Immune-related prognosis biomarkers associated with osteosarcoma microenvironment

CFHR1 involvement in bile duct carcinoma: Insights from a data mining study

The aim of this study is to investigate the role of CFHR1 in bile duct carcinoma (BDC) and its mechanism of action, and we hope that our analysis and research will contribute to a better understanding of cholangiocarcinoma (BDC) disease genesis, progression and the development of new therapeutic strategies. The prognostic receiver operating characteristic curve of CFHR1 was generated using survival ROC. The ROC curve for CFHR1 showed that there is a correlation between CFHR1 expression and clinicopathological parameters and has an impact on poor prognosis. STRING was used to predict the protein-protein interaction network of the identified genes, and the Microenvironment Cell Populations counter algorithm was used to analyze immune cell infiltration within the BDC. The combined analysis showed that CFHR1 was found to be upregulated in BDC tissues, along with a total of 20 related differentially expressed genes (DEGs) (8 downregulated and 12 upregulated genes). Also, the results showed that the expression of CFHR1 is correlated with immune cell infiltration in tumor and immune cell markers in BDC (P < 0.05). In addition, we have verified experimentally the biological function of CFHR1. These findings suggest that CFHR1 may be a prognostic marker and a potential therapeutic target for BDC. Information regarding the detailed roles of CFHR1 in BDC could be valuable for improving the diagnosis and treatment of this rare cancer.

Prognostic related gene index for predicting survival and immunotherapeutic effect of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common malignant liver tumor. It is an aggressive disease with high mortality rate. In this study, we investigated a new prognosis-related gene index (PRGI) that can predict the survival and efficacy of immunotherapy in patients with HCC. RNA-seq data and clinical data of HCC samples were obtained from the cancer genome atlas and ICGC databases. Prognosis-related genes were obtained using log-rank tests and univariate Cox proportional hazards regression. Univariate and multivariate analyses were performed on the overall survival rate of patients with prognosis-related genes and multiple clinicopathological factors, and a nomogram was constructed. A PRGI was then constructed based on least absolute shrinkage and selection operator or multivariate Cox Iterative Regression. The possible correlation between PRGI and immune cell infiltration or immunotherapy efficacy was discussed. Eight genes were identified to construct the PRGI. PRGI can predict the infiltration of immune cells into the tumor microenvironment of HCC and the response to immunotherapy. PRGI can accurately predict the survival rate of patients with HCC, reflect the immune microenvironment, and predict the efficacy of immunotherapy.

Construction and validation of a predictive nomogram for ferroptosis-related genes in osteosarcoma

BACKGROUND

Ferroptosis is a new type of cellular regulation of necrosis that has attracted great attention in recent years, which is different from the traditional mode of autophagy, apoptosis, and necrosis. Studies suggest that ferroptosis is key to the occurrence and development of tumors.

METHODS

Here, we investigated the prognostic significance of ferroptosis-related genes (FRGs) in osteosarcoma (OS) using RNA transcriptome data from 88 OS samples collected from the UCSC Xena platform. We defined the OS sample from the UCSC platform as the training cohort and the GEO dataset (GSE21257 and GSE16091) as the validation cohorts. We assessed 73 up-regulated and 63 down-regulated FRGs. We divided patients from the UCSC database into groups at high risk and low risk and built a prognostic risk model to assess prognosis using five FRGs: MT1G, G6PD, ARNTL, BNIP3, and SQLE.

RESULTS

High-risk OS patients presented a lower survival rate. These results were confirmed in the validation groups. In the training group, the areas under the ROC curves (AUC) were as follows: 0.880 for 1 year, 0.833 for 3 years, and 0.818 for 5 years. In the GSE21257 validation cohort, the AUC were as follows: 0.770 for 1 year, 0.641 for 3 years, and 0.632 for 5 years survival, and in the GSE16091 were 0.729 for 1 year, 0.663 for 3 years, and 0.735 for 5 years survival.

CONCLUSIONS

These findings suggest that FRGs are associated with the prognosis of osteosarcoma. Moreover, our prognostic risk model can predict overall survival in osteosarcoma. This provides new ideas for the clinical diagnosis and personalized treatment of osteosarcoma.

Mechanisms of inhibition of nucleus pulposus cells pyroptosis through SDF1/CXCR4-NFkB-NLRP3 axis in the treatment of intervertebral disc degeneration by Duhuo Jisheng Decoction

Intervertebral disc degeneration (IVDD) is one of the leading causes of lower back pain and the most common health problem in the world. Inflammasomes, which is mainly caused by NLRP3, mediated nucleus pulposus pyroptosis has been discovered to be strongly related to IVDD. In addition, Duhuo Jisheng Decoction (DHJSD) has anti-inflammatory and regulatory effects on NLRP3 inflammasome, but the molecular mechanism of whether DHJSD can regulate pyroptosis through NLRP3 to treat IVDD is unclear. In this study, we used a bioinformatics way to discover the molecular mechanism of DHJSD regulation of pyroptosis in IVDD, and validated our predictions through vitro and vivo experiments. Through bioinformatics, we found that NLRP3, GSDMD, IL-1βand other hub proteins of pyroptosis were highly expressed in IVDD SD rats, and network pharmacology discovered that DHJSD may control cellular senescence, apoptosis, and pyroptosis in order to treat IVDD. Additional findings demonstrated that DHJSD could successfully treat IVDD brought on by imaging and histomorphological analysis. Western blot showed that NLRP3, a key protein of pyroptosis, was elevated in rat degenerated nucleus pulposus tissue and lipopolysaccharide-treated Nucleus pulposus Cells (NPCs), and that DHJSD intervention was effective in reducing LPS-induced inflammatory responses and further suppressing the expression of pyroptosis related proteins to improve IVDD. The specific mechanism is that DHJSD inhibits NPCs pyroptosis via the SDF-1/CXCR4-NF-kB-NLRP3 axis. In conclusion, we revealed the intrinsic mechanism of DHJSD regulation of NPCs pyroptosis to improve IVDD and its intrinsic value for IVDD treatment.

Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients

Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma.

Identification of fatty acid-related subtypes, the establishment of a prognostic signature, and immune infiltration characteristics in lung adenocarcinoma

Abnormal fatty acid (FA) metabolism can change the inflammatory microenvironment and promote tumor progression and metastasis, however, the potential association between FA-related genes (FARGs) and lung adenocarcinoma (LUAD) is still unclear. In this study, we described the genetic and transcriptomic changes of FARGs in LUAD patients and identified two different FA subtypes, which were significantly correlated with overall survival and tumor microenvironment infiltrating cells in LUAD patients. In addition, the FA score was also constructed through the LASSO Cox to evaluate the FA dysfunction of each patient. Multivariate Cox analysis proved that the FA score was an independent predictor and created the FA score integrated nomogram, which offered a quantitative tool for clinical practice. The performance of the FA score has been substantiated in numerous datasets for its commendable accuracy in estimating overall survival in LUAD patients. The groups with high and low FA scores exhibited different mutation spectrums, copy number variations, enrichment pathways, and immune status. Noteworthy differences between the two groups in terms of immunophenoscore and Tumor Immune Dysfunction and Exclusion were observed, suggesting that the group with a low FA score was more responsive to immunotherapy, and similar results were also confirmed in the immunotherapy cohort. In addition, seven potential chemotherapeutic drugs related to FA score targeting were predicted. Ultimately, we ascertained that the attenuation of KRT6A expression impeded the proliferation, migration, and invasion of LUAD cell lines. In summary, this research offers novel biomarkers to facilitate prognostic forecasting and clinical supervision for individuals afflicted with LUAD.

Analysis of scRNA-seq and bulk RNA-seq demonstrates the effects of EVI2B or CD361 on CD8+ T cells in osteosarcoma

Osteosarcoma (OS) is a common primary malignant tumor of the bone in children and adolescents. The five-year survival rate is estimated to be ~70% based on the currently available treatment modalities. It is well known that tumor-infiltrating immune cells (TIICs) that are the most important components in the tumor microenvironment can exert a killing effect on tumor cells. Therefore, in the present study, 85 RNA-sequencing OS samples were categorized into high- and low-immune score groups with ESTIAMATE. Based on the immune score groups, 474 differentially expressed genes (DEGs) were acquired using the LIMMA package of R language. Subsequently, 86 DEGs were taken through univariate COX regression analysis, of which 14 were screened out by least absolute shrinkage and selection operator regression analysis. Furthermore, multivariate COX regression analysis was performed to obtain 4 DEGs. Finally, ecotropic virus integration site 2B (EVI2B) or CD361 gene was screened out via Kaplan-Meier analysis. In addition, CIBERSORT algorithm was used to evaluate the proportion of 22 kinds of TIICs in OS. Correlation analysis revealed that the high expression level of EVI2B can elevate the infiltrated proportion of CD8⁺ T cells. Moreover, analysis of single cell RNA-sequencing transcriptome datasets and immunohistochemical staining uncovered that EVI2B was mainly expressed on CD8⁺ T cells and that EVI2B could promote the expression of granzyme A and K of CD8⁺ T cells to exhibit a potent killing effect on tumor cells. Therefore, EVI2B was identified as a protective immune-related gene and contributed to good prognosis in OS patients.

Characterization of a lipid droplet and endoplasmic reticulum stress related gene risk signature to evaluate the clinical and biological value in hepatocellular carcinoma

INTRODUCTION

Lipid metabolism and endoplasmic reticulum (ER) stress play an important role in the progression and metastasis of hepatocellular carcinoma (HCC). We aimed to establish lipid droplet (LD)-associated and ER stress-related gene risk signature as prognostic indicators.

MATERIALS AND METHODS

Literature searches for LD-associated proteins was screened and validated in The Cancer Genome Atlas (TCGA) and International Cancer Genome Collaboratory (ICGC) databases. A total of 371 samples were enrolled from the TCGA RNA-seq dataset (training cohort) and 240 samples from IGGC RNA-seq dataset (validation cohort). A 10-gene risk signature was established by the last absolute shrinkage and selection operator (LASSO) regression analysis. The prognostic value of the risk signature was evaluated by Cox regression, Kaplan-Meier and ROC Curve analyses. Biological features associated with LD and ER stress-related factors were explored by functional analysis and in vitro experiment.

RESULTS

Based on the medical literatures, 124 lipid droplet-associated proteins were retrieved, and three genes failed to establish a valid prognostic model. ER stress was considered as an important component by functional analysis. A 10-gene risk signature compared the clinicopathology characteristics, immunosuppressive events and a nomogram in HCC patients.

CONCLUSION

LD-associated and ER stress-related gene risk signatures highlighted poor prognosis for clinicopathological features, positively correlate with macrophages and T cell immunoglobulin and mucin-3 (TIM-3) expression in the tumor microenvironment, and might act as independent prognostic factors.

Engineering Novel 3D Models to Recreate High-Grade Osteosarcoma and its Immune and Extracellular Matrix Microenvironment

Osteosarcoma (OS) is the most common primary bone cancer, where the overall 5-year surviving rate is below 20% in resistant forms. Accelerating cures for those poor outcome patients remains a challenge. Nevertheless, several studies of agents targeting abnormal cancerous pathways have yielded disappointing results when translated into clinic because of the lack of accurate OS preclinical modeling. So, any effort to design preclinical drug testing may consider all inter-, intra-, and extra-tumoral heterogeneities throughout models mimicking extracellular and immune microenvironment. Therefore, the bioengineering of patient-derived models reproducing the OS heterogeneity, the interaction with tumor-associated macrophages (TAMs), and the modulation of oxygen concentrations additionally to recreation of bone scaffold is proposed here. Eight 2D preclinical models mimicking several OS clinical situations and their TAMs in hypoxic conditions are developed first and, subsequently, the paired 3D models faithfully preserving histological and biological characteristics are generated. It is possible to shape reproducibly M2-like macrophages cultured with all OS patient-derived cell lines in both dimensions. The final 3D models pooling all heterogeneity features are providing accurate proliferation and migration data to understand the mechanisms involved in OS and immune cells/biomatrix interactions and sustained such that engineered 3D preclinical systems will improve personalized medicine.

Increased ANXA5 expression in stomach adenocarcinoma infers a poor prognosis and high level of immune infiltration

BACKGROUND: The prognostic role of annexin A5 (ANXA5) in stomach adenocarcinoma (STAD) has not been studied, and its relationship with immune infiltration is still unclear. OBJECTIVE: This investigation aimed at exploring the role of ANXA5 in STAD using an integrated bioinformatics analysis. METHODS: The expression of ANXA5 in STAD and the correlations between the effect of ANXA5 and survival of STAD patients were investigated using database. The clusterProfiler package in R software was used to perform enrichment analysis on the top 100 co-expressed genes of ANXA5 from the COXPRESdb online database. Correlations between ANXA5 and immune cell infiltrates were analyzed using the TIMER database. RESULTS: In STAD, ANXA5 expression was significantly upregulated and increased ANXA5 expression was significantly correlated with poor overall survival (P< 0.05). In multivariate analysis, upregulated ANXA5 expression was an independent predictive factors of poor prognosis (P< 0.05). The co-expressed genes were involved in extracellular matrix (ECM)-related processes. In STAD, ANXA5 expression was significantly correlated with various infiltrating immune cells (P< 0.05). CONCLUSIONS: Together with our findings, ANXA5 could serve as a potential biomarker to assess prognosis and immune infiltration level in STAD.

Selection of lncRNAs That Influence the Prognosis of Osteosarcoma Based on Copy Number Variation Data

Osteosarcoma is the most common primary malignancy in the musculoskeletal system. It is reported that copy number variation- (CNV-) derived lncRNAs contribute to the progression of osteosarcoma. However, whether CNV-derived lncRNAs affect the prognosis of osteosarcoma remains unclear. Here, we obtained osteosarcoma-related CNV data and gene expression profiles from The Cancer Genome Atlas (TCGA) database. CNV landscape analysis indicated that copy number amplification of lncRNAs was more frequent than deletion in osteosarcoma samples. Thirty-four CNV-lncRNAs with DNA-CNV frequencies greater than 30% and their corresponding 294 mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses revealed that these mRNAs were mainly enriched in olfaction, olfactory receptor activity, and olfactory transduction processes. Furthermore, we predicted that a total of 23 genes were cis-regulated by 16 CNV-lncRNAs, while 30 transcription factors (TFs) were trans-regulated by 5 CNV-lncRNAs. Through $t$ -tests, univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO), we constructed a CNV-related risk model including 3 lncRNAs (AC129492.1, PSMB1, and AC037459.4). The Kaplan-Meier (K-M) curves indicated that patients with high-risk scores showed poor prognoses. The areas under the receiver operating characteristic (ROC) curves (AUC) for predicting 3-, 5-, and 7-year overall survival (OS) were greater than 0.7, showing a satisfactory predictive efficiency. Gene set enrichment analysis (GSEA) revealed that the prognostic signature was intimately linked to skeletal system development, immune regulation, and inflammatory response. Collectively, our study developed a novel 3-CNV-lncRNA prognostic signature that would provide theoretical guidance for the clinical prognostic management of osteosarcoma.

Identification of a novel immune-related long noncoding RNA signature to predict the prognosis of bladder cancer

Tumour immune regulation has attracted widespread attention, and long noncoding RNAs (lncRNAs) play an important role in this process. Therefore, we evaluated patient prognosis by exploring the relationship between bladder cancer (BLCA) and immune-related lncRNAs (IRlncRNAs). Transcriptome data and immune-related genes were analysed for coexpression, and then, the IRlncRNAs were analysed to determine the differentially expressed IRlncRNAs (DEIRlncRNAs) between normal and tumour samples in The Cancer Genome Atlas. The screened lncRNAs were pairwise paired and combined with clinical data, and finally, a signature was constructed by Lasso regression and Cox regression in 13 pairs of DEIRlncRNAs. According to the Akaike information criterion (AIC) values of the 1-year receiver operating characteristic curve, BLCA patients were stratified into high- or low-risk groups. The high-risk group had a worse prognosis. A comprehensive analysis showed that differences in risk scores were associated with the immune status of BLCA-infiltrated patients. The identified signature was correlated with the expression of immune checkpoint inhibitor-related molecules and sensitivity to chemotherapeutic drugs. We also identified three BLCA clusters with different immune statuses and prognoses that are also associated with immunotherapy response and drug sensitivity. In conclusion, we constructed a powerful predictive signature with high accuracy and validated its prognostic value.

N6-Methyladenosine-Related LncRNAs Are Potential Remodeling Indicators in the Tumor Microenvironment and Prognostic Markers in Osteosarcoma

N6-Adenosine methylation, yielding N6-methyladenosine (m⁶A), is a reversible epigenetic modification found in messenger RNAs and long non-coding RNAs (lncRNAs), which affects the fate of modified RNA molecules and is essential for the development and differentiation of immune cells in the tumor microenvironment (TME). Osteosarcoma (OS) is the most common primary bone tumor in children and adolescents, and is characterized by high mortality. Currently, the possible role of m⁶A modifications in the prognosis of OS is unclear. In the present study, we investigated the correlation between m⁶A-related lncRNA expression and the clinical outcomes of OS patients via a comprehensive analysis. Clinical and workflow-type data were obtained from the Genotype-Tissue Expression Program and The Cancer Genome Atlas. We examined the relationship between m⁶A modifications and lncRNA expression, conducted Kyoto Encyclopedia of Genes analysis and also gene set enrichment analysis (GSEA), implemented survival analysis to investigate the association of clinical survival data with the expression of m⁶A-related lncRNAs, and utilized Lasso regression to model the prognosis of OS. Furthermore, we performed immune correlation analysis and TME differential analysis to investigate the infiltration levels of immune cells and their relationship with clinical prognosis. LncRNA expression and m⁶A levels were closely associated in co-expression analysis. The expression of m⁶A-related lncRNAs was quite low in tumor tissues; this appeared to be a predicting factor of OS in a prognostic model, independent of other clinical features. The NOD-like receptor signaling pathway was the most significantly enriched pathway in GSEA. In tumor tissues, SPAG4 was overexpressed while ZBTB32 and DEPTOR were downregulated. Tissues in cluster 2 were highly infiltrated by plasma cells. Cluster 2 presented higher ESTIMATE scores and stromal scores, showing a lower tumor cell purity in the TME. In conclusion, m⁶A-related lncRNA expression is strongly associated with the occurrence and development of OS, and can be used to as a prognostic factor of OS. Moreover, m⁶A-related lncRNAs and infiltrating immune cells in the TME could serve as new therapeutic targets and prognostic biomarkers for OS.

Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Objective: Hypoxic tumors contribute to local failure and distant metastases. Nevertheless, the molecular hallmarks of hypoxia remain ill-defined in osteosarcoma. Here, we developed a hypoxic gene signature in osteosarcoma prognoses. Methods: With the random survival forest algorithm, a prognostic hypoxia-related gene signature was constructed for osteosarcoma in the TARGET cohort. Overall survival (OS) analysis, receiver operating characteristic (ROC) curve, multivariate cox regression analysis, and subgroup analysis were utilized for assessing the predictive efficacy of this signature. Also, external validation was presented in the GSE21257 cohort. GSEA was applied for signaling pathways involved in the high- and low-risk samples. Correlation analyses between risk score and immune cells, stromal/immune score, immune checkpoints, and sensitivity of chemotherapy drugs were performed in osteosarcoma. Then, a nomogram was built by integrating risk score, age, and gender. Results: A five-hypoxic gene signature was developed for predicting survival outcomes of osteosarcoma patients. ROC curves confirmed that this signature possessed the well predictive performance on osteosarcoma prognosis. Furthermore, it could be independently predictive of prognosis. Metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism were activated in the high-risk samples while cell adhesion molecules cams and intestinal immune network for IgA production were enriched in the low-risk samples. The low-risk samples were characterized by elevated immune cell infiltrations, stromal/immune scores, TNFRSF4 expression, and sensitivity to cisplatin. The nomogram accurately predicted 1-, 3-, and 5-years survival duration. Conclusion: These findings might offer an insight into the optimization of prognosis risk stratification and individualized therapy for osteosarcoma patients.

Construction and Validation of a Potent Epigenetic Modification-Related Prognostic Signature for Osteosarcoma Patients

Background

Increasing evidence has shown that tumorigenesis correlates with aberrant epigenetic factors, such as DNA methylation, histone modification, RNA m6A modification, RNA binding proteins, and transcription factors. However, it is unclear that how epigenetic genes linked with alteration contribute to osteosarcoma's incidence and clinical prognosis. We developed an epigenetic modification-related prognostic model that may improve the diagnosis and prognosis of osteosarcoma.

Methods

We investigated the epigenetic modification-associated genes and their clinical significance in osteosarcoma in this research. Our gene transcriptome data were obtained from the TARGET database and the GEO database. Bioinformatics techniques were used to investigate their functionalities. The diagnostic and prognostic models were constructed using univariate and multivariate Cox regression. In addition, we developed a nomogram indicating the practicability of the prognostic model described above.

Results

A risk score model constructed based on four epigenetic modification-related genes (MYC, TERT, EIF4E3, and RBM34) can effectively predict the prognosis of patients with osteosarcoma. Based on the risk score and clinical features, we constructed a nomogram.

Conclusion

Epigenetic modification-related genes have been identified as important prognostic markers that may assist in osteosarcoma therapy therapeutic decision-making.

Low GNG12 Expression Predicts Adverse Outcomes: A Potential Therapeutic Target for Osteosarcoma

Background

G protein subunit gamma 12 (GNG12) is observed in some types of cancer, but its role in osteosarcoma is unknown. This study hypothesized that GNG12 may be a potential biomarker and therapeutic target. We aimed to identify an association between GNG12 and osteosarcoma based on the Gene Expression Omnibus and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases.

Methods

Osteosarcoma samples in GSE42352 and TARGET database were selected as the test cohorts. As the external validation cohort, 78 osteosarcoma specimens from The Second Affiliated Hospital of Nanchang University were collected. Patients with osteosarcoma were divided into high and low GNG12 mRNA-expression groups; differentially expressed genes were identified as GNG12-related genes. The biological function of GNG12 was annotated using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and immune infiltration analysis. Gene expression correlation analysis and competing endogenous RNA regulatory network construction were used to determine potential biological regulatory relationships of GNG12. Overall survival, Kaplan–Meier analysis, and log-rank tests were calculated to determine GNG12 reliability in predicting survival prognosis.

Results

GNG12 expression decreased in osteosarcoma samples. GNG12 was a highly effective biomarker for osteosarcoma [area under the receiver operating characteristic (ROC) curve (AUC) = 0.920], and the results of our Kaplan–Meier analysis indicated that overall survival and progression-free survival differed significantly between low and high GNG-expression group (p < 0.05). Functional analyses indicated that GNG12 may promote osteosarcoma through regulating the endoplasmic reticulum. Expression correlation analysis and competing endogenous RNA network construction showed that HOTTIP/miR-27a-3p may regulate GNG12 expression. Furthermore, the subunit suppresses adaptive immunity via inhibiting M1 and M2 macrophage infiltration. GNG12 was inhibited in metastatic osteosarcoma compared with non-metastatic osteosarcoma, and its expression predicted survival of patients (1, 3, and 5-year AUCs were 0.961, 0.826, and 0.808, respectively).

Conclusion

This study identified GNG12 as a potential biomarker for osteosarcoma prognosis, highlighting its potential as an immunotherapy target.

Identification of MAP3K15 as a potential prognostic biomarker and correlation with immune infiltrates in osteosarcoma

Background

Osteosarcoma (OS) is a type of primary malignant tumor, and increasing evidence shows the clinical benefits of immunotherapy in treating OS. However, the lack of comprehensive studies on the complex OS immune microenvironment hinders the application of immunotherapy. Thus, this study aimed to systematically explore the immune characteristics of OS and identify novel biomarkers for OS treatment.

Methods

We systematically studied the immune score and proportions of infiltrating immune cells in OS in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases using the ESTIMATE and CIBERSORT algorithms. Differential expression and functional analyses were used to identify dysregulated genes and explore their functions. Survival and Cox regression analyses were applied to establish an immune-related prognostic signature. Additionally, qPCR and immunohistochemistry were performed to validate the results.

Results

A total of 103 differentially expressed immune genes (DEIGs) were found in the TARGET-OS and GSE39058 databases, and these DEIGs were mainly enriched in leukocyte proliferation, leukocyte differentiation, osteoclast differentiation, natural killer (NK) cell-mediated cytotoxicity, and the adaptive immune system. A predictive signature was constructed based on the survival analysis, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.65. Moreover, we found that mitogen-activated protein kinase kinase kinase 15 (MAP3K15) can predict the prognosis of patients with OS and is closely related to CD4⁺ T cells and macrophages. The OS patients with high MAP3K15 expression had a significantly poorer prognosis.

Conclusions

Our study found that MAP3K15, whose expression level is closely related to immune activity in tumors, is a critical immune-related biomarker, and our findings may provide a basis for OS immunotherapy.

LPCAT1 overexpression promotes the progression of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains one of the most common malignant neoplasms. Lysophosphatidylcholine acyltransferase 1 (LPCAT1) plays a key role in the lipid remodelling and is correlated with various neoplasms. Nonetheless, the biological functions and molecular mechanisms of LPCAT1 underlying HCC remain obscure.

METHODS

In the present study, we investigated the role of LPCAT1 in the progression of HCC. In-house RT-qPCR, tissue microarrays, and immunohistochemistry were performed to detect the expression levels and the clinical value of LPCAT1 in HCC. External datasets were downloaded to confirm the results. Proliferation, migration, invasiveness, cell cycle, and apoptosis assays were conducted to reveal the biological effects LPCAT1 has on SMMC-7721 and Huh7 cells. HCC differentially expressed genes and LPCAT1 co-expressed genes were identified to explore the molecular mechanisms underlying HCC progression.

RESULTS

LPCAT1 showed upregulated expression in 3715 HCC specimens as opposed to 3105 non-tumour specimens. Additionally, LPCAT1 might be an independent prognostic factor for HCC. LPCAT1-knockout hampered cellular proliferation, migration, and metastasis in SMMC-7721 and Huh7 cells. More importantly, the cell cycle and chemical carcinogenesis were the two most enriched signalling pathways.

CONCLUSIONS

The present study demonstrated that increased LPCAT1 correlated with poor prognosis in HCC patients and fuelled HCC progression by promoting cellular growth, migration, and metastasis.

High Matrix Metalloproteinase 28 Expression is Associated with Poor Prognosis in Pancreatic Adenocarcinoma

Purpose

Pancreatic adenocarcinoma (PAAD) is a devastating disease with high mortality and morbidity. Matrix metalloproteinase 28 (MMP28) has been associated with carcinogenesis of many human cancers. However, little is known about the potential prognostic value and underlying regulatory mechanisms of MMP28 in PAAD.

Methods

The relationship between MMP28 expression level and various clinicopathological parameters was analyzed in TCGA-PAAD cohorts. MMP28-correlated genes in the TCGA-PAAD cohort were identified and enrichment analysis according to the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes was conducted using LinkedOmics. Protein–protein interaction and transcription factors-miRNA co-regulatory networks were constructed with the use of NetworkAnalyst. Then, the distribution of immune cells related to MMP28 expression in blood was analyzed using the Human Protein Atlas, and the tumor microenvironment of PAAD was analyzed by the TIMER 2.0 database. To investigate the biological function of MMP28 in PAAD, siRNA was constructed to knock down the MMP28 gene in vitro.

Results

High MMP28 expression is associated with poor overall survival and disease-free survival in PAAD patients. The expression of MMP28 in PAAD is most significantly correlated with KRT19, IL1RN, and ANXA2 genes. Network analysis revealed that MIR-181 family, TAFs, and CDC6 are potential regulators of MMP28. Furthermore, naive CD4⁺ T cell, naive CD8⁺ T cell, and mucosal-associated invariant T cell enrichment in blood were correlated with MMP28 expression. Furthermore, high MMP28 expression was correlated with a decrease in B cell, naive CD4⁺ T cell, naive CD8⁺ T cell, and endothelial cell presence in the tumor microenvironment in PAAD. Finally, genetic knockdown of MMP28 could restrain the proliferation, migration, and invasion of PAAD cells.

Conclusion

Our findings indicate that high MMP28 expression in PAAD is associated with cancer progression, invasion, and metastasis. Hence, MMP28 might serve as an independent prognostic biomarker and a prospective therapeutic target for PAAD.

The Immune Landscape of Osteosarcoma: Implications for Prognosis and Treatment Response

Osteosarcoma (OS) is a high-grade malignant stromal tumor composed of mesenchymal cells producing osteoid and immature bone, with a peak of incidence in the second decade of life. Hence, although relatively rare, the social impact of this neoplasm is particularly relevant. Differently from carcinomas, molecular genetics and the role of the tumor microenvironment in the development and progression of OS are mainly unknown. Indeed, while the tumor microenvironment has been widely studied in other solid tumor types and its contribution to tumor progression has been definitely established, tumor-stroma interaction in OS has been quite neglected for years. Only recently have new insights been gained, also thanks to the availability of new technologies and bioinformatics tools. A better understanding of the cross-talk between the bone microenvironment, including immune and stromal cells, and OS will be key not only for a deeper knowledge of osteosarcoma pathophysiology, but also for the development of novel therapeutic strategies. In this review, we summarize the current knowledge about the tumor microenvironment in OS, mainly focusing on immune cells, discussing their role and implication for disease prognosis and treatment response.

Development of a Bioluminescent Human Osteosarcoma Model in Humanized NSG Mice: A Pilot Study

BACKGROUND/AIM

Osteosarcoma is the most common type of bone cancer, but current therapeutic interventions remain largely insufficient. The development of new treatment strategies is needed, and moreover, optimal rodent models are necessary for testing the efficacy of new treatment modalities of osteosarcoma. Humanized mice carry human hematopoietic and immune systems, and are considered an ideal tool to study human diseases including cancer immunology. Herein, we performed a preliminary study toward developing an in vivo bioluminescent osteosarcoma model using humanized immunodeficient (NSG) mice.

MATERIALS AND METHODS

To establish the xenograft and orthotopic mouse model, NSG mice engrafted with human CD34⁺ hematopoietic stem cells were injected with luciferase-expressing KHOS/NP cells at two different time points. Bioluminescence images were obtained to monitor in vivo tumor growth and metastasis. Influence of the degree of human cell engraftment on tumor growth and metastatic behavior was analyzed and compared between the two groups.

RESULTS

KHOS/NP-luc cells injected in humanized NSG mice formed macroscopic tumors. The percentage of human CD45+ cells in these models was similar, but the percentage of human CD45+CD3+ and their subset was higher in the late-injection group compared to that of the early-injection group. The rate of KHOS/NP tumor growth was higher in the early-injection group than in the late-injection group. In the present study, human hematopoietic cell engraftment was not influenced by KHOS/NP cell injection, but KHOS/NP osteosarcoma showed more aggressive behavior in the early-injection group than that in the late-injection group, forming larger tumor volumes and earlier metastases.

CONCLUSION

The results indicated that tumor growth and progression in humanized NSG mice may have been influenced by higher levels of human cell engraftment, especially T cells. Although there exist some limitations to our study, our preliminary results can provide the basis for the development of a humanized osteosarcoma mouse model.

Development of a Machine Learning-Based Autophagy-Related lncRNA Signature to Improve Prognosis Prediction in Osteosarcoma Patients

Background

Osteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients.

Methods

We obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration.

Results

We initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells.

Conclusion

The autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma

BACKGROUND

Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma.

METHODS

We analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort.

RESULTS

The analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis.

CONCLUSIONS

The novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma.

Immune-Related Gene SERPINE1 Is a Novel Biomarker for Diffuse Lower-Grade Gliomas via Large-Scale Analysis

Background

Glioma is one of the highly fatal primary tumors in the central nervous system. As a major component of tumor microenvironment (TME), immune cell has been proved to play a critical role in the progression and prognosis of the diffuse lower-grade gliomas (LGGs). This study aims to screen the key immune-related factors of LGGs by investigating the TCGA database.

Methods

The RNA-sequencing data of 508 LGG patients were downloaded in the TCGA database. ESTIMATE algorithm was utilized to calculate the stromal, immune, and ESTIMATE scores, based on which, the differentially expressed genes (DEGs) were analyzed by using “limma” package. Cox regression analysis and the cytoHubba plugin of Cytoscape software were subsequently applied to screen the survival-related genes and hub genes, the intersection of which led to the identification of SERPINE1 that played key roles in the LGGs. The expression patterns, clinical features, and regulatory mechanisms of SERPINE1 in the LGGs were further analyzed by data mining of the TCGA database. What’s more, the above analyses of SERPINE1 were further validated in the LGG cohort from the CGGA database.

Result

We found that stromal and immune cell infiltrations were strongly related to the prognosis and malignancy of the LGGs. A total of 54 survival-related genes and 46 hub genes were screened out in the DEGs, within which SERPINE1 was identified to be significantly overexpressed in the LGG samples compared with the normal tissues. Moreover, the upregulation of SERPINE1 was more pronounced in the gliomas of WHO grade III and IDH wild type, and its expression was correlated with poor prognosis in the LGG patients. The independent prognostic value of SERPINE1 in the LGG patients was also confirmed by Cox regression analysis. In terms of the functions of SERPINE1, the results of enrichment analysis indicated that SERPINE1 was mainly enriched in the immune‐related biological processes and signaling pathways. Furthermore, it was closely associated with infiltrations of immune cells in the LGG microenvironment and acted synergistically with PD1, PD-L1, PD-L2.

Conclusion

These findings proved that SERPINE1 could serve as a prognostic biomarker and potential immunotherapy target of LGGs.

Identification and immunoprofiling of key prognostic genes in the tumor microenvironment of hepatocellular carcinoma

Tumor microenvironment (TME) is involved in the occurrence and development of hepatocellular carcinoma (HCC), and immune cells in the TME have been implicated in its progression and treatment. However, the association of genes involved in the TME with HCC prognosis remains unclear. Thus, in this study, we obtained transcriptomic and clinicopathological data of patients with HCC from The Cancer Genome Atlas to identify key genes in TME associated with HCC prognosis. Stromal and immune cell scores were calculated using the ESTIMATE method, and differentially expressed genes (DEGs) were determined. We identified 830 DEGs, which were further subjected to survival analyses and functional enrichment analysis. Next, we identified prognostic TME-associated DEGs, established a protein-protein interaction (PPI) network, and performed Cox analysis.Consequently, four key prognostic genes (CXCL5, CXCL8, IL18RAP, and TREM2) associated with TME, were identified, in which CXCL5 and IL18RAP may be potential independent prognostic factors. Age, clinical stage, N stage, and risk score were also determined as significant prognostic variables. CIBERSORT was used to predict the constitution and relative content of the immune cells, wherein M0 macrophages were the most closely related to the key genes. In conclusion, CXCL5, CXCL8, IL18RAP, and TREM2 were associated with HCC prognosis and were important for immune cell invasion into the TME. Additionally, IL18RAP expression may contribute toward favorable prognosis in patients with HCC. Consequently, these genes may serve as potential biomarkers and immunotherapeutic targets for HCC.

Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients

Glycolysis ensures energy supply to cancer cells, thereby facilitating tumor progression. Here, we identified glycolysis-related genes that could predict the prognosis of patients with osteosarcoma. We examined 198 glycolysis-related genes that showed differential expression in metastatic and non-metastatic osteosarcoma samples in the TARGET database, and identified three genes (P4HA1, ABCB6, and STC2) for the establishment of a risk signature. Based on the signature, patients in the high-risk group had poor outcomes. An independent Gene Expression Omnibus database GSE21257 was selected as the validation cohort. Receiver operating characteristic curve analysis was performed and the accuracy of predicting the 1- and 3-year survival rates was shown by the areas under the curve. The results were 0.884 and 0.790 in the TARGET database, and 0.740 and 0.759 in the GSE21257, respectively. Furthermore, we applied ESTIMATE algorithm and performed single sample gene set enrichment analysis to compare tumor immunity between high- and low-risk groups. We found that the low-risk group had higher immune scores and immune infiltration levels than the high-risk group. Finally, we chose P4HA1 as a representative gene to verify the function of risk genes in vitro and in vivo and found that P4HA1 could promote the metastasis of osteosarcoma cells. Our study established a novel glycolysis-related risk signature that could predict the prognosis of patients with osteosarcoma.

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Objectives

Osteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.

Methods

Clinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.

Results

Two molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.

Conclusion

Expression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

Identification of Prognostic RBPs in Osteosarcoma

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman's correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration

Background

Increasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients’ prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.

Methods

The differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan–Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.

Results

Two genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.

Conclusion

The current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.

Effect of Carbon Ion Radiation Induces Bystander Effect on Metastasis of A549 Cells and Metabonomic Correlation Analysis

Objective

To analyze the effect of carbon ion (¹²C⁶⁺) radiation may induce bystander effect on A549 cell metastasis and metabonomics.

Methods

A549 cell was irradiated with carbon ion to establish the clone survival model and the transwell matrix assay was applied to measure the effect of carbon ion on cell viability, migration, and invasion, respectively. Normal human embryonic lung fibroblasts (WI-38) were irradiated with carbon ions of 0 and 2 Gy and then transferred to A549 cell co-culture medium for 24 h. The migration and invasion of A549 cells were detected by the Transwell chamber. The analysis of metabonomic information in transfer medium by liquid phase mass spectrometry (LC-MS), The differential molecules were obtained by principal pomponent analysis (PCA) and the target proteins of significant differences (p = 1.7 × 10⁻³) obtained by combining with the STICH database. KEGG pathway was used to analyze the enrichment of the target protein pathway.

Results

Compared with 0 Gy, the colony formation, migration, and invasion of A549 cells were significantly inhibited by carbon ion 2 and 4 Gy irradiation, while the inhibitory effect was not significant after 1 Gy irradiation. Compared with 0 Gy, the culture medium 24 h after carbon ion 2 Gy irradiation significantly inhibited the metastasis of tumor cells (p = 0.03). LC-MS analysis showed that 23 differential metabolites were obtained in the cell culture medium 24 h after carbon ion 0 and 2 Gy irradiation (9 up-regulated and 14 down-regulated). Among them, two were up-regulated and two down-regulated (p = 2.9 × 10⁻³). 41 target proteins were corresponding to these four differential molecules. Through the analysis of the KEGG signal pathway, it was found that these target molecules were mainly enriched in purine metabolism, tyrosine metabolism, cysteine and methionine metabolism, peroxisome, and carbon metabolism. Neuroactive ligand-receptor interaction, calcium signaling pathway, arachidonic acid metabolism, and Fc epsilon RI signaling pathway.

Conclusion

The bystander effect induced by 2 Gy carbon ion radiation inhibits the metastasis of tumor cells, which indicates that carbon ions may change the metabolites of irradiated cells, so that it may indirectly affect the metabolism of tumor cell growth microenvironment, thus inhibiting the metastasis of malignant tumor cells.

Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity

Background

Osteosarcoma (OS) patients have a poor response to immunotherapy due to the sheer complexity of the immune system and the nuances of the tumor-immune microenvironment. Methodology. To gain insights into the immune heterogeneity of OS, we identified robust clusters of patients based on the immune gene expression profiles of OS patients in the TARGET database and assessed their reproducibility in an independent cohort collected from the GEO database. The association of comprehensive molecular characterization with reproducible immune subtypes was accessed with ANOVA. Furthermore, we visualized the distribution of individual patients in a tree structure by the graph structure learning-based dimensionality reduction algorithm.

Results

We found that 87 OS samples can be divided into 5 immune subtypes, and each of them was associated with distinct clinical outcomes. The immune subtypes also demonstrated widely different patterns in tumor genetic aberrations, tumor-infiltrating, immune cell composition, and cytokine profiles. The immune landscape of OS uncovered the significant intracluster heterogeneity within each subtype and depicted a continuous immune spectrum across patients.

Conclusion

The established five immune subtypes in our study suggested immune heterogeneity in OS patients and may provide optimal individual immunotherapy for patients exhibiting OS.

Immune classification of osteosarcoma

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

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.

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Aim

Immune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).

Methods

Immune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.

Results

We selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.

Conclusion

The current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.

Development of a novel immune-related genes prognostic signature for osteosarcoma

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

BACKGROUND

Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma.

METHODS

CIBERSORTX algorithm was used to calculate the proportion of 22 TIIC types in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIIC types. Forward stepwise approach was used to screen a minimal set of immune cell types. Multivariate Cox PHR analysis was performed to construct an immune risk score model.

RESULTS

Osteosarcoma samples with CIBERSORTX output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p = 0.047) and Monocytes (p = 0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cell types, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC = 0.724). Besides, a nomogram model base on eight immune cell types was constructed to predict the survival rate of patients with osteosarcoma.

CONCLUSIONS

TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.

A four-methylated LncRNA signature predicts survival of osteosarcoma patients based on machine learning

Risk stratification using prognostic markers facilitates clinical decision-making in treatment of osteosarcoma (OS). In this study, we performed a comprehensive analysis of DNA methylation and transcriptome data from OS patients to establish an optimal methylated lncRNA signature for determining OS patient prognosis. The original OS datasets were downloaded from the the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Univariate, Lasso, and machine learning algorithm-iterative Lasso Cox regression analyses were used to establish a methylated lncRNA signature that significantly correlated with OS patient survival. The validity of this signature was verified by the Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves. We established a four-methylated lncRNA signature that can predict OS patient survival (verified in independent cohort [GSE39055]). Kaplan-Meier analysis showed that the signature can distinguish between the survival of high- and low-risk patients. ROC analysis corroborated this finding and revealed that the signature had higher prediction accuracy than known biomarkers. Kaplan-Meier analysis of the clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. The four-methylated lncRNA signature is an independent prognostic biomarker of OS.

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

Focus on Hypoxia-Related Pathways in Pediatric Osteosarcomas and Their Druggability

Osteosarcoma is the most frequent primary bone tumor diagnosed during adolescence and young adulthood. It is associated with the worst outcomes in the case of poor response to chemotherapy and in metastatic disease. While no molecular biomarkers are clearly and currently associated with those worse situations, the study of pathways involved in the high level of tumor necrosis and in the immune/metabolic intra-tumor environment seems to be a way to understand these resistant and progressive osteosarcomas. In this review, we provide an updated overview of the role of hypoxia in osteosarcoma oncogenesis, progression and during treatment. We describe the role of normoxic/hypoxic environment in normal tissues, bones and osteosarcomas to understand their role and to estimate their druggability. We focus particularly on the role of intra-tumor hypoxia in osteosarcoma cell resistance to treatments and its impact in its endogenous immune component. Together, these previously published observations conduct us to present potential perspectives on the use of therapies targeting hypoxia pathways. These therapies could afford new treatment approaches in this bone cancer. Nevertheless, to study the osteosarcoma cell druggability, we now need specific in vitro models closely mimicking the tumor, its intra-tumor hypoxia and the immune microenvironment to more accurately predict treatment efficacy and be complementary to mouse models.

Gastric cancer risk-scoring system based on analysis of a competing endogenous RNA network

Background

Long noncoding RNAs (lncRNAs) can play vital roles in tumor initiation, progression, invasion, and metastasis. However, the functional role of the lncRNA-based competing endogenous RNA (ceRNA) networks in gastric cancer (GC) is still unclear. We aimed to identify novel lncRNAs and their association with GC prognosis.

Methods

The lncRNA, miRNA, and mRNA expression profiles of GC patients data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using the edge-R package. Then, the relationship among lncRNAs-miRNAs-mRNAs was integrated into a constructed ceRNA network with Cytoscape software. Using Cox regression analysis, a risk score system based on DEGs associated with patient prognosis in GC was established. Finally, a nomogram was founded to predict the prognosis of GC patients.

Results

A total of 971 differentially expressed lncRNAs (DElncRNAs), 144 differentially expressed miRNAs (DEmiRNAs) and 2,789 differentially expressed mRNAs (DEmRNAs) were identified and found to be associated with GC risk. Using the bioinformatics method, a ceRNA network involving 62 DElncRNAs, 21 DEmiRNAs and 59 DEmRNAs was constructed. Based on the results of the Cox regression analysis, a risk-scoring system involving 3 lncRNAs (i.e., ADAMTS9-AS1, C15orf54, and AL391152.1) was set up for the survival analysis of GC patients. The area under the receiver operating characteristic (ROC) curve for the risk-scoring system was 0.674, with a C-index of 0.64 [95% confidence interval (CI): 0.59–0.69, P=2.806485e−08]. Univariate and multivariate Cox regression analyses demonstrated that the risk-scoring system was an independent prognostic factor for GC. The risk-scoring system is positively associated with advanced tumor grade. The expression of these 3 lncRNAs were validated in GEPIA database. A nomogram based on these 3 lncRNAs was created to predict the prognosis of GC patients.

Conclusions

Our study established a novel lncRNA-expression-based ceRNA network and an ADAMTS9-AS1-C15orf54-AL391152.1-based risk-scoring system, which can be used to predict the prognosis of GC patients.

Profiles of tumor-infiltrating immune cells and prognostic genes associated with the microenvironment of bladder cancer

The immune microenvironment in bladder cancer (BC) and its significance still remain poorly understood. The present work aims to investigate tumor-infiltrating immune cells (TIICs) and prognostic genes associated with the tumor microenvironment (TME) of BC. The immune and stromal scores of BC samples from The Cancer Genome Atlas database were downloaded from the ESTIMATE website. Based on these scores, BC samples were assigned to the high and low score groups and 429 intersecting differentially expressed genes were identified. Functional enrichment analysis further revealed that these genes dramatically participated in the immune-related biological processes and signaling pathways. Two TME-related genes, angiotensin II receptor type 2 (AGTR2) and sclerostin domain containing 1 (SOSTDC1), were identified to establish an immune-related risk model using Cox regression analyses. Intriguingly, patients with high-risk scores had poor outcomes (p < 0.001). The areas under the curve for the risk model in predicting 3- and 5-year survival rates were 0.692 and 0.707, respectively. Kaplan-Meier survival analysis showed that the expression of AGTR2 and SOSTDC1 significantly correlated with the overall survival of BC patients. Additionally, 22 TIICs in the BC microenvironment were analyzed with the CIBERSORT algorithm. This study indicated that the effective components of TME affected the clinical outcomes of BC patients and might provide a basis for the development of new immunotherapies for BC patients.