Identification and characterization of aging/senescence-induced genes in osteosarcoma and predicting clinical prognosis

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

mir-744-5p inhibits cell growth and angiogenesis in osteosarcoma by targeting NFIX

BACKGROUND

Osteosarcoma (OS) is a malignant bone tumor that commonly occurs in children and adolescents under the age of 20. Dysregulation of microRNAs (miRNAs) is an important factor in the occurrence and progression of OS. MicroRNA miR-744-5p is aberrantly expressed in various tumors. However, its roles and molecular targets in OS remain unclear.

METHODS

Differentially expressed miRNAs in OS were analyzed using the Gene Expression Omnibus dataset GSE65071, and the potential hub miRNA was identified through weighted gene co-expression network analysis. Quantitative real-time PCR (qRT-PCR) was used to detect the expression of miR-744-5p in OS cell lines. In vitro experiments, including CCK-8 assays, colony formation assays, flow cytometry apoptosis assays, and tube formation assays, were performed to explore the effects of miR-744-5p on OS cell biological behaviors. The downstream target genes of miR-744-5p were predicted through bioinformatics, and the binding sites were validated by a dual-luciferase reporter assay.

RESULTS

The lowly expressed miRNA, miR-744-5p, was identified as a hub miRNA involved in OS progression through bioinformatic analysis. Nuclear factor I X (NFIX) was confirmed as a direct target for miR-744-5p in OS. In vitro studies revealed that overexpression of miR-744-5p could restrain the growth of OS cells, whereas miR-744-5p inhibition showed the opposite effect. It was also observed that treatment with the conditioned medium from miR-744-5p-overexpressed OS cells led to poorer proliferation and angiogenesis in human umbilical vein endothelial cells (HUVECs). Furthermore, NFIX overexpression restored the suppression effects of miR-744-5p overexpression on OS cell growth and HUVECs angiogenesis.

CONCLUSION

Our results indicated that miR-744-5p is a potential tumor-suppressive miRNA in OS progression by targeting NFIX to restrain the growth of OS cells and angiogenesis in HUVECs.

Predicting prognosis and drug sensitivity in bladder cancer: an insight into Pan-programmed cell death patterns regulated by M6A modifications

The team aimed to explore the possible functional significance of M6A regulation in Pan-programmed cell death (PCD) among patients with bladder cancer (BLCA). In BLCA patients, the analysis was conducted on the13 patterns of programmed cell death (PCD) and the regulation of M6A. Transcriptome, genomics, and clinical data were collected from TCGA-BLCA, GEO32548, and IMvigor210. Consensus clustering analysis, functional enrichment analysis, and other prognostic tools were used to validate the Pan-PCD. Finally, in vitro experiments and transcription sequencing were performed to understand the potential influence of the PI3K pathway on Pan-PCD in BLCA patients. Diverse PCD patterns were simultaneously activated, and M6A regulators exhibited significant variability in bladder malignant tissues. The machine learning algorithm established an 8-gene M6A-related Pan-PCD signature. This signature was validated in three independent datasets, and BLCA patients with higher risk scores had worse prognosis. An unsupervised clustering approach identified activated and suppressed Pan-PCD subgroups of BLCA patients, with distinct responses to immunotherapy and drug sensitivity. In addition, the PI3K pathway was identified as a key mechanism for various forms of programmed cell death, encompassing apoptosis, pyroptosis, autophagy, and cell death dependent on lysosomes. This research revealed that the Pan-PCD model was a more promising approach for BLCA patients under M6A regulation. A new signature from M6A-related Pan-PCD was proposed, with prognostic value for survival or drug sensitivity. The PI3K pathway was a key mechanism for multiple PCDs in BLCA patients.

Identification of a novel cellular senescence-related lncRNA signature for prognosis and immune response in osteosarcoma

Background

Cellular senescence, a novel hallmark of cancer, is associated with patient outcomes and tumor immunotherapy. However, at present, there is no systematic study on the use of cellular senescence-related long non-coding RNAs (CSR-lncRNAs) to predict survival in patients with osteosarcoma. In this study, we aimed to identify a CSR-lncRNAs signature and to evaluate its potential use as a survival prognostic marker and predictive tool for immune response of osteosarcoma.

Methods

We downloaded a cohort of patients with osteosarcoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We performed differential expression and co-expression analyses to identify CSR-lncRNAs. We performed univariate and multivariate Cox regression analyses along with the random forest algorithm to identify lncRNAs significantly correlated with senescence. Subsequently, we assessed the predictive models using survival curves, receiver operating characteristic curves, nomograms, C-index, and decision curve analysis. Based on this model, patients with osteosarcoma were divided into two groups according to their risk scores. Then, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we compared their clinical characteristics to uncover functional differences. We further conducted immune infiltration analyses using estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), cell-type identification by estimating relative subsets of rna transcripts (CIBERSORT), and single-sample gene set enrichment analysis for the two groups. We also evaluated the expression of the target genes of immune checkpoint inhibitors (ICIs).

Results

We identified six lncRNAs that were significantly correlated with senescence and accordingly established a novel cellular senescence-related lncRNA prognostic signature incorporating these lncRNAs. The nomogram indicated that the risk model was an independent prognostic factor that could predict the survival of patients with osteosarcoma. This model demonstrated high accuracy upon validation. Further analysis revealed that patients with osteosarcoma in the low-risk group exhibited better clinical outcomes and enhanced immune infiltration.

Conclusions

The six-CSR-lncRNA prognostic signature effectively predicted survival outcomes and patients in the low-risk group might have improved immune infiltration.

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

Coexpression of TP53, BIM, and PTEN Enhances the Therapeutic Efficacy of Non-Small-Cell Lung Cancer

For non-small-cell lung cancer (NSCLC), the ubiquitous occurrence of concurrent multiple genomic alterations poses challenges to single-gene therapy. To increase therapeutic efficacy, we used the branch-PCR method to develop a multigene nanovector, NP-TP53-BIM-PTEN, that carried three therapeutic gene expression cassettes for coexpression. NP-TP53-BIM-PTEN exhibited a uniform size of 104.8 ± 24.2 nm and high serum stability. In cell transfection tests, NP-TP53-BIM-PTEN could coexpress TP53, BIM, and PTEN in NCI-H1299 cells and induce cell apoptosis with a ratio of up to 94.9%. Furthermore, NP-TP53-BIM-PTEN also inhibited cell proliferation with a ratio of up to 42%. In a mouse model bearing an NCI-H1299 xenograft tumor, NP-TP53-BIM-PTEN exhibited a stronger inhibitory effect on the NCI-H1299 xenograft tumor than the other test vectors without any detectable side effects. These results exhibited the potential of NP-TP53-BIM-PTEN as an effective and safe multigene nanovector to enhance NSCLC therapy efficacy, which will provide a framework for genome therapy with multigene combinations.

CEP55, serving as a diagnostic marker gene for osteosarcoma, triggers the JAK2-STAT3-MMPs axis

Background

Osteosarcoma (OS) stands as the prevailing form of primary bone cancer in clinical practice. Lack of effective treatment options and an overall poor prognosis are caused by the disease's exceptionally rare occurrence and unclear rationale.

Objective

This study's goal is to determine diagnostic marker genes involved in the progression of OS and investigate related pathways and mechanisms with the purpose of offering effective methods for OS diagnostics and therapy.

Methods

The Gene Expression Omnibus database provided the gene microarray data. Core genes were identified through differential expression analysis and WGCNA. Three techniques for machine learning, random forest, least absolute shrinkage and selection operator regression, and support vector machine recursive feature elimination, were used to further screen the core genes and obtain diagnostic marker genes for OS. The specificity and sensitivity of the diagnostic marker genes for OS diagnosis were evaluated using receiver operating characteristic curves. Western blotting analysis was used for preliminary validation of the diagnostic marker genes and their related pathways.

Results

Two diagnostic marker genes were identified through screening, including CEP55 and VWF. Receiver operating characteristic curves have been utilized to assess the diagnostic and therapeutic effects of CEP55 and VWF on OS. Western blotting analysis preliminarily validated the overexpression of CEP55 in OS and its capacity to control MMP2 and MMP9 levels by activating the JAK2/STAT3 signaling pathway.

Conclusion

At the first time, this research shows that CEP55 and VWF are more powerful diagnostic and predictive indicators for OS. CEP55 holds the capacity to activate the JAK2/STAT3 signaling pathway and modulate MMP2 and MMP9 levels, thereby positioning it as a promising target in OS treatment.

The role of senescence genes in the treatment, prognosis, and tumor microenvironment of gastric cancer

AIM

Gastric cancer (GC) has a high incidence and poor prognosis. Senescence genes are suggested to participate in immune cell infiltration, thus affecting the immunotherapy of GC. In this research, we established a senescence-related GC model to explore and verify the role of senescence genes in the prognosis, treatment, and tumor microenvironment (TME) of GC.

METHODS

The TCGA GC (TCGA-STAD) dataset was used to screen key senescence genes from differentially expressed genes (DEGs). A prognostic risk model was trained utilizing the TCGA-STAD dataset and validated using an external GEO dataset. The CIBERSORT algorithm was run to explore the relationship between senescence genes and TME. The chemotherapy drug sensitivities in GC patients were calculated utilizing R package pRRophetic.

RESULTS

A total of 37 senescence-related DEGs were obtained. Five key senescence-related genes were further screened to establish a senescence-related risk model based on Cox regression. The survival status of GC patients in the high-risk group was found to be worse than that in the low-risk group. According to the results of gene set enrichment analysis, the senescence-related risk was mainly associated with cytokine activity, immune mechanism, and related pathways. By analyzing the sensitivity of common chemotherapy drugs in GC patients, it was revealed that the sensitivities of high-risk patients to Dasatinib, Lapatinib, and Pazopanib were lower than those of low-risk patients. The CIBERSORT algorithm was executed to analyze the TME in the high-risk group, revealing elevated levels of CD8 T cells, Macrophages M2, and resting Mast cells. In addition, decreased levels of resting memory CD4 T cells , resting NK cells, activated Dendritic cells, and activated Mast cells were also observed.

CONCLUSION

Senescence genes were related to the prognosis, response to chemotherapy drugs, and TME of GC. Our senescence-related risk model could forecast the survival of patients, their response to chemotherapy drugs, and the TME to a certain extent.

Comprehensive bioinformatics analysis reveals the oncogenic role of FoxM1 and its impact on prognosis, immune microenvironment, and drug sensitivity in osteosarcoma

Osteosarcoma, a highly malignant bone tumor primarily affecting adolescents, presents a significant challenge in cancer therapy due to its resistance to chemotherapy. This study explores the multifaceted impact of the transcription factor FoxM1 on osteosarcoma, shedding light on its pivotal role in tumor progression, immune microenvironment modulation, and drug response. Utilizing publicly available datasets from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET) databases, we conducted an in-depth bioinformatics analysis. Our findings illuminate the far-reaching implications of FoxM1 in osteosarcoma, emphasizing its significance as a potential therapeutic target. Differential expression analysis and Gene Set Enrichment Analysis (GSEA) revealed FoxM1's influence on critical pathways related to apoptosis, cell cycle regulation, and DNA repair. Notably, FoxM1 expression correlated with poor clinical outcomes in osteosarcoma patients, highlighting its prognostic relevance. Additionally, FoxM1 was found to modulate the immune microenvironment within tumor tissues, impacting immune cell infiltration, immunomodulators, immune checkpoints, and chemokines. Furthermore, a prognostic model based on FoxM1-coexpressed genes demonstrated its effectiveness in predicting patient survival. Drug sensitivity analysis indicated FoxM1's association with drug response, potentially guiding personalized treatment approaches. Hub gene screening identified RAB23 as a key target regulated by FoxM1, with RAB23 shown to influence osteosarcoma cell growth. This study also confirmed FoxM1's overexpression in osteosarcoma tissues compared to normal tissues, and its association with clinicopathological characteristics, including clinical stage, pathological type, and lung metastasis. In conclusion, FoxM1 emerges as a central player in the pathogenesis of osteosarcoma, impacting gene expression, immune responses, and therapeutic outcomes. This comprehensive analysis deepens our understanding of FoxM1's role in osteosarcoma and offers potential avenues for improved diagnosis and treatment.

Optimal combination of immune checkpoint and senescence molecule predicts adverse outcomes in patients with acute myeloid leukemia

BACKGROUND

High expression of immune checkpoints (ICs) and senescence molecules (SMs) contributes to T cell dysfunction, tumor escape, and progression, but systematic evaluation of them in co-expression patterns and prognosis in acute myeloid leukemia (AML) was lacking.

METHODS

Three publicly available datasets (TCGA, Beat-AML, and GSE71014) were first used to explore the effect of IC and SM combinations on prognosis and the immune microenvironment in AML, and bone marrow samples from 68 AML patients from our clinical center (GZFPH) was further used to validate the findings.

RESULTS

High expression of CD276, Bcl2-associated athanogene 3 (BAG3), and SRC was associated with poor overall survival (OS) of AML patients. CD276/BAG3/SRC combination, standard European Leukemia Net (ELN) risk stratification, age, and French-American-British (FAB) subtype were used to construct a nomogram model. Interestingly, the new risk stratification derived from the nomogram was better than the standard ELN risk stratification in predicting the prognosis for AML. A weighted combination of CD276 and BAG3/SRC positively corrected with TP53 mutation, p53 pathway, CD8+ T cells, activated memory CD4+ T cells, T-cell senescence score, and Tumor Immune Dysfunction and Exclusion (TIDE) score estimated by T-cell dysfunction.

CONCLUSION

High expression of ICs and SMs was associated with poor OS of AML patients. The co-expression patterns of CD276 and BAG3/SRC might be potential biomarkers for risk stratification and designing combinational immuno-targeted therapy in AML.Key MessagesHigh expression of CD276, BAG3, and SRC was associated with poor overall survival of AML patients.The co-expression patterns of CD276 and BAG3/SRC might be potential biomarkers for risk stratification and designing combinational immuno-targeted therapy in AML.

High expression of integrin-binding sialoprotein (IBSP) is associated with poor prognosis of osteosarcoma

INTRODUCTION

Osteosarcoma is a malignant tumor, accounting for 20% of primary malignant bone tumors worldwide. However, the role of IBSP as a biomarker in osteosarcoma progression has not been studied yet.

METHODS

85 cases of IBSP expression and clinical characteristics were obtained from TARGET database. Through the Kaplan-Meier curve, subgroup analysis, and univariate and multivariate Cox analysis, we further assessed the independent predictive capacity of IBSP expression for overall survival (OS) and relapse-free survival (RFS).

RESULTS

The mRNA expression of IBSP was higher in osteosarcoma than normal tissue (P < 0.0001). IBSP expression grouped by vital status showed statistical differences (P = 0.042). The race (P = 0.0183), vital status (P = 0.0034), and sample type (P = 0.0020) showed significant differences. IBSP expression exhibited satisfied diagnostic ability for osteosarcoma. The univariate and multivariate analysis confirmed that IBSP expression was an independent risk factor for OS (HR = 3.425, 95% CI: 1.604-7.313, P = 0.002) and RFS (HR = 3.377, 95% CI: 1.775-6.424, P < 0.001) in osteosarcoma patients. High IBSP expression was significantly associated with poor OS and RFS (P < 0.0001). The higher IBSP expression was observed in osteosarcoma (P < 0.001), confirmed by the IHC staining. The CCK-8 and colony formation assay showed that IBSP knockdown inhibits cell proliferation while overexpression promotes cell proliferation (P < 0.05).

CONCLUSION

High expression of IBSP was associated with poor OS and RFS. IBSP could serve as a potential biomarker for osteosarcoma, which could aid in early detection and disease monitoring.

Research progress in the mechanism and treatment of osteosarcoma

ABSTRACT

Osteosarcoma (OS) is the most common primary malignant bone tumor that more commonly occurs in children and adolescents. The most commonly used treatment for OS is surgery combined with chemotherapy, but the treatment outcomes are typically unsatisfactory. High rates of metastasis and post-treatment recurrence rates are major challenges in the treatment of OS. This underlines the need for studying the in-depth characterization of the pathogenetic mechanisms of OS and development of more effective therapeutic modalities. Previous studies have demonstrated the important role of the bone microenvironment and the regulation of signaling pathways in the occurrence and development of OS. In this review, we discussed the available evidence pertaining to the mechanisms of OS development and identified therapeutic targets for OS. We also summarized the available treatment modalities for OS and identified future priorities for therapeutics research.

HELLS modulates the stemness of intrahepatic cholangiocarcinoma through promoting senescence-associated secretory phenotype

The senescence-associated secretory phenotype (SASP) is closely associated with the tumorigenesis and progression of intrahepatic cholangiocarcinoma (ICC). However, it remains unclear its relation to stemness of ICC. In the study, the stemness indices of ICC were calculated using one-class linear regression (OCLR) and single-sample gene set enrichment analysis (ssGSEA) algorithms. A total of 14 senescence-related stemness genes (SRSGs) were identified using Pearson correlation analysis in ICC. Subsequently, a SRSGs-related classification was established using a consensus clustering for ICC. Different types of ICC exhibit distinct prognosis, immunity, metabolisms, and oncogenic signatures. Additionally, we constructed a risk score model for ICC using principal component analysis (PCA). The risk score was positively correlated with stemness, immune infiltration, metabolisms and oncogenic signatures, but negatively with prognosis in ICC. Patients with a high risk score may respond well to immunotherapy. Furthermore, we employed 3D fibrin gels to select tumor-repopulating cells (TRC) with stemness features. We found that HELLS, belonging to the 14 SRSGs, was up-regulated in ICC-TRC. And silencing HELLS significantly reduced the colony size, inhibited migration and invasion, and attenuated SASP in ICC-TRC. In summary, we provided a novel classification and risk score for ICC and uncovered a molecular mechanism via which CSLCs could obtain an active SASP.

Senescence risk score: a multifaceted prognostic tool predicting outcomes, stemness, and immune responses in colorectal cancer

Colorectal cancer (CRC) remains a primary cause of cancer mortality globally, necessitating precise prognostic indicators for effective clinical management. Our study introduces the Senescence Risk Score (SRRS), based on several senescence-related genes (SRGs), a potent prognostic tool designed to measure cellular senescence in CRC. The higher SRRS predicts a poorer prognosis, providing a novel and efficient approach to patient stratification. Notably, we found that SRRS correlates with methylation and mutation variations, and increased immune infiltration in the tumor microenvironment, thus revealing potential therapeutic targets. We also discovered an inverse relationship between SRRS and cell stemness, which could have significant implications for cancer treatment strategies. Utilizing bioinformatics resources and machine learning, we identified LIMK1 and WRN as key genes associated with SRRS, further enhancing its prognostic value. Importantly, the modulation of these genes significantly impacts cellular senescence, proliferation, and stemness in CRC cells. In summary, our development of SRRS offers a powerful tool for CRC prognosis and paves the way for novel therapeutic strategies, underscoring its potential in transforming CRC patient management.

Review of Predicting Synergistic Drug Combinations

The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.

Cellular senescence in cancer: clinical detection and prognostic implications

Cellular senescence is a state of stable cell-cycle arrest with secretory features in response to cellular stress. Historically, it has been considered as an endogenous evolutionary homeostatic mechanism to eliminate damaged cells, including damaged cells which are at risk of malignant transformation, thereby protecting against cancer. However, accumulation of senescent cells can cause long-term detrimental effects, mainly through the senescence-associated secretory phenotype, and paradoxically contribute to age-related diseases including cancer. Besides its role as tumor suppressor, cellular senescence is increasingly being recognized as an in vivo response in cancer patients to various anticancer therapies. Its role in cancer is ambiguous and even controversial, and senescence has recently been promoted as an emerging hallmark of cancer because of its hallmark-promoting capabilities. In addition, the prognostic implications of cellular senescence have been underappreciated due to the challenging detection and sparse in and ex vivo evidence of cellular senescence in cancer patients, which is only now catching up. In this review, we highlight the approaches and current challenges of in and ex vivo detection of cellular senescence in cancer patients, and we discuss the prognostic implications of cellular senescence based on in and ex vivo evidence in cancer patients.