The construction and analysis of tumor-infiltrating immune cell and ceRNA networks in recurrent soft tissue sarcoma

Clinical and translational implications of immunotherapy in sarcomas

Immunotherapy has emerged as promising treatment in sarcomas, but the high variability in terms of histology, clinical behavior and response to treatments determines a particular challenge for its role in these neoplasms. Tumor immune microenvironment (TiME) of sarcomas reflects the heterogeneity of these tumors originating from mesenchymal cells and encompassing more than 100 histologies. Advances in the understanding of the complexity of TiME have led to an improvement of the immunotherapeutic responsiveness in sarcomas, that at first showed disappointing results. The proposed immune-classification of sarcomas based on the interaction between immune cell populations and tumor cells showed to have a prognostic and potential predictive role for immunotherapies. Several studies have explored the clinical impact of immune therapies in the management of these histotypes leading to controversial results. The presence of Tumor Infiltrating Lymphocytes (TIL) seems to correlate with an improvement in the survival of patients and with a higher responsiveness to immunotherapy. In this context, it is important to consider that also immune-related genes (IRGs) have been demonstrated to have a key role in tumorigenesis and in the building of tumor immune microenvironment. The IRGs landscape in soft tissue and bone sarcomas is characterized by the connection between several tumor-related genes that can assume a potential prognostic and predictive therapeutic role. In this paper, we reviewed the state of art of the principal immune strategies in the management of sarcomas including their clinical and translational relevance.

A novel disulfidptosis-related gene signature predicts overall survival of glioblastoma patients

Aim: The aim of this study was to investigate the prognostic relevance of disulfidptosis-related genes in glioblastoma using bioinformatic analysis in The Cancer Genome Atlas Program-Glioblastoma (TCGA-GBM) database and develop a gene signature model for predicting patient prognosis. Methods: We conducted a bioinformatic analysis using the TCGA-GBM database and employed weighted co-expression network analysis to identify disulfidptosis-related genes. Subsequently, we developed a predictive gene signature model based on these genes to stratify glioblastoma patients into high and low-risk groups. Results: Patients categorized into the high-risk group based on the disulfidptosis-related gene signature exhibited a significantly reduced survival rate in comparison to those in the low-risk group. Functional analysis also revealed notable differences in the immune status between the two risk groups. Conclusion: In conclusion, a new disulfidptosis-related gene signature can be utilised to predict prognosis in GBM.

Integrated bioinformatics and validation reveal SOX12 as potential biomarker in colon adenocarcinoma based on an immune infiltration-related ceRNA network

PURPOSE

The primary objective of this study was to construct competing endogenous RNA (ceRNA) networks and evaluate the prognostic significance of tumor-infiltrating immune cells (TIICs) and key biomarkers within the ceRNA networks in colon adenocarcinoma (COAD) patients.

METHODS

Comprehensive bioinformatics tools were used to screen differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs) related to COAD, leading to the creation of ceRNA networks. The CIBERSORT technique was employed to assess the significance of TIICs in COAD, and an immune-related prognosis prediction model was subsequently developed. Co-expression analyses were conducted to determine the relationship between key genes in ceRNA networks and immunologically significant TIICs. The study also utilized 5 GEO datasets and web-based databases to externally validate the findings.

RESULTS

The study revealed a statistically significant relationship between key hub genes and immune cells, as determined through co-expression analysis. Two hub regulators (SOX12 and H19) demonstrated significant prognostic value in the ceRNA-related prognostic model, and their elevated expression levels were verified across multiple CRC cell lines. Additionally, the knockdown of SOX12 led to a suppression of proliferation, migration, and invasion in colon cancer cells.

CONCLUSION

Through the construction of ceRNA networks and evaluation of TIICs, the study successfully established two risk score models and nomograms. These models serve as valuable tools for understanding the molecular processes and predicting the prognosis of COAD patients. Further validation of hub regulators SOX12 and H19 substantiates their potential role as key biomarkers in COAD.

Construction of a novel nomogram based on competing endogenous RNAs and tumor-infiltrating immune cells for prognosis prediction in elderly patients with colorectal cancer

Competitive endogenous RNAs (ceRNAs) and tumor-infiltrating immune cells play essential roles in colorectal cancer (CRC) tumorigenesis. However, their prognostic role in elderly patients with CRC is unclear. Gene expression profiles and clinical information for elderly patients with CRC were downloaded from The Cancer Genome Atlas. Univariate, LASSO, and multivariate Cox regression analyses were utilized for screening key ceRNAs and prevent overfitting. A total of 265 elderly patients with CRC were included. We constructed a novel ceRNA network consisting of 17 lncRNAs, 35 miRNAs, and 5 mRNAs. We established three prognosis predictive nomograms based on four key ceRNAs (ceRNA nomogram), five key immune cells (immune cell nomogram), and their combination (ceRNA-immune cell nomogram). Among them, the ceRNA-immune cell nomogram had the best accuracy. Furthermore, the areas under the curve of the ceRNA-immune cell nomogram were also significantly greater than the TNM stage at 1 (0.818 vs. 0.693), 3 (0.865 vs. 0.674), and 5 (0.832 vs. 0.627) years. Co-expression analysis revealed that CBX6 was positively correlated with activated dendritic cells (R = 0.45, p < 0.01), whereas negatively correlated with activated mast cells (R =- 0.43, p < 0.01). In conclusion, our study constructed three nomograms to predict prognosis in elderly patients with CRC, among which the ceRNA-immune cell nomogram had the best prediction accuracy. We inferred that the mechanism underlying the regulation of activated dendritic cells and mast cells by CBX6 might play a crucial role in tumor development and prognosis of elderly patients with CRC.

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Background: N⁶ methyladenosine (m⁶A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m⁶A regulators and m⁶A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.

Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m⁶A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m⁶A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.

Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.

Conclusion: This m6Ascore risk model based on m⁶A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3

INTRODUCTION

Prognostic biomarkers for osteosarcoma (OS) are still very few, and this study aims to examine 2 novel prognostic biomarkers for OS through combined bioinformatics and experimental approach.

MATERIALS AND METHODS

Expression profile data of OS and paraneoplastic tissues were downloaded from several online databases, and prognostic genes were screened by differential expression analysis, Univariate Cox analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis to construct prognostic models. The accuracy of the model was validated using principal component analysis, constructing calibration plots, and column line plots. We also analyzed the relationship between genes and drug sensitivity. Gene expression profiles were analyzed by immunocytotyping. Also, protein expressions of the constructed biomarkers in OS and paraneoplastic tissues were verified by immunohistochemistry.

RESULTS

Heparan sulfate 2-O-sulfotransferase 1 (HS2ST1) and Syndecan 3 (SDC3, met all our requirements after screening. The constructed prognostic model indicated that patients in the high-risk group had a much lower patient survival rate than in the low-risk group. Moreover, these genes were closely related to immune cells (P < .05). Drug sensitivity analysis showed that the 2 genes modeled were strongly correlated with multiple drugs. Immunohistochemical analysis showed significantly higher protein expression of both genes in OS than in paraneoplastic tissues.

CONCLUSIONS

HS2ST1 and SDC3 are significantly dysregulated in OS, and the prognostic models constructed based on these 2 genes have much lower survival rates in the high-risk group than in the low-risk group. HS2ST1 and SDC3 can be used as glycolytic and immune-related prognostic biomarkers in OS.

Pyroptosis-related prognosis model, immunocyte infiltration characterization, and competing endogenous RNA network of glioblastoma

Background

Glioblastoma (GBM) has a high incidence rate, invasive growth, and easy recurrence, and the current therapeutic effect is less than satisfying. Pyroptosis plays an important role in morbidity and progress of GBM. Meanwhile, the tumor microenvironment (TME) is involved in the progress and treatment tolerance of GBM. In the present study, we analyzed prognosis model, immunocyte infiltration characterization, and competing endogenous RNA (ceRNA) network of GBM on the basis of pyroptosis-related genes (PRGs).

Methods

The transcriptome and clinical data of 155 patients with GBM and 120 normal subjects were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). Lasso (Least absolute shrinkage and selection operator) Cox expression analysis was used in predicting prognostic markers, and its predictive ability was tested using a nomogram. A prognostic risk score formula was constructed, and CIBERSORT, ssGSEA algorithm, Tumor IMmune Estimation Resource (TIMER), and TISIDB database were used in evaluating the immunocyte infiltration characterization and tumor immune response of differential risk samples. A ceRNA network was constructed with Starbase, mirtarbase, and lncbase, and the mechanism of this regulatory axis was explored using Gene Set Enrichment Analysis (GSEA).

Results

Five PRGs (CASP3, NLRP2, TP63, GZMB, and CASP9) were identified as the independent prognostic biomarkers of GBM. Prognostic risk score formula analysis showed that the low-risk group had obvious survival advantage compared with the high-risk group, and significant differences in immunocyte infiltration and immune related function score were found. In addition, a ceRNA network of messenger RNA (CASP3, TP63)–microRNA (hsa-miR-519c-5p)–long noncoding RNA (GABPB1-AS1) was established. GSEA analysis showed that the regulatory axis played a considerable role in the extracellular matrix (ECM) and immune inflammatory response.

Conclusions

Pyroptosis and TME-related independent prognostic markers were screened in this study, and a prognosis risk score formula was established for the first time according to the prognosis PRGs. TME immunocyte infiltration characterization and immune response were assessed using ssGSEA, CIBERSORT algorithm, TIMER, and TISIDB database. Besides a ceRNA network was built up. This study not only laid foundations for further exploring pyroptosis and TME in improving prognosis of GBM, but also provided a new idea for more effective guidance on clinical immunotherapy to patients and developing new immunotherapeutic drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09706-x.

A Novel Signature of Lipid Metabolism-Related Gene Predicts Prognosis and Response to Immunotherapy in Lung Adenocarcinoma

Background: Lipid metabolism disorder, a new hallmark of cancer initiation, has been involved in lung adenocarcinoma (LUAD). However, few biomarkers about lipid metabolism-related genes (LMRGs) have been developed for prognosis prediction and clinical treatment of LUAD patients.

Methods: In this study, we constructed and validated an effective prognostic prediction model for LUAD patients depending on LMRGs. Subsequently, we investigated the prediction model from immune microenvironment, genomic changes, and immunotherapy.

Results: Then, eleven LMRGs were identified and applied to LUAD subtyping. In comparison with the high-risk group, the low-risk group exhibited a remarkably favorable prognosis, along with a higher immune score and lower tumor purity. Moreover, the low-risk group presented higher levels of immune checkpoint molecules, lower tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB), and higher likelihood of benefiting from immunotherapy. Furthermore, the genomic changes of six LMRGs (CD79A, HACD1, CYP17A1, SLCO1B3, ANGPTL4, and LDHA) were responsible for the difference in susceptibility to LUAD by greatly influencing B-cell activation.

Conclusion: Generally speaking, the LMRG model is a reliable independent biomarker for predicting adverse outcomes in LUAD patients and has the potential to facilitate risk-stratified immunotherapy.

Characterization of Immune-Related Long Non-coding RNAs to Construct a Novel Signature and Predict the Prognosis and Immune Landscape of Soft Tissue Sarcoma

Background: Increasing evidence has demonstrated that immune-related long non-coding RNAs (irlncRNAs) are critically involved in tumor initiation and progression and associated with the prognosis of various cancers. However, their role in soft tissue sarcoma (STS) remains significantly uninvestigated.

Materials and Methods: Gene expression profiles were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) for the identification of irlncRNAs. Univariate analysis and modified least absolute shrinkage and selection operator (LASSO) penalized regression analysis were employed to determine differently expressed irlncRNA (DEirlncRNA) pairs of prognostic value, and subsequently, a risk signature based on DEirlncRNA pairs was established. Furthermore, Kaplan–Meier analysis and the area under the receiver operating characteristic curve (AUC) were used to assess survival differences and the predictive accuracy of the risk signature, respectively. Lastly, the correlation of irlncRNAs with immune characteristics and chemosensitivity in patients with STS were further investigated.

Results: A total of 1088 irlncRNAs were identified, and 311 irlncRNAs were distinguished as DEirlncRNAs. A total of 130 DEirlncRNA pairs were further identified as prognostic markers, and 14 pairs were selected for establishing a risk signature. The irlncRNA-based risk signature functioned as an independent prognostic marker for STS. Compared with the patients in the high-risk group, those in the low-risk group exhibited a better prognosis and were more sensitive to several chemotherapeutic agents. In addition, the irlncRNA-based risk signature was significantly associated with immune scores, infiltrating immune cells, and the expression of several immune checkpoints.

Conclusion: In conclusion, our data revealed that the irlncRNA-based risk signature resulted in reliable prognosis, effectively predicted the immune landscape of patients with STS and was significantly correlated with chemosensitivity, thus providing insights into the potential role of irlncRNAs as prognostic biomarkers and novel therapeutic targets for STS.

A seven-gene signature and the C-C motif chemokine receptor family genes are the sarcoma-related immune genes

Cells of the tumor microenvironment exert a vital influence on sarcoma prognosis. This study aimed to analyze and identify differentially expressed genes (DEGs) related to immunity and their significance as immune biomarkers for the accurate prediction of overall survival of patients with sarcoma. The Cancer Genome Atlas was adopted for obtaining sarcoma gene microarray and corresponding clinical information. ESTIMATE algorithm was used to calculate tumor immune microenvironment indices. Immune-associated DEGs were identified using the limma packages and were further analyzed using the ClusterProfiler package and STRING website. Based on the results of these analyses, we constructed a prognostic model. Furthermore, we assessed the prognosis prediction model through functional evaluation and analysis of GSE17674. The functional analysis revealed that the upregulated immune DEGs were related to immune-related aspects. Chemokine ligands/receptors and immune-related genes were found to be vital for sarcoma formation and progression. We established a prognostic signature of seven genes, which indicated that high-risk cases exhibit poor prognostic outcome. The prognostic signature constructed in this study can accurately predict the overall prognosis in patients with sarcoma. Moreover, the novel immune gene expression analysis may provide clinical guidance for predicting prognosis in patients with sarcoma.

Clinical biomarkers in soft tissue sarcoma A comprehensive review of current soft tissue sarcoma biomarkers

Soft tissue sarcomas (STS) are a heterogeneous group of tumors that arise from mesenchymal tissue. Investigation at the molecular level has been challenging due to the rarity of STS and the number of histologic subtypes. However, recent research has provided new insight into potential genomic, proteomic, and immunological biomarkers of STS. The identification of biomarkers can improve diagnosis, prognosis, and prediction of recurrence and treatment response. This review provides an understanding of biomarkers, discussing the current status of biomarker research in STS.

Long Noncoding RNA HCG9 Promotes Osteosarcoma Progression through RAD51 by Acting as a ceRNA of miR-34b-3p

Background

Long noncoding RNAs (lncRNAs) have critical regulatory functions in biological and pathological activities during osteosarcoma progression. It is important to elucidate the expression pattern and reveal the underlying mechanisms of the newly identified lncRNAs.

Methods

Herein, we screened the differentially expressed lncRNAs in osteosarcoma tumors and cell lines using lncRNA microarray. The candidate lncRNA was further verified by qRT-PCR, and the association of gene expression with clinicopathological features was evaluated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The targeting miRNA was identified using starBase analysis, and the competing endogenous RNA (ceRNA) network was established by STRING. Overexpression and silence of RNA were detected by qRT-PCR. Osteosarcoma cell proliferation was measured with CCK-8 assay, and the migration and invasion were evaluated with Transwell assay. Colony formation assay was observed. Flow cytometry evaluated the cell cycle. Western blot was performed to detect the mitotic markers and apoptosis-related proteins. A nude mouse tumor formation experiment was used to evaluate osteosarcoma progression in vivo. Cooverexpressing miR-34b-3p with RAD51 reversed the miR-34b-3p-induced changes in proliferation, the cell cycle, the expression of H2A.X, and that of apoptosis-related proteins.

Results

HCG9 was identified as osteosarcoma-associated lncRNA. Osteosarcoma tissues and cell lines expressed higher levels of HCG9 as compared to normal tissues and osteoblasts, and high expression of HCG9 was further proved to be related to metastasis and the grade of osteosarcoma in clinical cases. Knockdown of HCG9 inhibited the proliferation, migration, and invasion of osteosarcoma cells. miR-34b-3p was identified as the target of HCG9, and RAD51 acted as a potential target of miR-34b-3p. Cooverexpressing miR-34b-3p with HCG9 partially suppressed the HCG9-stimulated proliferation, migration, and invasion of osteosarcoma cells in vitro and delayed the tumor progression in vivo.

Conclusion

We discovered that lncRNA HCG9 promoted the proliferation of osteosarcoma cells via suppressing miR-34b-3p. Our study provides novel biomarkers and potential therapeutic targets for osteosarcoma treatment.

Comprehensive analysis of metabolism-related lncRNAs related to the progression and prognosis in osteosarcoma from TCGA

Background

Osteosarcoma is one of the most common malignant neoplasms in children and adolescents. Studies have shown that metabolism-related pathways are vital for the development and metastasis of osteosarcoma. Long non-coding RNA (lncRNA) plays a key role in the occurrence and progression of cancer in a variety of ways. However, the detailed molecular mechanisms of metabolism-related lncRNA in osteosarcoma remain to be deeply elucidated.

Methods

In this study, all metabolism-related mRNAs and lncRNAs in osteosarcoma were extracted and identified based on transcriptomic data from the TCGA database. Usingsurvival analysis, univariate and multivariate independent prognostic analysis, gene set enrichment analysis, and nomogram, a prognostic signature with metabolic lncRNAs as prognostic factors was constructed.

Results

Nine prognostic factors included lncRNA AC009779.2, lncRNA AL591895.1, lncRNA AC026271.3, lncRNA LPP-AS2, lncRNA LINC01857, lncRNA AP005264.1, lncRNA LINC02454, lncRNA AL133338.1, and lncRNA AC135178.5, respectively. Survival analysis indicated that alterations of specific lncRNA expression were strongly correlated with poor prognosis in osteosarcoma. Univariate and multivariate independent prognostic analysis showed that the prognostic signature had a good independent predictive ability for patient survival. The results of GSEA suggested that these predictors may be involved in the metabolism of certain substances or energy in cancer. The nomogram was further drawn for clinical guidance and assistance in clinical decision-making.

Conclusions

This study identified multiple metabolism-related lncRNAs, which may be novel therapeutic targets for osteosarcoma, and contributed to better explore the specific metabolic regulatory mechanisms of lncRNA in osteosarcoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-021-02647-4.

A prognostic nomogram based on competing endogenous RNA network for clear-cell renal cell carcinoma

Background

Clear‐cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts in understanding the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) was involved in the development of varied tumors. However, a comprehensive analysis of the prognostic model based on lncRNA‐miRNA‐mRNA ceRNA regulatory network of ccRCC with large‐scale sample size and RNA‐sequencing expression data is still limited.

Methods

RNA‐sequencing expression data were taken out from GTEx database and TCGA database, a total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC‐specific genes were obtained by WGCNA and differential expression analysis. Following, the communication of mRNAs and lncRNAs with targeted miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was further constructed by univariate Cox regression, Lasso methods, and multivariate Cox regression analysis.

Results

A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dysregulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature including eight genes based on this ceRNA was determined followed by the development of a nomogram predicting 1‐, 3‐, and 5‐year survival probability for ccRCC.

Conclusion

It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

Construction of Two Alternative Polyadenylation Signatures to Predict the Prognosis of Sarcoma Patients

Background

Increasing evidence indicates that alternative polyadenylation (APA) is associated with the prognosis of cancers.

Methods

We obtained gene expression and APA profiles of 259 sarcoma patients from the TCGA dataportal and TC3A database, respectively. The prognostic signatures, clinical nomograms, and regulatory networks were studied by integrated bioinformatics analyses. Then, the immune cell infiltration profile was obtained from the ImmuCellAI. The association between APA-based signature and immune cells was studied.

Results

A total of 61 and 38 APA events were identified as overall survival (OS)- and progress free-survival (PFS)-related biomarkers, respectively. Two signatures were generated. The area under the curves (AUC) values of OS signature were 0.900, 0.928, and 0.963 over 2-, 4-, and 6-years, respectively. And the AUC values of PFS signature at 2-, 4-, and 6-years were 0.826, 0.840, and 0.847, respectively. Overall and subgroup analyses indicated that high-risk patients had a worse prognosis than low-risk patients (all p-values < 0.05). In addition, immunomics analyses indicated that there are different patterns of immune cell infiltration between low- and high-risk patients. Furthermore, two clinical-APA nomograms were established and the C-indexes were 0.813 and 0.809 for OS nomogram and PFS nomogram, respectively. Finally, two APA regulatory networks were constructed. FIP1L1-VPS26B was identified as a key regulating relationship and validated in the pan-cancer analyses.

Conclusion

In this study, we identified prognostic predictors based on APA events with high accuracy for risk stratification in sarcoma patients and uncovered interesting regulatory networks in sarcoma that could be underlying mechanisms. This study not only provides novel potential prognostic biomarkers but promote precision medicine and provide potential novel research interests for immunotherapy.

Construction and analysis of a ceRNA network and patterns of immune infiltration in bladder cancer

Background

Bladder cancer (BC) is the ninth most common malignant tumor, accounting for an estimate of 549,000 new BC cases and 200,000 BC-related deaths worldwide in 2018. The prognosis of BC has not substantially improved despite significant advances in the diagnosis and treatment of the disease.

Methods

The RNA sequencing (RNA-seq) data and clinical information of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm was used to assess immune infiltration. The survival analyses were performed using the selected components of a ceRNA network and selected immune cell types by least absolute shrinkage and selection operator (LASSO) Cox regression to calculate the risk score. The accuracy of prognosis prediction was determined by receiver operating characteristic (ROC) curves, survival curves, and nomograms. Finally, the correlation analysis was performed to investigate the relationships between the signature components of the ceRNA network and the immune cell signature.

Results

Two completed survival analyses included selected components of the ceRNA network (ELN, SREBF1, DSC2, TTLL7, DIP2C, SATB1, hsa-miR-20a-5p, and hsa-miR-29c-3p) and selected immune cell types (M0 macrophages, M2 macrophages, resting mast cells, and neutrophils). ROC curves, survival curves (all P values <0.05), nomograms, and calibration curves indicated that the accuracy of the two survival analyses was acceptable. Moreover, the correlations between TTLL7 and resting mast cells (R=0.24, P<0.001), DSC2 and resting mast cells (R=−0.23, P<0.001), ELN and resting mast cells (R=0.44, P<0.001), and hsa-miR-29c-3p and M0 macrophages (R=−0.29, P<0.001) were significant, indicating that interactions of these factors may play significant roles in the prognosis of BC.

Conclusions

TTLL7, DSC2, ELN, hsa-miR-29c-3p, resting mast cells, and M0 macrophages may play an important role in the development of BC. However, additional studies are needed to confirm this hypothesis.

Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is malignant hematologic tumors with frequent recurrence and cause high mortality. Its fate is determined by abnormal intracellular competitive endogenous RNA (ceRNA) network and extracellular tumor microenvironment (TME). This study aims to build a ceRNA network related to AML TME to explore new prognostic and therapeutic targets. The RNA expression data of AML were obtained from The Cancer Genome Atlas (TCGA) database. First, we used the ESTIMATE algorithm to calculate the immune cells and stromal cells infiltration scores in the TME and found that all scores were highly correlated with AML’s prognostic characteristics. Subsequently, differentially expressed mRNAs and lncRNAs between high and low score groups were identified to construct a TME-related ceRNA network. Further, the Cox-lasso survival model was employed to screen out the hub prognostic ceRNA network composed of two mRNAs (EPB41L3, COL2A1), three miRNAs (hsa-mir-26a-5p, hsa-mir-148b-3p, hsa-mir-148a-3p), and two lncRNAs (CYP1B1-AS1, C9orf106), and construct nomograms. Finally, we used CIBERSORT algorithm and Kaplan-Meier survival analysis to identify the prognostic TME immune cells and found that naive B cells, M2-type macrophages, and helper follicular T cells were related to prognosis, and the hub ceRNAs were highly correlated with immune cell infiltration. This study provided a new perspective to elucidate how TME regulates AML process and put forward the new therapy strategies combining targeting tumor cells with disintegrating TME.

Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer

Metabolic changes, especially in glucose metabolism, are widely established during the occurrence and development of tumors and regarded as biological markers of pan-cancer. The well-known ‘Warburg effect’ demonstrates that cancer cells prefer aerobic glycolysis even if there is sufficient ambient oxygen. Accumulating evidence suggests that aerobic glycolysis plays a pivotal role in colorectal cancer (CRC) development. However, few studies have examined the relationship of glycolytic gene clusters with prognosis of CRC patients. Here, our aim is to build a glycolysis-associated gene signature as a biomarker for colorectal cancer. The mRNA sequencing and corresponding clinical data were downloaded from TCGA and GEO databases. Gene set enrichment analysis (GSEA) was performed, indicating that four gene clusters were significantly enriched, which revealed the inextricable relationship of CRC with glycolysis. By comparing gene expression of cancer and adjacent samples, 236 genes were identified. Univariate, multivariate, and LASSO Cox regression analyses screened out five prognostic-related genes (ENO3, GPC1, P4HA1, SPAG4, and STC2). Kaplan–Meier curves and receiver operating characteristic curves (ROC, AUC = 0.766) showed that the risk model could become an effective prognostic indicator (P < 0.001). Multivariate Cox analysis also revealed that this risk model is independent of age and TNM stages. We further validated this risk model in external cohorts (GES38832 and GSE39582), showing these five glycolytic genes could emerge as reliable predictors for CRC patients’ outcomes. Lastly, based on five genes and risk score, we construct a nomogram model assessed by C-index (0.7905) and calibration plot. In conclusion, we highlighted the clinical significance of glycolysis in CRC and constructed a glycolysis-related prognostic model, providing a promising target for glycolysis regulation in CRC.

Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

BACKGROUND

Competitive endogenous RNA (ceRNA) networks have revealed a new mechanism of interaction between RNAs, and play crucial roles in multiple biological processes and development of neoplasms. They might serve as diagnostic and prognosis markers as well as therapeutic targets.

METHODS

In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas by comparing the gene expression profiles between sarcoma and normal muscle samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed using Cytoscape software. In addition, the protein-protein interaction (PPI) network and survival analysis were performed.

RESULTS

A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients.

CONCLUSIONS

Overall, we constructed a ceRNA network in sarcomas, which might provide insights for further research on the molecular mechanism and potential prognosis biomarkers.

Bioinformatics analysis reveals the competing endogenous RNA (ceRNA) coexpression network in the tumor microenvironment and prognostic biomarkers in soft tissue sarcomas

Soft tissue sarcomas (STSs) are rare, heterogeneous mesenchymal neoplasias. Understanding the tumor microenvironment (TME) and identifying potential biomarkers for prognosis associated with the TME of STS might provide effective clues for immune therapy. We evaluated the immune scores and stromal scores of STS patients by using the RNA sequencing dataset from The Cancer Genome Atlas (TCGA) database and the ESTIMATE algorithm. Then, the differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs) were identified after comparing the high- and low-score groups. Next, we established a competing endogenous RNA (ceRNA) network and explored the prognostic values of biomarkers involved in the network with the help of bioinformatics analysis. High immune score was significantly associated with favorable overall survival in STS patients. A total of 328 DEGs, 18 DEMs and 67 DELs commonly regulated in the immune and stromal score groups were obtained. A ceRNA network and protein-protein interaction (PPI) network identified some hub nodes with considerable importance in the network. Kaplan-Meier survival analysis demonstrated that nine mRNAs, two miRNAs and three lncRNAs were closely associated with overall survival of STS patients. Gene set enrichment analysis (GSEA) suggested that these three lncRNAs were mainly involved in immune response-associated pathways in STS patients. Finally, the expression levels of five mRNAs (APOL1, EFEMP1, LYZ, RARRES1 and TNFAIP2) were verified, which were consistent with the results of the TCGA cohort. The results of our study confirmed the prognostic value of immune scores for STS patients. We also identified several TME-related biomarkers that might contribute to prognostic prediction and immune therapy.

Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

Background

Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs.

Methods

We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed.

Results

A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis (P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs.

Conclusions

In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07852-2.

DNA methylation patterns-based subtype distinction and identification of soft tissue sarcoma prognosis

Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database. Eventually, samples were clustered into 4 subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above-described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer-associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. This study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

The Construction and Analysis of Tumor-Infiltrating Immune Cells and ceRNA Networks in Bladder Cancer

Background

Bladder cancer (BLCA) is the 11th most common malignancy worldwide. Although significant improvements have been made in screening, diagnosis, and precise management in recent years, the prognosis of BLCA remains bleak.

Objectives

This study aimed to investigate the prognostic significance of tumor-infiltrating immune cells and construct ceRNA networks in BLCA patients.

Methods

The expression data of BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. A competing endogenous RNA (ceRNA) network was constructed to identify the hub genes involved in the prognosis of BLCA. The CIBERSORT algorithm was utilized to investigate the infiltration levels of 22 subsets of immune cells. Ultimately, the nomogram was generated to visualize the survival probability of each patient, with the calibration curve being performed to assess its performance. Furthermore, the Pearson correlation test was used to explore the correlation between the identified hub genes in the ceRNA network and the prognostic-related immune cells.

Results

A total of eight elements in the ceRNA network were considered as key members and correlated with the prognosis of BLCA, including ELN, SREBF1, DSC2, TTLL7, DIP2C, SATB1, hsa-miR-20a-5p, and hsa-miR-29c-3p. T cells CD8, T cells follicular helper (Tfh), and neutrophils were identified as independent prognostic factors in BLCA. The co-expression analysis showed that there was a significant correlation between the identified hub genes and immune cells.

Conclusion

Our results suggest that the mechanism of hsa-miR-29c-3p regulates the expression of ELN and DSC2, and the infiltration of Tfh and neutrophils might play pivotal roles in the progression of BLCA.

Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

Background

The innovation of immune checkpoint blockade (ICB) represents a promising shift in the treatment of advanced hepatocellular carcinoma (HCC). However, response to ICB has varied largely due to the high tumor heterogeneity and complex tumor microenvironment (TME). The competitive endogenous RNA (ceRNA) network also plays an important role in tumor occurrence and progression, but its relation with tumor-infiltrating immune cells (TICs) remains largely unexplored in HCC. The overriding objective of our study was thus to construct a prognosis-related risk model and to further evaluate the relationship between ceRNA networks and TICs.

Methods

Differentially expressed gene (DEG) analysis was performed to identify the differentially expressed RNAs. Lasso and multivariable Cox regression analyses were used to construct risk models, which were assessed by the area under the receiver operating characteristic curve (AUC of ROC) and Kaplan-Meier (K-M) curves. Then, a single-sample gene set enrichment analysis (ssGSEA) algorithm was adopted to dissect the TICs in HCC samples. Nomograms were constructed and calibration curves were used to verify the discrimination and accuracy of the nomograms. Finally, integration analysis was performed to validate the correlation of ceRNA and TICs.

Results

In the study, 7 differentially expressed RNAs [5 messenger RNA s (mRNAs) and 2 micro RNAs (miRNAs)] were incorporated to construct a ceRNA risk model. The AUC of the 1-, 3-, and 5-year overall survival (OS) were 0.784, 0.685, and 0.691 respectively. Likewise, 7 types TICs were in the TICs signature model and the AUC of the 1-, 3-, and 5-year OS were 0.706, 0.731, and 0.721 respectively. The integration analysis showed that 7 pairs of mRNA-TICs and 1 pair of miRNA-TICs had a close relation (all correlation coefficients >0.2, P<0.001).

Conclusions

Through constructing two risk models based on ceRNA network and TICs, we identified the hub RNAs and key TICs in the progression and prognosis of HCC, and further explored the relationship between ceRNA and TME. Importantly, targeting these hub RNAs may facilitate the remodeling of the TME and be a potential therapeutic alternative to enhancing the response to ICB, thus improving the prognosis of HCC patients.

Long non-coding RNA LPP-AS2 promotes glioma tumorigenesis via miR-7-5p/EGFR/PI3K/AKT/c-MYC feedback loop

Background

Glioblastoma is the most common primary malignant intracranial tumor with poor clinical prognosis in adults. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) function as important regulators in cancer progression, including glioblastoma. Here, we identified a new lncRNA LPP antisense RNA-2 (LPP-AS2) and investigated its function and mechanism in the development of glioma.

Methods

High-throughput RNA sequencing was performed to discriminate differentially expressed lncRNAs and mRNAs between glioma tissues and normal brain tissues. Expression of LPP-AS2, epidermal growth factor receptor (EGFR) and miR-7-5p in glioma tissues and cell lines was detected by real-time quantitative PCR (RT-qPCR), and the functions of lncRNA LPP-AS2 in glioma were assessed by in vivo and in vitro assays. Insight into the underlying mechanism of competitive endogenous RNAs (ceRNAs) was obtained via bioinformatic analysis, dual luciferase reporter assays, RNA pulldown assays, RNA immunoprecipitation (RIP) and rescue experiments.

Results

The results of high-throughput RNA-seq indicated lncRNA LPP-AS2 was upregulated in glioma tissues and further confirmed by RT-qPCR. Higher LPP-AS2 expression was related to a poor prognosis in glioma patients. Based on functional studies, LPP-AS2 depletion inhibited glioma cell proliferation, invasion and promoted apoptosis in vitro and restrained tumor growth in vivo, overexpression of LPP-AS2 resulted in the opposite effects. In addition, LPP-AS2 and EGFR were observed in co-expression networks. LPP-AS2 was found to function as a ceRNA to regulate EGFR expression by sponging miR-7-5p in glioma cells. The result of chromatin immunoprecipitation (ChIP) assays validated that c-MYC binds directly to the promoter region of LPP-AS2. As a downstream protein of EGFR, c-MYC was modulated by LPP-AS2 and in turn enhanced LPP-AS2 expression. Thus, lncRNA LPP-AS2 promoted glioma tumorigenesis via a miR-7-5p/EGFR/PI3K/AKT/c-MYC feedback loop.

Conclusions

Our study elucidated that LPP-AS2 acted as an oncogene through a novel molecular pathway in glioma and might be a potential therapeutic approach for glioma diagnosis, therapy and prognosis.

The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis

Microenvironment-driven tumor heterogeneity causes the limitation of immunotherapy of sarcomas. Nonetheless, systematical studies of various molecular levels can enhance the understanding of tumor microenvironment (TME) related to prognosis and provide novel insights of precision immunotherapy. Three prognostic-related TME phenotypes were identified by consensus clustering of the relative infiltration of 22 immune cells from 869 samples of sarcomas. Additionally, integrative immunogenomic analysis is applied to explore the characteristics of different TME groups. The results revealed that most of the immune cell infiltration is higher in the better prognostic group, which are more affected by lower DNA methylation levels and fewer copy number variations in the worse prognostic group. The signaling pathway crosstalk analysis suggested that the changes in the TME will cause considerable variation in the flow of information between pathways, especially when the degree of relative infiltration of immune cells is low, patient’s endocrine system may also be significantly affected. Also, the endogenous competitive network analysis indicated that several differentially expressed long non-coding RNAs (lncRNAs) associated with the prognosis or tumor recurrence of sarcoma patients affected the regulatory relationship between miRNAs and different genes when the sarcoma microenvironment changes. In summary, the significant relationship between genetic alterations and prognostic-related TME characteristics in sarcomas were determined in this study. These findings may provide new clues for the treatment of sarcomas.

Immune profiling reveals prognostic genes in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a heterogeneous disease with diverse clinical outcomes, highlighting a need for prognostic biomarker identification. Here, we combined tumor microenvironment (TME) scores with HGSOC characteristics to identify immune-related prognostic genes through analysis of gene expression profiles and clinical patient data from The Cancer Genome Atlas and the International Cancer Genome Consortium public cohorts. We found that high TME scores (TMEscores) based on the fractions of immune cell types correlated with better overall survival. Furthermore, differential expression analysis revealed 329 differentially expressed genes between patients with high vs. low TMEscores. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that these genes participated mainly in immune-related functions and, among them, 48 TME-related genes predicted overall survival in HGSOC. Seven of those genes were associated with prognosis in an independent HGSOC database. Finally, the two genes with the lowest p-values in the prognostic analysis (GBP1, ETV7) were verified through in vitro experiments. These findings reveal specific TME-related genes that could serve as effective prognostic biomarkers for HGSOC.

PIWI-interacting RNAs piR-13643 and piR-21238 are promising diagnostic biomarkers of papillary thyroid carcinoma

Emerging studies demonstrate that PIWI-interacting RNAs (piRNAs) participate in the development of cancers. 75 pairs of papillary thyroid carcinoma (PTC) samples and 31 benign thyroid nodule samples were included in this three-phase biomarker identifying study. First, piRNA expression profiles of five pairs of PTC samples were acquired piRNA sequencing. The expression of all upregulated piRNAs were further validated by RT-qPCR. Paired t and nonparametric test were used to evaluate the association between all upregulated piRNAs and clinic stage. The expression levels of key piRNAs were corrected by demographic data to construct a multivariate model to distinguish malignant nodules from benign. Additionally, the intersection between target genes of key piRNAs and differentially expressed genes in The Cancer Genome Atlas (TCGA) PTC samples were used to perform enrichment analysis. Only piR-13643 and piR-21238 were significantly upregulated in PTC and associated with clinic stage. Moreover, both piR-13643 (Area Under Curve (AUC): 0.821) and piR-21238 (AUC: 0.823) showed better performance in distinguishing malignant nodules from benign than currently used biomarkers HBME1 (AUC: 0.590). Based on our findings, piR-13643 and piR-21238 were observed to be significantly upregulated in human PTC. PIWI-interacting RNAs could serve as promising novel biomarkers for accurate detection of PTC.