Role of a Pyroptosis-Related lncRNA Signature in Risk Stratification and Immunotherapy of Ovarian Cancer

Background: Pyroptosis is a newly recognized form of cell death. Emerging evidence has suggested the crucial role of long non-coding RNAs (lncRNAs) in the tumorigenesis and progression of ovarian cancer (OC). However, there is still poor understanding of pyroptosis-related lncRNAs in OC.Methods: The TCGA database was accessed for gene expression and clinical data of 377 patients with OC. Two cohorts for training and validation were established by random allocation. Correlation analysis and Cox regression analysis were performed to identify pyroptosis-related lncRNAs and construct a risk model.Results: Six pyroptosis-related lncRNAs were included in the final signature with unfavorable survival data. Subsequent ROC curves showed promising predictive value of patient prognosis. Further multivariate regression analyses confirmed the signature as an independent risk factor in the training (HR: 2.242, 95% CI: 1.598–3.145) and validation (HR: 1.884, 95% CI: 1.204–2.95) cohorts. A signature-based nomogram was also established with a C-index of.684 (95% CI: 0.662–0.705). Involvement of the identified signature in multiple immune-related pathways was revealed by functional analysis. Moreover, the signature was also associated with higher expression of three immune checkpoints (PD-1, B7-H3, and VSIR), suggesting the potential of the signature as an indicator for OC immunotherapies.Conclusion: This study suggests that the identified pyroptosis-related lncRNA signature and signature-based nomogram may serve as methods for risk stratification of OC. The signature is also associated with the tumor immune microenvironment, potentially providing an indicator for patient selection of immunotherapy in OC.

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The Prognostic Value of the m6A Score in Multiple Myeloma Based on Machine Learning

BioMedInformatics ◽

10.3390/biomedinformatics1030006 ◽

2021 ◽

Vol 1 (3) ◽

pp. 77-87

Author(s):

Gong Xiao ◽

Qiongjing Yuan ◽

Wei Wang

Keyword(s):

Multiple Myeloma ◽

Cancer Progression ◽

Risk Score ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Microarray Dataset ◽

Immune Checkpoints ◽

Bioinformatics Analyses ◽

Cox Regression Analysis

Background: Multiple myeloma (MM) is one of the most common cancers of the blood system. N6-methyladenosine (m6A) plays an important role in cancer progression. We aimed to investigate the prognostic relevance of the m6A score in multiple myeloma through a series of bioinformatics analyses. Methods: The microarray dataset GSE4581 and GSE57317 used in this study were downloaded from the Gene Expression Omnibus (GEO) database. The m6A score was calculated using the GSVA package. The Random forests, univariate Cox regression analysis and Lasso analyses were performed for the differentially expressed genes (DEGs). Kaplan–Meier analysis and an ROC curve were used to diagnose the effectiveness of the model. Results: The GSVA R software package was used to predict the function. A total of 21 m6A genes were obtained, and 286 DEGs were identified between high and low m6A score groups. The risk model was constructed and composed of PRX, LBR, RB1, FBXL19-AS1, ARSK, MFAP3L, SLC44A3, UNC119 and SHCBP1. Functional analysis of risk score showed that with the increase in the risk score, Activated CD4 T cells, Memory B cells and Type 2 T helper cells were highly infiltrated. Conclusions: Immune checkpoints such as HMGB1, TGFB1, CXCL9 and HAVCR2 were significantly positively correlated with the risk score. We believe that the m6A score has a certain prognostic value in multiple myeloma.

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Immune-related miRNA signature identifies prognosis and immune landscape in head and neck squamous cell carcinomas

Bioscience Reports ◽

10.1042/bsr20201820 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Bo Ma ◽

Hui Li ◽

Jia Qiao ◽

Tao Meng ◽

Riyue Yu

Keyword(s):

Head And Neck ◽

Squamous Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Immune Checkpoints ◽

Cox Regression Analysis ◽

Immunosuppressive Microenvironment ◽

Selection Operator

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is recognised as an immune active cancer, but little is known about the role of microRNAs (miRNAs) in it. In the present study, we aim to determine a prognostic and immune-related miRNAs signature (IRMS) in HNSCC. Methods: Spearman correlation analysis was used to screen out prognostic immune-related miRNAs based on single-sample gene set enrichment analysis (ssGSEA). Least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish IRMS in HNSCC. Then, the influence of the IRMS on HNSCC was comprehensively analysed. Results: We obtained 11 prognostic immune-related miRNAs based on ssGSEA. Then an IRMS integrated with six miRNAs was established through LASSO Cox regression analysis. The stratification survival analysis indicated that IRMS was independent from other characteristics and performed favourably in the overall survival (OS) prediction. The function annotation suggested that IRMS was highly associated with the immune-related response biological processes and pathways which are so important for tumorigenesis of HNSCC. Moreover, the nomogram demonstrated that our model was identified as an independent prognostic factor. In addition, we found that IRMS was significantly correlated with the immune infiltration and expression of critical immune checkpoints, indicating that the poor prognosis might be caused partly by immunosuppressive microenvironment. Conclusion: We established a novel IRMS, which exhibited a potent prognostic value and could be representative of immune status in HNSCC.

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Long-term survival outcomes of intravenous versus intraperitoneal chemotherapy in the treatment of advanced ovarian cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.6046 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 6046-6046 ◽

Cited By ~ 1

Author(s):

Rachel Soyoun Kim ◽

Manjula Maganti ◽

Marcus Bernardini ◽

Stephane Laframboise ◽

Sarah E. Ferguson ◽

...

Keyword(s):

Ovarian Cancer ◽

Cox Regression ◽

Residual Disease ◽

Advanced Ovarian Cancer ◽

Term Survival ◽

Long Term Survival ◽

Cox Regression Analysis

6046 Background: The role of intraperitoneal (IP) chemotherapy in the management of advanced ovarian cancer has been questioned given emerging evidence showing lack of survival benefits. The objective of this study was to compare the long-term survival associated with IP chemotherapy at a tertiary cancer center. Methods: We reviewed the long-term survival records of 271 women with stage IIIC or IV high-grade serous ovarian cancer treated with primary cytoreductive surgery (PCS) followed by IP or intravenous (IV) chemotherapy between 2001-2015 with a minimum follow-up of 4 years. 5-year progression free (PFS) and overall survival (OS) rates were compared using Kaplan-Meier survival analysis and covariates were evaluated using Cox regression analysis. Results: Women who received IP chemotherapy after PCS (n = 91) were more likely to have undergone aggressive surgery (p < 0.001), longer surgery (p < 0.001), and had no residual disease (p < 0.001) compared to the IV arm (n = 180). Median follow-up was 51.6 months. Five-year PFS was 19% vs. 18% (p = 0.63) and OS was 73% vs. 44% (p = 0.00016) in the IP vs. IV arms, respectively. After controlling for covariates in a multivariable model, the use of IP was no longer a significant predictor of OS in the entire cohort (p = 0.12). In patients with 0mm residual disease, PFS was 28% vs. 26% (p = 0.67) and OS was 81% vs. 60% (p = 0.059) in IP (n = 61) vs. IV (n = 69), respectively. In patients with residual of 1-9mm, PFS was 30% vs. 48% (p = 0.076) and OS was 60% vs. 43% (p = 0.74) in IP (n = 29) vs. IV (n = 31), respectively. Conclusions: IP chemotherapy showed a trend towards improved survival over conventional IV chemotherapy, especially in patients with no residual disease. Given the retrospective nature and small numbers in this study, prospective non-randomized cohort studies are warranted to evaluate the role of IP chemotherapy in advanced ovarian cancer.

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Identification of SnoRNAs Predicting Prognosis for Ovarian Cancer

10.21203/rs.3.rs-1022969/v1 ◽

2021 ◽

Author(s):

Wenjing Zhu ◽

Tao Zhang ◽

Shaohong Luan ◽

Qingnuan Kong ◽

Wenmin Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Prediction Model ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

Clinicopathological Parameters ◽

Systematic Research ◽

Cox Regression Analysis ◽

Plot Analysis ◽

Number Variation

Abstract Background: Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV).Material/methods: 379 OV patients with RNA-Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of snoRNAs. ROC curves and KM plot curves were applied to validate the prediction model. Besides, the model was validated in 5 paired clinical tissues by real-time PCR, H&E staining and immunohistochemistry. Results: A prognostic model was constructed on the basis of SnoRNAs in OV patients.Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumorsize, advanced stage and with tumor status. KM plot analysis confirmed that patients with high RiskScore had poorer prognosis in subgroup of age, tumor size and stage. 7 of 9 snoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 snoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, H&E staining and immunohistochemistry of Ki67, P53 and P16 were confirmed that patients with higher RiskScore are more malignant. Conclusions: In summary, we identified a nine-snoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.

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A Novel Assessment Model Based on Molecular Subtypes of Hypoxia-Related LncRNAs for Prognosis of Bladder Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.718991 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xianwu Chen ◽

Yan Zhang ◽

Feifan Wang ◽

Xuejian Zhou ◽

Qinghe Fu ◽

...

Keyword(s):

Bladder Cancer ◽

Risk Model ◽

Cox Regression ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Tumor Therapy ◽

Assessment Model ◽

Immune Checkpoints ◽

Cox Regression Analysis ◽

Model Based

Hypoxia is a common feature in various tumors that regulates aggressiveness. Previous studies have demonstrated that some dysregulated long non-coding RNAs (lncRNAs) are correlated with tumor progression, including bladder cancer (BCa). However, the prognostic effect of hypoxia-related lncRNAs (HRLs) and their clinical relevance, as well as their regulatory effect on the tumor immune microenvironment, are largely unknown in BCa. A co-expression analysis between hypoxia genes and lncRNA expression, which was downloaded from the TCGA database, was performed to identify HRLs. Univariate Cox regression analysis was performed to select the most desirable lncRNAs for molecular subtype, and further LASSO analysis was performed to develop a prognostic model. This molecular subtype based on four HRLs (AC104653, AL136084, AL139393, and LINC00892) showed good performance in the tumor microenvironment and tumor mutation burden. The prognostic risk model suggested better performance in predicting BCa patients’ prognosis and obtained a close correlation with clinicopathologic features. Furthermore, four of five first-line clinical chemotherapies showed different sensitivities to this model, and nine immune checkpoints showed different expression in the molecular subtypes or the risk model. In conclusion, this study indicates that this molecular subtype and risk model based on HRLs may be useful in improving the prognostic prediction of BCa patients with different clinical situations and may help to find a useful target for tumor therapy.

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Prognostic Score and Associations With Survival Improvement Offered by Radiotherapy After Surgery for Adenocarcinomas of Parotid Gland: A Population-based Longitudinal Cohort Study

10.21203/rs.3.rs-73079/v1 ◽

2020 ◽

Author(s):

Wenlong Qiu ◽

Wenhao Zhang

Keyword(s):

Cohort Study ◽

Risk Stratification ◽

Survival Benefit ◽

Risk Model ◽

Cox Regression ◽

Postoperative Radiotherapy ◽

Prognostic Score ◽

Population Based ◽

Comprehensive Treatment ◽

Cox Regression Analysis

Abstract Background: Radiotherapy (RT) after surgery is a treatment option in the management of parotid adenocarcinoma, and the role of adjuvant RT for parotid adenocarcinoma remains to be clarified. The survival benefit of postoperative radiotherapy (PORT) based on prognostic risk factors needs further determination.Materials and methods: In this retrospective cohort study using SEER data, patients were divided into surgery+RT (RT group) and surgery alone (non-RT group). A prognostic risk model was constructed to stratify patients based on the survival rate. We performed a Cox regression analysis with propensity score weighting to evaluate survival benefit between the two groups. Results: We identified 2223 eligible patients with parotid adenocarcinoma, 1449 (65.2%) in the RT group and 774 (34.8%) in the non-RT group. Overall, 674 cancer-specific deaths occurred over a median follow-up of 141 months, the overall survival (OS) of RT group was better than that of non-RT group in the weighted analysis (HR=0.656, 95% CI=0.487-0.882, P=0.005). Significant survival improvements in the RT group compared with the non-RT group were only observed in patients with high risk (HR=0.647, 95% CI=0.426-0.983, P=0.041). The survival benefit of RT was significantly correlated with prognostic risk stratification (P<.001).Conclusion: In this population-based study, the patient prognostic risk stratification for parotid adenocarcinoma is associated with the magnitude of survival improvement by RT after surgery, suggesting that this risk model could provide decision guidance on comprehensive treatment strategy.

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Comprehensive Analysis of the Immune Effect of TGFβ1 in Colorectal Adenocarcinoma:A TGFβ1-related Prognostic Model of Tumor Microenvironment

10.21203/rs.3.rs-477295/v1 ◽

2021 ◽

Author(s):

Yucheng Qian ◽

Lina Zhang ◽

Jihang Wen ◽

Yanxia Mei ◽

Jingsun Wei ◽

...

Keyword(s):

Tumor Microenvironment ◽

Survival Data ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

High Expression ◽

Immune Checkpoints ◽

Cox Regression Analysis

Abstract ColorColorectal cancer is one of the most common cancer worldwide. Recently, tumor microenvironment (TME), especially its remoulding , is thought to control the colorectal cancer genesis and progression. In this study, we use ESTIMATEscore to make out the proportion of immune and stromal components in colorectal adenocarcinoma (CRA) samples from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were found by COX regression analysis and protein-protein interaction (PPI) network, among which TGFβ1 was supposed to be a prognosis factor and tumor environment indicator. Continuous analysis showed that TGFβ1 expression is positively correlated with lymph node metastasis (N stage) but negatively correlated with survival. Gene Set Enrichment Analysis (GSEA) revealed that the genes of the high-expression TGFβ1 group were mainly enriched in immune-related activities. Cluster analysis divided the samples into 2 subgroups. 24 HLA-related genes and 8 immune checkpoint genes were found upregulated in the high immunity group as well as TGFβ1, which suggests the possibility of novel therapies targeting immune checkpoints combined with TGFβ1. Tumor-infiltrating immune cell (TIC) profile of CRA patients was described by CIBERSORT analysis. Further analysis showed that the infiltration of Tregs and Neutrophils were positively correlated with TGFβ1 high expression. Then 3 TGFβ1-related genes were picked out to construct a prognostic model, which matches the survival data well.

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Identification and validation of lncRNAs involved in m6A regulation for patients with ovarian cancer

Cancer Cell International ◽

10.1186/s12935-021-02076-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jianfeng Zheng ◽

Jialu Guo ◽

Benben Cao ◽

Ying Zhou ◽

Jinyi Tong

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

Clinicopathological Characteristics ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Both N6-methyladenosine (m6A) modification and lncRNAs play an important role in the carcinogenesis and cancer inhibition of ovarian cancer (OC). However, lncRNAs involved in m6A regulation (LI-m6As) have never been reported in OC. Herein, we aimed to identify and validate a signature based on LI-m6A for OC. Methods RNA sequencing profiles with corresponding clinical information associated with OC and 23 m6A regulators were extracted from TCGA. The Pearson correlation coefficient (PCC) between lncRNAs and 23 m6A regulators (|PCC|> 0.4 and p < 0.01) was calculated to identify LI-m6As. The LI-m6As with significant prognostic value were screened based on univariate Cox regression analysis to construct a risk model by LASSO Cox regression. Gene Set Enrichment Analysis (GSEA) was implemented to survey the biological functions of the risk groups. Several clinicopathological characteristics were utilized to evaluate their ability to predict prognosis, and a nomogram was constructed to evaluate the accuracy of survival prediction. Besides, immune microenvironment, checkpoint, and drug sensitivity in the two risk groups were compared using comprehensive algorithms. Finally, real-time qPCR analysis and cell counting kit-8 assays were performed on an alternative lncRNA, CACNA1G-AS1. Results The training cohort involving 258 OC patients and the validation cohort involving 111 OC patients were downloaded from TCGA. According to the PCC between the m6A regulators and lncRNAs, 129 LI-m6As were obtained to perform univariate Cox regression analysis and then 10 significant prognostic LI-m6As were identified. A prognostic signature containing four LI-m6As (AC010894.3, ACAP2-IT1, CACNA1G-AS1, and UBA6-AS1) was constructed according to the LASSO Cox regression analysis of the 10 LI-m6As. The prognostic signature was validated to show completely opposite prognostic value in the two risk groups and adverse overall survival (OS) in several clinicopathological characteristics. GSEA indicated that differentially expressed genes in disparate risk groups were enriched in several tumor-related pathways. At the same time, we found significant differences in some immune cells and chemotherapeutic agents between the two groups. An alternative lncRNA, CACNA1G-AS1, was proven to be upregulated in 30 OC specimens and 3 OC cell lines relative to control. Furthermore, knockdown of CACNA1G‐AS1 was proven to restrain the multiplication capacity of OC cells. Conclusions Based on the four LI-m6As (AC010894.3, ACAP2-IT1, CACNA1G-AS1, and UBA6-AS1), the risk model we identified can independently predict the OS and therapeutic value of OC. CACNA1G‐AS1 was preliminarily proved to be a malignant lncRNA.

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Integrin α2β1 Represents a Prognostic and Predictive Biomarker in Primary Ovarian Cancer

Biomedicines ◽

10.3390/biomedicines9030289 ◽

2021 ◽

Vol 9 (3) ◽

pp. 289

Author(s):

Katharina Dötzer ◽

Friederike Schlüter ◽

Franz Edler von Koch ◽

Christine E. Brambs ◽

Sabine Anthuber ◽

...

Keyword(s):

Ovarian Cancer ◽

Growth Factor ◽

Cancer Progression ◽

Survival Data ◽

Cox Regression ◽

Treatment Strategies ◽

Growth Factor Receptor ◽

High Recurrence Rate ◽

Cox Regression Analysis ◽

Primary Ovarian Cancer

Currently, the same first-line chemotherapy is administered to almost all patients suffering from primary ovarian cancer. The high recurrence rate emphasizes the need for precise drug treatment in primary ovarian cancer. Being crucial in ovarian cancer progression and chemotherapeutic resistance, integrins became promising therapeutic targets. To evaluate its prognostic and predictive value, in the present study, the expression of integrin α2β1 was analyzed immunohistochemically and correlated with the survival data and other therapy-relevant biomarkers. The significant correlation of a high α2β1-expression with the estrogen receptor alpha (ERα; p = 0.035) and epithelial growth factor receptor (EGFR; p = 0.027) was observed. In addition, high α2β1-expression was significantly associated with a low number of tumor-infiltrating immune cells (CD3 intratumoral, p = 0.017; CD3 stromal, p = 0.035; PD-1 intratumoral, p = 0.002; PD-1 stromal, p = 0.049) and the lack of PD-L1 expression (p = 0.005). In Kaplan–Meier survival analysis, patients with a high expression of integrin α2β1 revealed a significant shorter progression-free survival (PFS, p = 0.035) and platinum-free interval (PFI, p = 0.034). In the multivariate Cox regression analysis, integrin α2β1 was confirmed as an independent prognostic factor for both PFS (p = 0.021) and PFI (p = 0.020). Dual expression of integrin α2β1 and the hepatocyte growth factor receptor (HGFR; PFS/PFI, p = 0.004) and CD44v6 (PFS, p = 0.000; PFI, p = 0.001; overall survival [OS], p = 0.025) impaired survival. Integrin α2β1 was established as a prognostic and predictive marker in primary ovarian cancer with the potential to stratify patients for chemotherapy and immunotherapy, and to design new targeted treatment strategies.

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Systematic Construction of an Autophagic Risk Model in Bone Marrow for Prognostic Prediction in Multiple Myeloma

Blood ◽

10.1182/blood-2021-150454 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4713-4713

Author(s):

Lingling Shu ◽

Han-Ying Huang ◽

Yang Liu ◽

Yang Li ◽

Weida Wang ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

Regression Analysis ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Conflicts Of Interest ◽

Critical Role ◽

Cox Regression Analysis

Abstract Autophagy is an intracellular self-degradative process that balances cell energy source and regulates tissue homeostasis, which plays critical role in the pathogenesis of multiple myeloma (MM). However, the prognostic role of autophagy-related genes (ARGs) in MM remains undefined. In the present study, the ARGs were obtained from Gene Expression Omnibus datasets (accession GSE24080, GSE136337, GSE57317), which contains 1038 samples of patients with MM. Univariate Cox regression analysis identified 38 ARGs that were significantly associated with overall survival of MM. Furthermore, a risk score model with 11 prognosis-associated ARGs was developed using multivariate Cox regression analysis, including ARNT, ATG4D, BIRC5, BNIP3L, CDKN1A, EIF2S1, IRGM, ITGA3, NCKAP1, NRG1 and TM9SF1. The 3-year area under the curve (AUC) values for the receiver operating characteristic curves were 0.717(0.662, 0.758), 0.646(0.587, 0.703) and 0.906(0.694, 1.000) for GSE24080, GSE136337, GSE57317 prognosis predictions, respectively (Figure A-C). Using this prognostic signature, patients with MM could be separated into high- and low-risk groups with distinct clinical outcomes (Figure D-F). Moreover, autophagy risk score was an independent prognostic factor by multivariate analysis. KEGG revealed that most pathways were related to autophagy and metabolism. Furthermore, we validated the expression of 11 genes and ARNT in bone marrow of MM patients (Figure G-I) and showed the critical role of ARNT-mediated autophagy in the proliferation and drug resistance of bortezomib in myeloma cells (Figure J-M). In conclusion, we constructed ARGs-based prognostic model to predict the prognosis of MM, targeting specific autophagic gene such as ARNT might provide therapeutic clues for MM treatment. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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