEGs shared between CP and CPR are relevant to extracellular matrix and cancer biology, whereas the top 10 gene sets enriched from DEGs specific to CPR are pertinent to DNA methylation and specific signaling pathways. In conclusion, we identified a distinct set of DEGs in association with extracellular matrix and cancer cell activities to contrast CP and CPR. Once during ongoing CP recovery, DEGs relevant to DNA methylation and specific signaling pathways were induced to express. The DEGs shared between CP and CPR and the DEGs specific to CPR may serve as the unique transcriptomic signatures and biomarkers for determining CP recovery and monitoring potential therapeutic responses at the molecular level to reflect pancreatic histological resolution.