Background Biologic agents that reverse early changes in the aerodigestive tract mucosa have potential treatment applications for patients with field cancerization of the upper aerodigestive tract. Sodium butyrate (BA) is a normal dietary constituent that induces differentiation and inhibits growth in several malignant cell types in vitro, but its effect on head and neck squamous cell carcinoma (HNSCC) has not been evaluated. Methods Using five HNSCC cell lines, the effects of BA on cell proliferation and apoptosis were examined by colorimetric and fluorescence‐labeling methods, and the expression of differentiation markers and apoptosis‐related proteins were analyzed using Western and Northern blotting, flow cytometry, and cell cycle analysis. Results BA‐induced growth inhibition and apoptosis in HNSCC cells at millimolar concentrations. Apoptosis induction did not depend on the p53 status of the cell lines or on expression of members of the Bcl‐2/Bax family. Conclusions These results demonstrate that butyrate has activity against HNSCC in vitro and may have clinical applications for management of HNSCC patients. © 2000 John Wiley & Sons, Inc. Head Neck 22: 247–256, 2000.
Ovarian cancer (OC) is one of the most malignant tumors whose mortality rate ranks first in gynecological tumors. Although immunotherapy sheds new light on clinical treatments, the low response still restricts its clinical use because of the unique characteristics of OC such as immunosuppressive microenvironment and unstable genomes. Further exploration on determining an efficient biomarker to predict the immunotherapy response of OC patients is of vital importance. In this study, integrative analyses were performed systematically using transcriptome profiles and somatic mutation data from The Cancer Genome Atlas (TCGA) based on the immune microenvironment and genomic instability of OC patients. Firstly, intersection analysis was conducted to identify immune-related differentially expressed genes (DEGs) and genomic instability-related DEGs. Secondly, Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3A (APOBEC3A) was recognized as a protective factor for OC, which was also verified through basic experiments such as quantitative reverse transcription PCR (RT-qPCR), immunohistochemistry (IHC), Cell Counting Kit-8 (CCK-8), and transwell assays. Thirdly, the correlation analyses of APOBEC3A expression with tumor-infiltrating immune cells (TICs), inhibitory checkpoint molecules (ICPs), Immunophenoscores (IPS), and response to anti-PD-L1 immunotherapy were further applied along with single-sample GSEA (ssGSEA), demonstrating APOBEC3A as a promising biomarker to forecast the immunotherapy response of OC patients. Last, the relationship between APOBEC3A expression with tumor mutation burden (TMB), DNA damage response (DDR) genes, and m6A-related regulators was also analyzed along with the experimental verification of immunofluorescence (IF) and RT-qPCR, comprehensively confirming the intimate association of APOBEC3A with genomic instability in OC. In conclusion, APOBEC3A was identified as a protective signature and a promising prognostic biomarker for forecasting the survival and immunotherapy effect of OC patients, which might accelerate the clinical application and improve immunotherapy effect.
Background. Cervical cancer (CC) has long been a concern, as a gynecological cancer type of high-risk. At present, there are few studies on the early detection of CC at the genetic level. The breakthrough is to recognize CC patients tending to have a worse prognosis by checking the expression pattern of ferroptosis-related genes, which enjoy a great potential of being applied to cancer treatment. Methods. Data used in this study was obtained from a series of public online databases, integrated with ferroptosis-related gene collection stored from the FerrDb database and GeneCards database. The least absolute shrinkage and selection operator- (LASSO-) penalized analysis was taken for modeling, and before, univariate Cox regression analysis got done to shrink the candidates’ range. Several analyses were made for the evaluation of the efficacy of the new model, based on CC patients’ overall survival (OS). Tumor microenvironment- (TME-) related analyses were conducted by various algorithms on different populations, comprising CIBERSORT, ssGSEA, XCELL, etc. Nonnegative matrix factorization (NMF) clustering got applied to find that ferroptosis-marker genes affect prognosis more than “driver” and “suppressor”. Hub-gene PTGS2 was screened out by protein-protein interaction analysis and real-time qPCR after ferroptosis induction, and ELISA was conducted for further verification on the correlation between ferroptosis and M1 polarization. Results. The twenty-five ferroptosis-related genes model can estimate the prognosis of patients independently of other clinical factors, and the low-risk score group shows higher expression of immune-enhancing cells, noteworthily for M1 macrophages. It is experimentally validated that the M1 marker TNF-α significantly increased after coculturing M1 macrophages and SiHa cells processed with ferroptosis inductor before. The key gene to the model, PTGS2, presented to be a risk factor in cervical cancer, and its low-expression group has stronger immune activity and higher tumor mutation burden, with the significantly highly mutated gene TENM2 in it showing high drug sensitivity and neoantigen for patients with its mutant-type. Meanwhile, it influences macrophage polarization. Conclusion. Prognosis of early-stage cervical cancer patients can be exactly predicted on ferroptosis-related genes. Among model genes, PTGS2 may have a major impact by affecting macrophage polarization and mutation effects.
Cervical cancer (CC) is a malignancy that tends to have a poor prognosis when detected at an advanced stage; however, there are few studies on the early detection of CC at the genetic level. The tumor microenvironment (TME) and genomic instability (GI) greatly affect the survival of tumor patients via effects on carcinogenesis, tumor growth, and resistance. It is necessary to identify biomarkers simultaneously correlated with components of the TME and with GI, as these could predict the survival of patients and the efficacy of immunotherapy. In this study, we extracted somatic mutational data and transcriptome information of CC cases from The Cancer Genome Atlas, and the GSE44001 dataset from the Gene Expression Omnibus database was downloaded for external verification. Stromal components differed most between genomic unstable and genomic stable groups. Differentially expressed genes were screened out on the basis of GI and StromalScore, using somatic mutation information and ESTIMATE methods. We obtained the intersection of GI- and StromalScore-related genes and used them to establish a four-gene signature comprising RIPOR2, CCL22, PAMR1, and FBN1 for prognostic prediction. We described immunogenomic characteristics using this risk model, with methods including CIBERSORT, gene set enrichment analysis (GSEA), and single-sample GSEA. We further explored the protective factor RIPOR2, which has a close relationship with ImmuneScore. A series of in vitro experiments, including immunohistochemistry, immunofluorescence, quantitative reverse transcription PCR, transwell assay, CCK8 assay, EdU assay, cell cycle detection, colony formation assay, and Western blotting were performed to validate RIPOR2 as an anti-tumor signature. Combined with integrative bioinformatic analyses, these experiments showed a strong relationship between RIPOR2 with tumor mutation burden, expression of genes related to DNA damage response (especially PARP1), TME-related scores, activation of immune checkpoint activation, and efficacy of immunotherapy. To summarize, RIPOR2 was successfully identified through comprehensive analyses of the TME and GI as a potential biomarker for forecasting the prognosis and immunotherapy response, which could guide clinical strategies for the treatment of CC patients.
BackgroundAlthough lipid metabolism has been proven to play a key role in the development of cancer, its significance in uveal melanoma (UM) has not yet been elucidated in the available literature.MethodsTo identify the expression patterns of lipid metabolism in 80 UM patients from the TCGA database, 47 genes involved in lipid metabolism were analyzed. Consensus clustering revealed two distinct molecular groups. ESTIMATE, TIMER, and ssGSEA analyses were done to identify the differences between the two subgroups in tumor microenvironment (TME) and immune state. Using Cox regression and Lasso regression analysis, a risk model based on differentially expressed genes (DEGs) was developed. To validate the expression of monoacylglycerol lipase (MGLL) and immune infiltration in diverse malignancies, a pan-cancer cohort from the UCSC database was utilized. Next, a single-cell sequencing analysis on UM patients from the GEO data was used to characterize the lipid metabolism in TME and the role of MGLL in UM. Finally, in vitro investigations were utilized to study the involvement of MGLL in UM.ResultsTwo molecular subgroups of UM patients have considerably varied survival rates. The majority of DEGs between the two subgroups were associated with immune-related pathways. Low immune scores, high tumor purity, a low number of immune infiltrating cells, and a comparatively low immunological state were associated with a more favorable prognosis. An examination of GO and KEGG data demonstrated that the risk model based on genes involved with lipid metabolism can accurately predict survival in patients with UM. It has been demonstrated that MGLL, a crucial gene in this paradigm, promotes the proliferation, invasion, and migration of UM cells. In addition, we discovered that MGLL is strongly expressed in macrophages, specifically M2 macrophages, which may play a function in the M2 polarization of macrophages and M2 macrophage activation in cancer cells.ConclusionThis study demonstrates that the risk model based on lipid metabolism may be useful for predicting the prognosis of patients with UM. By promoting macrophage M2 polarization, MGLL contributes to the evolution of malignancy in UM, suggesting that it may be a therapeutic target for UM.
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