Objective To evaluate the utility of collagen-gel droplet embedded-culture drug sensitivity test (CD-DST) in pancreatic carcinoma cell by compared with WST-8. Methods The chemosensitivity to 5-fluorouracil (5-FU), gemzar (GEM) and oxaliplatin (OXA) of pancreatic adenocarcinoma cells SW1990, PCT-3 and ASPC-1 were tested by WST-8 and CD-DST respectively. Results In a certain living cell number range (500-10 000), there was a linear correlation (r=0.991 1, P＜0.05) between the integral optical density in CD-DST and the cell number. The inhibition ratios of three kinds of cell growth tested by CD-DST were higher than those tested by WST-8 (P＜0.05). The results of drug chemosensitivity to 5-FU， GEM and OXA detected by two methods were uniform. Conclusion The CD-DST can be used to assay the drug chemosensitivity in vitro for pancreatic carcinoma.
【Abstract】ObjectiveTo study the relationship between the bacterial spectrum difference of gallbladder mucosa and choledochus bile and clinical prognosis of gallstone pancreatitis. MethodsA synchronic bacterial culture and drug sensitivity test were carried out on 48 patients with gallstone pancreatitis. ResultsThe cases of positive gallbladder mucosa and choledochus bile were 13 (27.1%) and 31 (64.6%) respectively. The cases of double positivity were 12 (25.0%). The cultural strains of gallbladder mucosa and choledochus bile were significantly different. Some strains were only found in choledochus bile，whereas in gallbladder mucosa L-form bacteria predominated.ConclusionThe most common causative strain of gallstone pancreatitis is Bacterium coli. The drug-resistant strain emerges maybe due to bacterium immigration and delitescence in gallbladder mucosa. L-form bacteria should be considered when using antibiotics, because L-form bacteria have close relationship with the prognosis of gallstone pancreatitis.
ObjectiveTo summarize the clinical application and future application prospects of organoid model in pancreatic cancer. MethodThe domestic and foreign literature related on the application of organoid model in pancreatic cancer was reviewed. ResultsIn recent years, the organoid model of pancreatic cancer was constructed mainly using patient-derived tissues, fine-needle aspiration samples, and human pluripotent stem cells. The biomarkers of pancreatic cancer were screened according to the histological and structural heterogeneities of the primary tumor retained in organoid model, such as microRNA, glypican-1, annexin A6 and protein biomarkers cytokeratin 7 and 20, cell tumor antigen p53, Claudin-4, carbohydrate antigen 19-9, etc.in the extracellular vesicles. The results of organoid model could maintain the original tumor characteristics and the higher correlation between the organoid model drug sensitivity data and the clinical results of pancreatic cancer patients suggested that, the drug sensitivity data of organoid model could be used to avoid ineffective chemotherapy, so as to improve the treatment response rate and reduce the toxicity of chemical drug treatment, and reasonably select individualized treatment plans for pancreatic cancer patients in future. ConclusionsOrganoid model has many research in screening biomarkers of pancreatic cancer, individualized drug screening, and drug sensitivity test. It can simulate the complex pathophysiological characteristics of pancreatic cancer in vitro, and retain the physiological characteristics and gene phenotype of original tumor cells. It is expected to become a new platform for selecting biomarkers of pancreatic cancer, testing drug sensitivity, and formulating individualized treatment methods for pancreatic cancer, which might further accelerate the research progress of pancreatic cancer.
ObjectiveTo construct a prognostic model of esophageal squamous cell carcinoma (ESCC) based on immune checkpoint-related genes and explore the potential relationship between these genes and the tumor microenvironment (TME). Methods The transcriptome sequencing data and clinical information of immune checkpoint genes of samples from GSE53625 in GEO database were collected. The difference of gene expression between ESCC and normal paracancerous tissues was evaluated, and the drug sensitivity of differentially expressed genes in ESCC was analyzed. We then constructed a risk model based on survival-related genes and explored the prognostic characteristics, enriched pathway, immune checkpoints, immune score, immune cell infiltration, and potentially sensitive drugs of different risk groups. ResultsA total of 358 samples from 179 patients were enrolled, including 179 ESCC samples and 179 corresponding paracancerous tissues. There were 33 males and 146 females, including 80 patients≤60 years and 99 patients>60 years. 39 immune checkpoint genes were differentially expressed in ESCC, including 14 low expression genes and 25 high expression genes. Drug sensitivity analysis of 8 highly expressed genes (TNFRSF8, CTLA4, TNFRSF4, CD276, TNFSF4, IDO1, CD80, TNFRSF18) showed that many compounds were sensitive to these immunotherapy targets. A risk model based on three prognostic genes (NRP1, ICOSLG, HHLA2) was constructed by the least absolute shrinkage and selection operator analysis. It was found that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P<0.001). Similar results were obtained in different ESCC subtypes. The risk score based on the immune checkpoint gene was identified as an independent prognostic factor for ESCC. Different risk groups had unique enriched pathways, immune cell infiltration, TME, and sensitive drugs. Conclusion A prognostic model based on immune checkpoint gene is established, which can accurately stratify ESCC and provide potential sensitive drugs for ESCC with different risks, thus providing a possibility for personalized treatment of ESCC.
ObjectiveTo analyze the correlations between the immune function and inflammatory factors levels of patients with hepatocellular carcinoma (HCC) and the results of in vitro high-throughput drug sensitivity, and to provide a reference for personalized drug selection for patients with HCC. MethodsThe patients with HCC who met the inclusion criteria from December 2019 to June 2021 in the First Affiliated Hospital of Chongqing Medical University were included. The HCC cells were used to perform in vitro high-throughput drug sensitivity screening, the result was classified into sensitive and insensitive. The correlations between drug sensitivity results and immune function and inflammatory factors levels of corresponding patients were analyzed, and the relation between these indexes (P<0.05) and drug sensitivity of HCC cells to drugs or combination regimen of drugs was further analyzed by univariate logistic regression. ResultsA total of 74 patients with HCC were included in this study. The results showed that the level of interleukin-6 was negatively correlated with sorafenib, caffezomib, gemcitabine, oxaliplatin + epirubicin + irinotecan + 5-fluorouracil, oxaliplatin + irinotecan + epirubicin, and oxaliplatin + epirubicin regimens on the inhibition rates of HCC in vitro (P<0.05), and positively correlated with bortezomib (P<0.05). However, the level of interleukin-6 was not related to the sensitivity of HCC cells to these single drugs or combined regimens (P>0.05). Meanwhile it was found that tumor necrosis factor (TNF)-α was negatively correlated with cabotinib, apatinib, caffezomib, and epirubicin on the inhibition rates of HCC in vitro (P<0.05), and positively correlated with epirubicin (P<0.05). But only it was found that tumor necrosis factor-α level was related to the sensitivity of HCC cells to epirubicin (P<0.05). ConclusionsTumor necrosis factor-α level in peripheral blood of patients with HCC has a certain relation with epirubicin on inhibition rate of HCC in vitro and it might have a certain value in predicting sensitivity of HCC cells to epirubicin. Meanwhile, although it is found that level of IL-6 is related to sorafenib, caffezomib, gemcitabine, or including combination regiems including oxaliplatin and epirubicin on inhibition rates of HCC in vitro, their value is not found in predicting sensitivity of HCC cells to these single drugs or combined regimens.
ObjectiveTo reveal the expression patterns of tertiary lymphoid structure (TLS)-related gene features in non-small cell lung cancer (NSCLC), and further construct a prognostic prediction model for NSCLC patients based on machine learning, as well as evaluate the correlation between the TLS risk score and tumor immune microenvironment characteristics and potential immunotherapy benefits. MethodsThe training cohort was derived from the NSCLC dataset of The Cancer Genome Atlas (TCGA) database, including 994 tumor samples with survival time >0 days (and 110 normal tissue samples for differential expression analysis). External validation cohorts were obtained from the Gene Expression Omnibus (GEO) database, including GSE30219 (n=289) and GSE72094 (n=398). Based on the expression levels of TLS-related genes, consensus clustering was performed to identify molecular subtypes associated with TLS. Weighted gene co-expression network analysis (WGCNA) was applied to screen co-expression modules significantly correlated with TLS subtypes. To construct the TLS prognostic model, 101 algorithm combinations comprising 10 machine learning algorithms were employed for model training and selection. A high-confidence TLS prognostic model was established and systematically evaluated for its predictive performance in both the training cohort and external validation cohorts. Additionally, associations between the model and clinical characteristics as well as immune microenvironment indicators were analyzed. ResultsConsensus clustering identified three TLS molecular subtypes in the TCGA-NSCLC cohort (n=994): C1 (n=441), C2 (n=263), and C3 (n=290). These subtypes exhibited distinct overall survival outcomes and demonstrated differences in clinical characteristics and immune infiltration levels. Under the soft threshold β=9 condition, WGCNA identified seven co-expression modules, among which the blue module (r=0.32) and yellow module (r=0.44) showed the highest correlations with TLS subtypes. From these two modules containing 758 genes, univariate Cox regression analysis selected 32 prognosis-related genes. Through optimization across 101 algorithm combinations, the optimal TLS prognostic model was established and validated in external cohorts. This model stratified patients into high-risk and low-risk groups, demonstrating stable prognostic discrimination capability in TCGA, GSE30219, and GSE72094 datasets. Immune infiltration analysis revealed significantly higher infiltration levels of multiple immune cell types in the low-risk group. Drug sensitivity analysis indicated that the low-risk group exhibited greater sensitivity to cisplatin, docetaxel, gemcitabine, and paclitaxel. Additionally, pharmacological screening identified four potential candidate drugs (BI-2536, GSK461364, Paclitaxel, SB-743921) in the Cancer Therapeutics Response Portal (CTRP) database and three candidates (Epothilone-b, Mitoxantrone, Volasertib) in the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) database for high-risk group patients. ConclusionThe TLS risk score serves as an independent prognostic factor effectively predicting NSCLC patient outcomes, representing a potential biomarker for NSCLC.