During the last ten years, many research results have been referring to a particular type of cancer-associated fibroblasts associated with poor prognosis, invasiveness, metastasis and resistance to therapy in multiple cancer types, characterized by a gene expression signature with prominent presence of genes COL11A1, THBS2 and INHBA. Identifying the underlying biological mechanisms responsible for their creation may facilitate the discovery of targets for potential pan-cancer therapeutics. Using a novel computational approach for single-cell gene expression data analysis identifying the dominant cell populations in a sequence of samples from patients at various stages, we conclude that these fibroblasts are produced by a pan-cancer cellular transition originating from a particular type of adipose-derived stromal cells naturally present in the stromal vascular fraction of normal adipose tissue, having a characteristic gene expression signature. Focusing on a rich pancreatic cancer dataset, we provide a detailed description of the continuous modification of the gene expression profiles of cells as they transition from APOD-expressing adipose-derived stromal cells to COL11A1-expressing cancer-associated fibroblasts, identifying the key genes that participate in this transition. These results also provide an explanation to the well-known fact that the adipose microenvironment contributes to cancer progression.

Background: Lung adenocarcinoma (LUAD) is a heterogeneous disease characterized by high mortality and poor prognosis. Ferroptosis, a newly discovered iron-dependent type of cell death, has been found to play a crucial role in the development of cancers. However, little is known about the prognostic value of ferroptosis-related genes (FRGs) in LUAD. Methods: In the present study, RNA-seq transcriptome data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database. Cox regression analysis was used to construct a multigene signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were utilized to assess the prognostic prediction efficiency of the constructed survival model. LUAD patients from the GSE30219 dataset were used for validation. Results: We found 46 differentially expressed FRGs between LUAD and adjacent normal tissues. Via univariate and multivariate Cox regression analyses, 5 differentially expressed FRGs were identified as being highly correlated with LUAD. Patients were divided into low-and high-risk groups according to the risk score. We found that the overall survival (OS) of patients in the high-risk group was significantly worse than that of their low-risk counterparts. (P < 0.0001 in the TCGA dataset and P = 0.044 in the GSE30219 cohort). In addition, gene set variation analysis (GSVA) of the tumor microenvironment of the two groups may explain the different survival of LUAD patients. Conclusions: Our study identified a novel FRG signature that could be used to evaluate and predict the prognosis of LUAD patients, which might provide a new therapeutic target for the treatment of LUAD patients.

Background Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world, and its 5-year survival rate is less than 20%, despite various treatments being available. Increasing evidence indicates that alternative splicing (AS) plays a nonnegligible role in the formation and development of the tumor microenvironment (TME). However, the comprehensive analysis of the impact on prognostic AS events on immune-related perspectives in HCC is lacking but urgently needed. Methods The transcriptional data and clinical information of HCC patients were downloaded from TCGA (The Cancer Genome Atlas) database for calculating immune and stromal scores by ESTIMATE algorithm. We then divided patients into high/low score groups and explored their prognostic significance using Kaplan–Meier curves. Based on stromal and immune scores, differentially expressed AS events (DEASs) were screened and evaluated with functional enrichment analysis. Additionally, a risk score model was established by applying univariate and multivariate Cox regression analyses. Finally, gene set variation analysis (GSVA) was adopted to explore differences in biological behaviors between the high- and low-risk subgroups. Results A total of 370 HCC patients with complete and qualified corresponding data were included in the subsequent analysis. According to the results of ESTIMATE analysis, we observed that the high immune/stromal score group had a longer survival probability, which was significantly correlated with prognosis in HCC patients. In addition, 467 stromal/immune score-related DEASs were identified, and enrichment analysis revealed that DEASs were significantly enriched in pathways related to HCC tumorigenesis and the immune microenvironment. More importantly, the final prognostic signature containing 16 DEASs showed powerful predictive ability. Finally, GSVA demonstrated that activation of carcinogenic pathways and immune-related pathways in the high-risk group may lead to poor prognosis. Conclusions Collectively, these outcomes revealed prognostic AS events related to carcinogenesis and the immune microenvironment, which may yield new directions for HCC immunotherapy.

During the last ten years, many research results have been referring to a particular type of cancer-associated fibroblasts associated with invasiveness, metastasis and resistance to therapy, present in nearly identical form in many individual types of solid cancer. These fibroblasts are characterized by the expression of several genes, prominent among them COL11A1, THBS2, and INHBA. Identifying the origin of this universal metastasis-associated fibroblastic cell population and the underlying biological mechanisms responsible for their creation may help towards the identification of drug targets for pan-cancer metastasis-inhibiting therapeutics. This information, however, remains elusive. We have performed an extensive computational analysis of single-cell gene expression data from many cancer types, concluding that these fibroblasts are produced by a cancer-associated transformation of naturally occurring normal adipose-derived stromal cells. Focusing on a rich pancreatic cancer dataset, we also provide a detailed description of the continuous modification of the gene expression profile of the fibroblastic population as they transition from APOD-expressing adipose-derived stromal cells to COL11A1-expressing metastasis-associated fibroblasts, identifying the key genes that participate in this transformation.

Objective:To analyze the changes in downstream genes, signaling pathways, and proteins based on the difference of microRNA (miRNA) expression in neuroblastoma (NB). Methods: GSE128004 second-generation sequencing expression data were downloaded from GEO, and Limma package of R language was used to analyze differential expression, and a volcano map and heat map were drawn; the target genes corresponding to the differential miRNA were found using the miWalk web tool, and GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were performed.The key genes were identified and verified in the TCGA database. Results: A total of 34 differentially expressed miRNAs were screened out. Among them, 22 up-regulated miRNAs predicted 1163 target genes and 12 down-regulated miRNAs predicted 1474 target genes. Target genes were enriched and analyzed by KEGG to find the FOXO signal pathway, mTOR signal pathway, AMPK signal pathway, and other signal pathways. After GO analysis, axon formation, regulation of chemical synaptic transmitters, regulation of nerve synapses, regulation of cross-synaptic signals, and other physiological processes were assessed. A total of 16 key genes were obtained by PPI analysis, and the survival analysis of TP53 and ATM genes verified in the TCGA database showed statistical significance. Conclusion:The 34 differential miRNAs may be related to the occurrence and development of NB. TP53 and ATM are related to the prognosis of NB. The role and mechanism of TP53 and ATM in NB need to be further verified.