SPP1 could be an immunological and prognostic biomarker: From pan-cancer comprehensive analysis to osteosarcoma validation.

Secreted phosphoprotein 1 (SPP1), also known as osteopontin, is a phosphorylated protein. High SPP1 expression levels have been detected in multiple cancers and are associated with poor prognosis and reduced survival rates. However, only a few pan-cancer analyses have targeted SPP1. We conducted a comprehensive analysis using multiple public databases, including TIMER and TCGA, to investigate the expression levels of SPP1 in 33 different tumor types. In addition, we verified the effect of SPP1 on osteosarcoma. To assess the impact of SPP1 on patient outcomes, we employed univariate Cox regression and Kaplan-Meier survival analyses to analyze overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in these tumor patients. We also explored SPP1 gene alterations in various tumor tissues using cBioPortal. We then examined the relationship between SPP1 and clinical characteristics, TME, immune regulatory genes, immune checkpoints, TMB, and MSI using R language. In addition, we used GSEA to investigate the molecular mechanisms underlying the role of SPP1. Bioinformatics analysis indicated that SPP1 was upregulated in 17 tumors. Overexpression of SPP1 results in poor OS, DSS, and PFI in CESC, ESCA, GBM, LGG, LIHC, PAAD, PRAD, and skin cutaneous melanoma. SPP1 expression was positively associated with immunocyte infiltration, immune regulatory genes, immune checkpoints, TMB, MSI, and drug sensitivity in certain cancers. We found that high expression of SPP1 in osteosarcoma was related to drug resistance and metastasis and further demonstrated that SPP1 can stimulate osteosarcoma cell proliferation via CCND1 by activating the PI3K/Akt pathway. These findings strongly suggest that SPP1 is a potential prognostic marker and novel target for cancer immunotherapy.

ITGB3BP is a potential biomarker associated with poor prognosis of glioma.

Despite the growing recognition of ITGB3BP as an essential feature of various cancers, the relationship between ITGB3BP and glioma remains unclear. The main aim of this study was to determine the prognostic and diagnostic value of ITGB3BP in glioma. RNA-Seq and microarray data from 2222 glioma patients were included, and we found that the expression level of ITGB3BP in glioma tissues was significantly higher than that in normal brain tissues. Moreover, ITGB3BP can be considered an independent risk factor for poor prognosis and has great predictive value for the prognosis of glioma. Gene Set Enrichment Analysis results showed that ITGB3BP contributes to the poor prognosis of glioma by activating tumour-related signalling pathways. Some small-molecule drugs were identified, such as hexestrol, which may specifically inhibit ITGB3BP and be useful in the treatment of glioma. The TIMER database analysis results revealed a correlation between the expression of ITGB3BP and the infiltration of various immune cells in glioma. Our findings provide the first evidence that the up-regulation of ITGB3BP correlates with poor prognosis in human glioma. Thus, ITGB3BP is a potential new biomarker that can be used for the clinical diagnosis and treatment of glioma.

Cysteine cathepsin C: a novel potential biomarker for the diagnosis and prognosis of glioma.

BACKGROUND: Cysteine cathepsin C encoded by the CTSC gene is an important member of the cysteine cathepsin family that plays a key role regulation of many types of tumors. However, whether CTSC is involved in the pathological process of glioma has not yet been reported. We comprehensively analyzed data from multiple databases and for the first time revealed a role and specific mechanism of action of CTSC in glioma, identifying it as a novel and efficient biomarker for the diagnosis and treatment of this brain tumor. METHODS: The expression of CTSC in glioma and its relationship with clinical characteristics and prognosis of patients with glioma were analyzed at different levels by using clinical sample information from several databases. CTSC expression levels in glioma and normal brain tissues, as well as in glioma cells and normal brain cells, was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Gene set enrichment analysis (GSEA) was used to reveal the signaling pathways that CTSC may participate in. The connectivity map was used to reveal small molecules that may inhibit CTSC expression in glioma, and the putative effect of these compounds was verified by RT-qPCR. RESULTS: Our analyses showed that the expression of CTSC in glioma was higher than that in non-cancerous cells. GSEA showed that CTSC expression may regulate the malignant development of glioma through Toll-like receptor signaling pathways, pathways in cancer, and extracellular matrix receptor interaction signaling pathways. And we proved piperlongumine and scopoletin could inhibit CTSC expression in glioma cells. CONCLUSIONS: CTSC may serve as an efficient molecular target for the diagnosis and therapy of glioma, thereby improving the poor prognosis of patients with glioma.

Identification of PYGL as a key prognostic gene of glioma by integrated bioinformatics analysis.

Aim: PYGL has been reported to have carcinogenic effects in a variety of tumors. This study is the first to reveal the relationship between PYGL and the prognosis of glioma. Materials & methods: Analyzing the Chinese Glioma Genome Atlas database, the authors revealed the expression status and prognostic value of PYGL in gliomas and used quantitative real-time PCR to verify PYGL expression again. Subsequently, they used Gene Set Enrichment Analysis to explore the biological pathways that PYGL may participate in. The authors also used the tumor immune estimation resource database to explore the relationship between PYGL and tumor immune cells. Results: PYGL is involved in the malignant progression of glioma. Conclusions: PYGL can be used as a new biomarker and molecular target for evaluating the prognosis and immunotherapy of glioma.

Overexpression of GINS4 is associated with poor prognosis and survival in glioma patients.

BACKGROUND: GINS4, an indispensable component of the GINS complex, is vital for a variety of cancer. However, no known empirical research has focused on exploring relationships between GINS4 and glioma. Thus, this study aims to understand and explain the role of GINS4 in glioma. METHOD: First, we used the data in the CGGA, TCGA, GEO, GEPIA, and HPA databases to explore the expression level of GINS4 in glioma, the correlation between GINS4 expression and the clinical features of glioma, its impact on the survival of glioma patients, and verified the analysis results through RT-qPCR, IHC, and meta-analysis. Subsequently, GSEA enrichment analysis is used to find the potential molecular mechanism of GINS4 to promote the malignant process of glioma and the anti-glioma drugs that may target GINS4 screened by CMap analysis. Moreover, we further explored the influence of the GINS4 expression on the immune microenvironment of glioma patients through the TIMER database. RESULTS: Our results suggested that GINS4 was elevated in glioma, and the overexpression of GINS4 was connected with a vast number of clinical features. The next, GINS4 as an independent prognostic factor, which can result in an unfavorable prognosis of glioma. Once more, GINS4 may be participating in the oncogenesis of glioma through JAK-STAT signaling pathways, etc. 6-thioguanine, Doxazosin, and Emetine had potential value in the clinical application of drugs targeting GINS4. Finally, the expression exhibited a close relationship with some immune cells, especially Dendritic cells. CONCLUSION: GINS4 is an independent prognostic factor that led to a poor prognosis of glioma. The present study revealed the probable underlying molecular mechanisms of GINS4 in glioma and provided a potential target for improving the prognosis of glioma.

Overexpressed XRCC2 as an independent risk factor for poor prognosis in glioma patients.

BACKGROUND: XRCC2, a homologous recombination-related gene, has been reported to be associated with a variety of cancers. However, its role in glioma has not been reported. This study aimed to find out the role of XRCC2 in glioma and reveal in which glioma-specific biological processes is XRCC2 involved based on thousands of glioma samples, thereby, providing a new perspective in the treatment and prognostic evaluation of glioma. METHODS: The expression characteristics of XRCC2 in thousands of glioma samples from CGGA and TCGA databases were comprehensively analyzed. Wilcox or Kruskal test was used to analyze the expression pattern of XRCC2 in gliomas with different clinical and molecular features. The effect of XRCC2 on the prognosis of glioma patients was explored by Kaplan-Meier and Cox regression. Gene set enrichment analysis (GSEA) revealed the possible cellular mechanisms involved in XRCC2 in glioma. Connectivity map (CMap) was used to screen small molecule drugs targeting XRCC2 and the expression levels of XRCC2 were verified in glioma cells and tissues by RT-qPCR and immunohistochemical staining. RESULTS: We found the overexpression of XRCC2 in glioma. Moreover, the overexpressed XRCC2 was associated with a variety of clinical features related to prognosis. Cox and meta-analyses showed that XRCC2 is an independent risk factor for the poor prognosis of glioma. Furthermore, the results of GSEA indicated that overexpressed XRCC2 could promote malignant progression through involved signaling pathways, such as in the cell cycle. Finally, doxazosin, quinostatin, canavanine, and chrysin were identified to exert anti-glioma effects by targeting XRCC2. CONCLUSIONS: This study analyzed the expression pattern of XRCC2 in gliomas and its relationship with prognosis using multiple datasets. This is the first study to show that XRCC2, a novel oncogene, is significantly overexpressed in glioma and can lead to poor prognosis in glioma patients. XRCC2 could serve as a new biomarker for glioma diagnosis, treatment, and prognosis evaluation, thus bringing new insight into the management of glioma.

GNG5 is a novel oncogene associated with cell migration, proliferation, and poor prognosis in glioma.

BACKGROUND: Although many biomarkers have been reported for detecting glioma, the prognosis for the disease remains poor, and therefore, new biomarkers need to be identified. GNG5, which is part of the G-protein family, has been associated with different malignant tumors, though the role of GNG5 in glioma has not been studied. Therefore, we aimed to identify the relationship between GNG5 and glioma prognosis and identify a new biomarker for the diagnosis and treatment of gliomas. METHODS: We used data on more than a thousand gliomas from multiple databases and clinical data to determine the expression of GNG5 in glioma. Based on clinical data and CGGA database, we identified the correlation between GNG5 and multiple molecular and clinical features and prognosis using various analytical methods. Co-expression analysis and GSEA were performed to detect GNG5-related genes in glioma and possible signaling pathways involved. ESTIMATE, ssGSEA, and TIMER were used to detect the relationship between GNG5 and the immune microenvironment. Functional experiments were performed to explore the function of GNG5 in glioma cells. RESULTS: GNG5 is highly expressed in gliomas, and its expression level is positively correlated with pathological grade, histological type, age, and tumor recurrence and negatively correlated with isocitrate dehydrogenase mutation, 1p/19 co-deletion, and chemotherapy. Moreover, GNG5 as an independent risk factor was negatively correlated with the overall survival time. GSEA revealed the potential signaling pathways involved in GNG5 function in gliomas, including cell adhesion molecules signaling pathway. The ssGSEA, ESTIMATE, and TIMER based analysis indicated a correlation between GNG5 expression and various immune cells in glioma. In vivo and in vitro experiments showed that GNG5 could participate in glioma cell proliferation and migration. CONCLUSIONS: Based on the large data platform and the use of different databases to corroborate results obtained using various datasets, as well as in vitro and in vivo experiments, our study reveals for the first time that GNG5, as an oncogene, is overexpressed in gliomas and can inhibit the proliferation and migration of glioma cells and lead to poor prognosis of patients. Thus, GNG5 is a potential novel biomarker for the clinical diagnosis and treatment of gliomas.

Tertiary lymphoid structures in cancer: immune mechanisms and clinical implications.

Cancer is a major cause of death globally, and traditional treatments often have limited efficacy and adverse effects. Immunotherapy has shown promise in various malignancies but is less effective in tumors with low immunogenicity or immunosuppressive microenvironment, especially sarcomas. Tertiary lymphoid structures (TLSs) have been associated with a favorable response to immunotherapy and improved survival in cancer patients. However, the immunological mechanisms and clinical significance of TLS in malignant tumors are not fully understood. In this review, we elucidate the composition, neogenesis, and immune characteristics of TLS in tumors, as well as the inflammatory response in cancer development. An in-depth discussion of the unique immune characteristics of TLSs in lung cancer, breast cancer, melanoma, and soft tissue sarcomas will be presented. Additionally, the therapeutic implications of TLS, including its role as a marker of therapeutic response and prognosis, and strategies to promote TLS formation and maturation will be explored. Overall, we aim to provide a comprehensive understanding of the role of TLS in the tumor immune microenvironment and suggest potential interventions for cancer treatment.

PRKDC Induces Chemoresistance in Osteosarcoma by Recruiting GDE2 to Stabilize GNAS and Activate AKT.

Chemoresistance is one of the major causes of poor prognosis in osteosarcoma. Alternative therapeutic strategies for osteosarcoma are limited, indicating that increasing sensitivity to currently used chemotherapies could be an effective approach to improve patient outcomes. Using a kinome-wide CRISPR screen, we identified PRKDC as a critical determinant of doxorubicin (DOX) sensitivity in osteosarcoma. The analysis of clinical samples demonstrated that PRKDC was hyperactivated in osteosarcoma, and functional experiments showed that the loss of PRKDC significantly increased sensitivity of osteosarcoma to DOX. Mechanistically, PRKDC recruited and bound GDE2 to enhance the stability of protein GNAS. The elevated GNAS protein levels subsequently activated AKT phosphorylation and conferred resistance to DOX. The PRKDC inhibitor AZD7648 and DOX synergized and strongly suppressed the growth of osteosarcoma in mouse xenograft models and human organoids. In conclusion, the PRKDC-GDE2-GNAS-AKT regulatory axis suppresses DOX sensitivity and comprises targetable candidates for improving the efficacy of chemotherapy in osteosarcoma. Significance: Targeting PRKDC suppresses AKT activation and increases sensitivity to doxorubicin in osteosarcoma, which provides a therapeutic strategy for overcoming chemoresistance.

Integrative analysis of TROAP with molecular features, carcinogenesis, and related immune and pharmacogenomic characteristics in soft tissue sarcoma.

Soft tissue sarcoma (STS) is an uncommon malignancy that often carries a grim prognosis. Trophinin-associated protein (TROAP) is augmented in a variety of tumors and can affect tumor proliferation. Nevertheless, the prognostic value and specific functions of TROAP in STS are still vague. Herein, we display that TROAP exhibits an augmented trend in STS, and its elevation correlates with a poor prognosis of STS. Furthermore, its reduction is related to increased immune cell infiltration, enhanced stroma, and elevation of immune activation. Meanwhile, the TROAP-derived genomic signature is validated to predict patient prognosis, immunotherapy, and drug response reliably. A nomogram constructed based on age, metastatic status, and a TROAP-derived risk score of an STS individual could be used to quantify the survival probability of STS. In addition, in vitro experiments have demonstrated that TROAP is overexpressed in STS, and the downregulation of TROAP could affect the proliferation, migration, metastasis, and cell cycle of STS cells. In summary, the TROAP expression is elevated in STS tissues and cells, which is related to the poor prognosis and malignant biological behaviors of STS. It could act as a potential prognostic biomarker for diagnosis and treatment of STS.

Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma.

Background: Soft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS. Methods: The ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis. Results: A novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells. Conclusion: In sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

Integration analysis based on fatty acid metabolism robustly predicts prognosis, dissecting immunity microenvironment and aiding immunotherapy for soft tissue sarcoma.

Background: Soft tissue sarcoma (STS) is a highly malignant tumor with a dismal prognosis. Presently, the dysregulation of fatty acid metabolism has received increasing attention in tumor research, but fewer reports are relevant to STS. Methods: Based on fatty acid metabolism-related genes (FRGs), a novel risk score for STS was developed utilizing univariate analysis and least absolute shrinkage selection operator (LASSO) Cox regression analyses in the STS cohort, which were further validated using the external validation cohort from other databases. Furthermore, independent prognostic analysis, C-index, ROC curves, and nomogram were carried out to investigate the predictive performance of fatty acid-related risk scores. We also analysed the differences in enrichment pathways, the immune microenvironment, gene mutations, and immunotherapy response between the two distinct fatty acid score groups. Moreover, the real-time quantitative polymerase chain reaction (RT-qPCR) was used to further verify the expression of FRGs in STS. Results: A total of 153 FRGs were retrieved in our study. Next, a novel fatty acid metabolism-related risk score (FAS) was constructed based on 18 FRGs. The predictive performance of FAS was also verified in external cohorts. In addition, the independent analysis, C-index, ROC curve, and nomograph also revealed that FAS could serve as an independent prognostic factor for the STS patients. Meanwhile, our results demonstrated that the STS cohort in two distinct FAS groups had different copy number variations, immune cell infiltration, and immunotherapy responses. Finally, the in vitro validation results demonstrated that several FRGs included in the FAS exhibited abnormal expression in STS. Conclusion: Altogether, our work comprehensively and systematically clarifies fatty acid metabolism's potential roles and clinical significance in STS. The novel individualized score based on fatty acid metabolism may be provided as a potential marker and treatment strategy in STS.

Novel insights into the multifaceted roles of m6A-modified LncRNAs in cancers: biological functions and therapeutic applications.

N6-methyladenosine (m6A) is considered as the most common and important internal transcript modification in several diseases like type 2 diabetes, schizophrenia and especially cancer. As a main target of m6A methylation, long non-coding RNAs (lncRNAs) have been proved to regulate cellular processes at various levels, including epigenetic modification, transcriptional, post-transcriptional, translational and post-translational regulation. Recently, accumulating evidence suggests that m6A-modified lncRNAs greatly participate in the tumorigenesis of cancers. In this review, we systematically summarized the biogenesis of m6A-modified lncRNAs and the identified m6A-lncRNAs in a variety of cancers, as well as their potential diagnostic and therapeutic applications as biomarkers and therapeutic targets, hoping to shed light on the novel strategies for cancer treatment.

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning.

Background: Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS. Methods: We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR). Results: Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS. Conclusion: In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

A Necroptosis-Related lncRNA Signature Predicts Prognosis and Indicates the Immune Microenvironment in Soft Tissue Sarcomas.

Background: The necroptosis and long noncoding RNA (lncRNA) are critical in the occurrence and development of malignancy, while the association between the necroptosis-related lncRNAs (NRlncRNAs) and soft tissue sarcoma (STS) remains controversial. Therefore, the present study aims to construct a novel signature based on NRlncRNAs to predict the prognosis of STS patients and investigate its possible role. Methods: The transcriptome data and clinical characteristics were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression database (GTEx). A novel NRlncRNA signature was established and verified by the COX regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the K-M survival analysis, ROC, univariate, multivariate Cox regression analysis, and nomogram were used to evaluate the predictive value of the signature. Also, a variety of bioinformatic analysis algorithms explored the differences between the potential mechanism, tumor immune status, and drug sensitivity in the two-risk group. Finally, the RT-qPCR was performed to evaluate the expression of signature NRlncRNAs. Results: A novel signature consisting of seven NRlncRNAs was successfully established and verified with stable prediction performance and general applicability for STS. Next, the GSEA showed that the patients in the high-risk group were mainly enriched with tumor-related pathways, while the low-risk patients were significantly involved in immune-related pathways. In parallel, we found that the STS patients in the low-risk group had a better immune status than that in the high-risk group. Additionally, there were significant differences in the sensitivity to anti-tumor agents between the two groups. Finally, the RT-qPCR results indicated that these signature NRlncRNAs were abnormally expressed in STS. Conclusion: To the best of our knowledge, it is the first study to construct an NRlncRNA signature for STS. More importantly, the novel signature displays stable value and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in STS.

Kinematic alignment versus mechanical alignment in primary total knee arthroplasty: an updated meta-analysis of randomized controlled trials.

BACKGROUND: The purpose of this study was to perform an updated meta-analysis to compare the outcomes of kinematic alignment (KA) and mechanical alignment (MA) in patients undergoing total knee arthroplasty. METHODS: PubMed, EMBASE, Web of Science, Google Scholar, and the Cochrane Library were systematically searched. Eligible randomized controlled trials regarding the clinical outcomes of patients undergoing total knee arthroplasty with KA and MA were included for the analysis. RESULTS: A total of 1112 participants were included in this study, including 559 participants with KA and 553 patients with MA. This study revealed that the Western Ontario and McMaster Universities Osteoarthritis Index, Knee Society Score (knee and combined), and knee flexion range were better in the patients with kinematic alignment than in the mechanical alignment. In terms of radiological results, the femoral knee angle, mechanical medial proximal tibial angle, and joint line orientation angle were significantly different between the two techniques. Perioperatively, the walk distance before discharge was longer in the KA group than in the MA group. In contrast, other functional outcomes, radiological results, perioperative outcomes, and postoperative complication rates were similar in both the kinematic and mechanical alignment groups. CONCLUSIONS: The KA technique achieved better functional outcomes than the mechanical technique in terms of KSS (knee and combined), WOMAC scores, and knee flexion range. PROSPERO trial registration number CRD42021264519. Date registration: July 28, 2021.

A novel necroptosis-related lncRNAs signature effectively predicts the prognosis for osteosarcoma and is associated with immunity.

Background: Necroptosis is closely related to tumorigenesis and development. Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) are also central players in osteosarcoma (OS). However, the role of necroptosis-related lncRNAs in OS remains unclear. In the present study, we aim to craft a prognostic signature based on necroptosis-related lncRNAs to improve the OS prognosis prediction. Methods: The signature based on necroptosis-related lncRNAs was discovered using univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The prognosis efficiency of the signature was then estimated by employing various bioinformatics methods. Subsequently, immunological analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the association between necroptosis-related lncRNAs with clinical outcomes and immune status. More importantly, several necroptosis-related lncRNAs were validated with RT-qPCR. Results: Consequently, a novel prognosis signature was successfully constructed based on eight necroptosis-related lncRNAs. Meanwhile, the novel necroptosis-related lncRNAs model could distribute OS patients into two risk groups with a stable and accurate predictive ability. Additionally, the GSEA and immune analysis revealed that the necroptosis-related lncRNAs signature affects the development and prognosis of OS by regulating the immune status. The necroptosis-related lncRNA signature was closely correlated with multiple anticancer agent susceptibility. Moreover, the RT-qPCR results indicated several necroptosis-related lncRNAs were significantly differently expressed in osteosarcoma and osteoblast cell lines. Conclusion: In this summary, a novel prognostic signature integrating necroptosis-related lncRNAs was firstly constructed and could accurately predict the prognosis of OS. This study may increase the predicted value and guide the personalized chemotherapy treatment for OS.

Identification of cuproptosis-related lncRNA prognostic signature for osteosarcoma.

Background: Copper is an indispensably mineral element involved in various metabolic processes and functions in the active sites of many metalloproteins. Copper dysregulation is associated with cancers such as osteosarcoma (OS), the most common primary bone malignancy with invasiveness and metastasis. However, the causality between cuproptosis and OS remains elusive. We aim to identify cuproptosis-related long non-coding RNAs (lncRNAs) for osteosarcomatous prognosis, immune microenvironment response, and immunotherapy. Methods: The Person correlation and differential expression analysis were used to identify differentially expressed cuproptosis-related lncRNAs (CRLs). The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were performed to construct the CRL signature. The Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curve, internal validation, independent prognostic analysis, and nomograph were used to evaluate the prognostic value. The functional enrichment, tumor microenvironment, immunotherapy and chemotherapy response between the two distinct groups were further explored using a series of algorithms. The expression of signature CRLs was verified by real-time quantitative polymerase chain reaction (RT-qPCR) in OS cell lines. Results: A novel CRL signature consisting of four CRLs were successfully identified. The K-M survival analysis indicated that the OS patients in the low-risk groups had a better prognosis than that in the high-risk group. Then, the ROC curve and subgroup survival analysis confirmed the prognostic evaluation performance of the signature. Equally, the independent prognostic analysis demonstrated that the CRL signature was an independently predicted factor for OS. Friends analysis determined the hub genes that played a critical role in differentially expressed genes between two distinct risk groups. In addition, the risk score was related to immunity status, immunotherapy response, and chemotherapeutic drug sensitivity. Finally, the expression of these signature CRLs detected by RT-qPCR was consistent with the bioinformatic analysis results. Conclusion: In summary, our study confirmed that the novel CRL signature could effectively evaluate prognosis, tumor immune microenvironment, and immunotherapy response in OS. It may benefit for clinical decision-making and provide new insights for personalized therapeutics.

GNG12 as A Novel Molecular Marker for the Diagnosis and Treatment of Glioma.

Purpose: GNG12 influences a variety of tumors; however, its relationship with glioma remains unclear. The aim of this study was to comprehensively investigate the relationship between GNG12 and the clinical characteristics and prognosis of glioma patients and reveal the mechanisms causing the malignant process of GNG12. Materials and Methods: We obtained information on clinical samples from multiple databases. The expression level of GNG12 was validated using a RT-qPCR and IHC. KM curves were used to assess the correlation between the GNG12 expression and OS of glioma patients. An ROC curve was drawn to assess the predictive performance of GNG12. Univariate and multivariate Cox analyses were performed to analyze the factors affecting the prognosis of patients with glioma. GSEA and TIMER databases were used to estimate the relationship between GNG12 expression, possible molecular mechanisms, and immune cell infiltration. CMap analysis was used to screen candidate drugs for glioma. Subsequent in vitro experiments were used to validate the proliferation and migration of glioma cells and to explore the potential mechanisms by which GNG12 causes poor prognosis in gliomas. Results: GNG12 was overexpressed in glioma patients and GNG12 expression level correlated closely with clinical features, including age and histological type, etc. Subsequently, the K-M survival analysis indicated that the expression level of GNG12 was relevant to the prognosis of glioma, and the ROC curve implied that GNG12 can predict glioma stability. Univariate and multivariate analyses showed that GNG12 represents a risk factor for glioma occurrence. GNG12 expression is closely associated with some immune cells. Additionally, several in vitro experiments demonstrated that down-regulation of GNG12 expression can inhibits the proliferation and migration capacity of glioma cells. Ultimately, the results for the GSEA and WB experiments revealed that GNG12 may promote the malignant progression of gliomas by regulating the cell adhesion molecule cell signaling pathway. Conclusion: In this study, we identified GNG12 as a novel oncogene elevated in gliomas. Reducing GNG12 expression inhibits the proliferation and migration of glioma cells. In summary, GNG12 can be used as a novel biomarker for the early diagnosis of human gliomas and as a potential therapeutic target.

Effects of ESCO2 or its methylation on the prognosis, clinical characteristics, immune microenvironment, and pathogenesis of low-grade glioma.

The establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) has an important regulatory effect on cell proliferation and division, which is closely related to the malignant process of glioma cells. Therefore, this study attempts to provide a target for biologically targeted therapy for low-grade glioma (LGG) by demonstrating the regulatory effect of ESCO2 during the pathological process of LGG. First, the 1064 samples of LGG transcriptomic data and corresponding clinicopathological information obtained from various databases were included in the study. Second, the chi-squared test showed that the expression of ESCO2 was associated with the malignant characteristics of LGG (recurrence and grade), and Kaplan Meier and multivariate analysis suggested that ESCO2 was an independent risk factor, resulting in a significant reduction in the overall duration of survival of patients. Third, co-expression analysis showed that the level of mRNA expression of ESCO2 was negatively regulated by multiple methylation sites (cg04108328, cg12564175, and cg26534677), and the hypermethylation status of cg12564175 could prolong the overall survival of patients. Fourth, the Tumor Immune Estimation Resource (TIMER) database shows that ESCO2 can have a positive regulatory relationship with six different immune cells, such as CD8 + T cells and macrophages, and a positive expression relationship with PD-1 and PD-L1. Finally, Gene Set Enrichment Analysis (GSEA) showed that ESCO2 may play a carcinogenic role by affecting cell replication and DNA repair. In summary, this study confirmed the carcinogenic effect of ESCO2 on LGG for the first time. It is speculated that both the mRNA of ESCO2 and its methylation site (cg12564175) can be useful biological targets for molecular targeted therapy of LGG.