Identification of a RNA-seq-based signature to improve prognostics for uterine sarcoma

Integrated gene co-expression network analysis and experimental validation revealed potential targets of human urine extract CDA-II in treating chronic myeloid leukemia

BACKGROUND

Cell differentiation agent II (CDA-II) exhibits potent anti-proliferative and apoptosis-inducing properties against a variety of cancer cells. However, its mechanism of action in chronic myeloid leukemia (CML) remains unclear.

METHODS

Cell counting Kit 8 (CCK-8) and flow cytometry were used to investigate the effects of CDA-II on the biological characteristics of K562 cells. Gene (mRNA and lncRNA) expression profiles were analyzed by bioinformatics to screen differentially expressed genes and to perform enrichment analysis. The Pearson correlation coefficients of lncRNAs and mRNAs were calculated using gene expression values, and a lncRNA/mRNA co-expression network was constructed. The MCODE and cytoHubba plugins were used to analyze the co-expression network.

RESULTS

The Results, derived from CCK-8 and flow cytometry, indicated that CDA-II exerts dual effects on K562 cells: it inhibits their proliferation and induces apoptosis. From bioinformatics analysis, we identified 316 mRNAs and 32 lncRNAs. These mRNAs were predominantly related to the meiotic cell cycle, DNA methylation, transporter complex and peptidase regulator activity, complement and coagulation cascades, protein digestion and absorption, and cell adhesion molecule signaling pathways. The co-expression network comprised of 163 lncRNA/mRNA interaction pairs. Notably, our analysis results implicated clustered histone gene families and five lncRNAs in the biological effects of CDA-II on K562 cells.

CONCLUSION

This study highlights the hub gene and lncRNA/mRNA co-expression network as crucial elements in the context of CDA-II treatment of CML. This insight not only enriches our understanding of CDA-II's mechanism of action but also might provide valuable clues for subsequent experimental studies of CDA-II, and potentially contribute to the discovery of new therapeutic targets for CML.

Fibroblast Growth Factor 23 is a Potential Prognostic Biomarker in Uterine Sarcoma

BACKGROUND

Uterine sarcoma (US) is a highly malignant cancer with poor prognosis and high mortality in women. In this study, we evaluated the expression of human fibroblast growth factor 23 (FGF23) in different US subtypes and the relationship between survival and clinicopathological characteristics.

METHODS

We conducted a comparative analysis of FGF23 gene expression in different pathological types of US. Utilizing a cohort from The Cancer Genome Atlas of 57 patients, a 50-patient microarray dataset (GSE119043) from the Gene Expression Omnibus and a Suining cohort of 44 patients, we analyzed gene expression profiles and corresponding clinicopathological information. Immunohistochemistry was used to examine the expression level of FGF23 in four US subtypes. Survival analysis was used to assess the relationship between FGF23 expression and prognosis in US patients.

RESULTS

Compared with uterine normal smooth muscle and uterine leiomyoma, FGF23 expression was significantly upregulated in US and was differentially expressed in four US subtypes. Uterine carcinosarcoma exhibited the highest expression of FGF23 among the subtypes. Survival analysis revealed no correlation between FGF23 expression and either overall survival or progression-free survival in US (P > 0.05). Similar results were obtained from the validation cohorts. Univariate and multivariate analyses showed no significant correlation between FGF23 expression and the US prognosis. Tumor stage, CA125, and tumor recurrence were independent prognostic factors for survival of US patients.

CONCLUSION

FGF23 was highly expressed in US and was promising as a novel potential biomarker for the diagnosis and prognosis of US.

Comprehensive analysis of TLX2 in pan cancer as a prognostic and immunologic biomarker and validation in ovarian cancer

T cell leukemia homeobox 2 (TLX2) plays an important role in some tumors. Bioinformatics and experimental validation represent a useful way to explore the mechanisms and functions of TLX2 gene in the cancer disease process from a pan cancer perspective. TLX2 was aberrantly expressed in pan cancer and cell lines and correlated with clinical stage. High TLX2 expression was significantly associated with poor overall survival in COAD, KIRC, OC, and UCS. The greatest frequency of TLX2 alterations in pan cancer was amplification. Alterations of NXF2B, MSLNL, PCGF1, INO80B-WBP1, LBX2-AS1, MRPL53, LBX2, TTC31, WDR54, and WBP1 co-occurred in the TLX2 alteration group. PFS was significantly shorter in the TLX2-altered group (n = 6) compared to the TLX2-unaltered group (n = 400). Methylation levels of TLX2 were high in 17 tumors. TLX2 expression was associated with MSI in seven tumors and TMB in five tumors. TLX2 expression was associated with immune infiltration and immune checkpoint genes. TLX2 may be associated with some pathways and chemoresistance. We constructed a possible competing endogenous RNA (ceRNA) network of LINC01010/miR-146a-5p/TLX2 in OC. TLX2 expression was significantly upregulated in ovarian cancer cell lines compared to ovarian epithelial cell lines. Aberrant expression of TLX2 in pan cancer may promote tumorigenesis and progression through different mechanisms. TLX2 may represent an important therapeutic target for human cancers.

Discovering biomarkers for hormone-dependent tumors: in silico study on signaling pathways implicated in cell cycle and cytoskeleton regulation

Malignancies dependent on hormone homeostasis include breast, ovary, cervical, prostate, testis and uterine tumors. Hormones are involved in signal transduction which orchestrate processes, such as apoptosis, proliferation, cell cycle or cytoskeleton organization. Currently, there is a need for novel biomarkers which would help to diagnose cancers efficiently. In this study, the genes implicated in signaling that is important in hormone-sensitive carcinogenesis were investigated regarding their prognostic significance. Data of seven cancer cohorts were collected from FireBrowse. 54 gene sets implicated in specific pathways were browsed through MSig database. Profiling was assessed via Monocle3, while gene ontology through PANTHER. For confirmation, correlation analysis was performed using WGCNA. Protein-protein networks were visualized via Cytoscape and impact of genes on survival, as well as cell cycle or cytoskeleton-related prognostic signatures, was tested. Several differences in expression profile were identified, some of them allowed to distinguish histology. Functional annotation revealed that various regulation of cell cycle, adhesion, migration, apoptosis and angiogenesis underlie these differences. Clinical traits, such as histological type or cancer staging, were found during evaluation of module-trait relationships. Of modules, the TopHubs (COL6A3, TNR, GTF2A1, NKX3-1) interacted directly with, e.g., PDGFB, ITGA10, SP1 or AKT3. Among TopHubs and interacting proteins, many showed an impact on hazard ratio and affected the cell cycle or cytoskeleton-related prognostic signatures, e.g., COL1A1 or PDGFB. In conclusion, this study laid the foundation for further hormone-sensitive carcinogenesis research through identification of genes which prove that crosstalk between cell cycle and cytoskeleton exists, opening avenues for future therapeutic strategies.

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

A novel nomogram for predicting cancer-specific survival in women with uterine sarcoma: a large population-based study

Background

Uterine sarcoma (US) is a rare malignant uterine tumor with aggressive behavior and rapid progression. The purpose of this study was to constructa comprehensive nomogram to predict cancer-specific survival (CSS) of patients with US-based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

A retrospective population-based study was conducted using data from patients with US between 2010 and 2015 from the SEER database. They were randomly divided into a training cohort and a validation cohort ata 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, a nomogram was established to predict patient CSS. The discrimination and calibration of the nomogram were evaluated by the concordance index (C-index) and the area under the curve (AUC). Finally, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.

Results

A total of 3861 patients with US were included in our study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognostic factors. In our nomogram, pathology grade had strongest correlation with CSS, followed by age at diagnosis and surgery status. Compared to the AJCC staging system, the new nomogram showed better predictive discrimination with a higher C-index in the training and validation cohorts (0.796 and 0.767 vs. 0.706 and 0.713, respectively). Furthermore, the AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.

Conclusion

Our study validated the first comprehensive nomogram for US, which could provide more accurate and individualized survival predictions for US patients in clinical practice.

Violations of proportional hazard assumption in Cox regression model of transcriptomic data in TCGA pan-cancer cohorts

Background

Cox proportional hazard regression (CPH) model relies on the proportional hazard (PH) assumption: the hazard of variables is independent of time. CPH has been widely used to identify prognostic markers of the transcriptome. However, the comprehensive investigation on PH assumption in transcriptomic data has lacked.

Results

The whole transcriptomic data of the 9,056 patients from 32 cohorts of The Cancer Genome Atlas and the 3 lung cancer cohorts from Gene Expression Omnibus were collected to construct CPH model for each gene separately for fitting the overall survival. An average of 8.5% gene CPH models violated the PH assumption in TCGA pan-cancer cohorts. In the gene interaction networks, both hub and non-hub genes in CPH models were likely to have non-proportional hazards. Violations of PH assumption for the same gene models were not consistent in 5 non-small cell lung cancer datasets (all kappa coefficients < 0.2), indicating that the non-proportionality of gene CPH models depended on the datasets. Furthermore, the introduction of log(t) or sqrt(t) time-functions into CPH improved the performance of gene models on overall survival fitting in most tumors. The time-dependent CPH changed the significance of log hazard ratio of the 31.9% gene variables.

Conclusions

Our analysis resulted that non-proportional hazards should not be ignored in transcriptomic data. Introducing time interaction term ameliorated performance and interpretability of non-proportional hazards of transcriptome data in CPH.

Comprehensive Analysis of E3 Ubiquitin Ligases Reveals Ring Finger Protein 223 as a Novel Oncogene Activated by KLF4 in Pancreatic Cancer

Pancreatic cancer is one of the major malignancies and causes of mortality worldwide. E3 ubiquitin-protein ligases transfer activated ubiquitin from ubiquitin-conjugating enzymes to protein substrates and confer substrate specificity in cancer. In this study, we first downloaded data from The Cancer Genome Atlas pancreatic adenocarcinoma dataset, acquired all 27 differentially expressed genes (DEGs), and identified genomic alterations. Then, the prognostic significance of DEGs was analyzed, and eight DEGs (MECOM, CBLC, MARCHF4, RNF166, TRIM46, LONRF3, RNF39, and RNF223) and two clinical parameters (pathological N stage and T stage) exhibited prognostic significance. RNF223 showed independent significance as an unfavorable prognostic marker and was chosen for subsequent analysis. Next, the function of RNF223 in the pancreatic cancer cell lines ASPC-1 and PANC-1 was investigated, and RNF223 silencing promoted pancreatic cancer growth and migration. To explore the potential targets and pathways of RNF223 in pancreatic cancer, quantitative proteomics was applied to analyze differentially expressed proteins, and metabolism-related pathways were primarily enriched. Finally, the reason for the elevated expression of RNF223 was analyzed, and KLF4 was shown to contribute to the increased expression of RNF233. In conclusion, this study comprehensively analyzed the clinical significance of E3 ligases. Functional assays revealed that RNF223 promotes cancer by regulating cell metabolism. Finally, the elevated expression of RNF223 was attributed to KLF4-mediated transcriptional activation. This study broadens our knowledge regarding E3 ubiquitin ligases and signal transduction and provides novel markers and therapeutic targets in pancreatic cancer.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6A-related lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high- and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high- and low-risk subgroups were investigated by functional and single-sample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

Gynecological sarcomas: literature review of 2020

PURPOSE OF REVIEW

This article, focus on recently published data of the last 18 months on the management of gynecologic sarcomas.

RECENT FINDINGS

Different tools have been studied to identify the differences between benign from malignant uterine conjonctive tumor.Molecular biology impact more and more on the diagnosis of uterine sarcoma with new definitions of very specific groups. This will make it possible to better define the last group of endometrial sarcoma which has been defined as undifferentiated.In several articles, surgical approaches and fertility-sparing surgery were described including the role of surgery for recurrences.Some other articles have evaluated the potential benefice of adjuvant therapy for uterine sarcoma with early stages.Several new targeted therapies are in development. Notably deoxyribonucleic acid repair machinery in uterine leiomyosarcoma and also immune therapies, transforming growth factor beta pathway, mechanistic target of rapamycin inhibitor, anti angiogenics, etc.

SUMMARY

This last year the potential interest for uterine sarcoma increased, demonstrated by the increasing number of publications in the literature compared to previous years. Despite this greater interest over time, the standard of care for uterine sarcoma does not change and we are always waiting for new innovative therapies able to change routine practice and survival of patients. Currently, the result of different clinical trials, which include new options as targeted molecular approach or immune checkpoint inhibitors are closed to be reported.

Preoperative Blood Inflammatory Markers for the Differentiation of Uterine Leiomyosarcoma from Leiomyoma

Purpose

Preoperative diagnosis of uterine leiomyosarcoma (LMS) is challenging because the disease can mimic benign leiomyoma (LM). The objective of the present study was to investigate the role of preoperative clinical characteristics and hematologic parameters to differentiate uterine LMS and LM.

Methods

Preoperative clinical and laboratory variables were reviewed retrospectively in patients with LMS or LM, and the significances of intergroup differences were assessed. Receiver operating characteristic (ROC) curves were used to determine optimal cut-off values for each variable. Logistic regression analysis was applied to identify variables predicting the presence of LMS.

Results

The preoperative clinical and laboratory variables of 336 patients with uterine tumor were analyzed. Seventy-nine patients had LMS and 257 had LM. A significant difference was observed between LMS and LM in terms of the median value of age at diagnosis, menopausal status, white blood cell (WBC) count, absolute neutrophil count (ANC), C-reactive protein (CRP), lactate dehydrogenase (LDH), and neutrophil-to-lymphocyte ratio (NLR) (all P < 0.001). Multivariate analyses showed that menopausal status (odds ratio [OR] = 3.40, P= 0.002), WBC count (OR = 2.09, P = 0.012), ANC (OR = 3.17, P < 0.001), CRP (OR = 21.74, P < 0.001), LDH (OR = 10.77, P < 0.001), and NLR (OR = 2.58, P = 0.001) predicted the presence of LMS.

Conclusion

Our results suggest that in older or postmenopausal patients, high WBC count, ANC, CRP, LDH, and NLR could be useful biomarkers for the differentiation of LMS and LM, which indicate that serum markers might be useful, cost-effective, and broadly available diagnostic markers for uterine LMS.

Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study)

Simple Summary

Uterine leiomyosarcoma is an aggressive tumor and the current staging system cannot differentiate the patients into different prognostic groups. This leads to difficulty in predicting the patients’ outcomes and planning for adjuvant therapy. We aimed to develop a prediction model that can predict the chance of survival by the third year. In this article, we had used different statistical tests to identify five readily available clinicopathologic parameters to build the prediction model. Internal validation was performed with satisfactory accuracy. Such a prediction model might help to predict survival outcome, and guide future research on the treatment modality.

Abstract

Background: The existing staging systems of uterine leiomyosarcoma (uLMS) cannot classify the patients into four non-overlapping prognostic groups. This study aimed to develop a prediction model to predict the three-year survival status of uLMS. Methods: In total, 201 patients with uLMS who had been treated between June 1993 and January 2014, were analyzed. Potential prognostic indicators were identified by univariate models followed by multivariate analyses. Prediction models were constructed by binomial regression with 3-year survival status as a binary outcome, and the final model was validated by internal cross-validation. Results: Nine potential parameters, including age, log tumor diameter, log mitotic count, cervical involvement, parametrial involvement, lymph node metastasis, distant metastasis, tumor circumscription and lymphovascular space invasion were identified. 110 patients had complete data to build the prediction models. Age, log tumor diameter, log mitotic count, distant metastasis, and circumscription were significantly correlated with the 3-year survival status. The final model with the lowest Akaike’s Information Criterion (117.56) was chosen and the cross validation estimated prediction accuracy was 0.745. Conclusion: We developed a prediction model for uLMS based on five readily available clinicopathologic parameters. This might provide a personalized prediction of the 3-year survival status and guide the use of adjuvant therapy, a cancer surveillance program, and future studies.

Distinct clinical and genetic mutation characteristics in sporadic and Lynch syndrome-associated endometrial cancer in a Chinese population

BACKGROUND

The diagnosis of Lynch syndrome-associated endometrial cancer patients is significant for early warning of their relatives. The purpose of this study was to provide diagnostic indicators of Lynch syndrome-associated endometrial cancer by screening the differential clinical and genetic characteristics.

METHODS

Clinical information and hysterectomy specimens were collected from 377 eligible patients with endometrial cancer. The MLH1 methylation level was detected by an EZ DNA Methylation-Gold Kit. According to the above experimental results, the patients were then divided into sporadic endometrial cancer and suspected Lynch syndrome-associated endometrial cancer groups. A total of 62 samples were randomly selected for whole-exome sequencing. IBM SPSS Statistics 21 was used to compare the clinical data between the sporadic and suspected Lynch syndrome-associated endometrial cancer groups, and the relationship between the specific high-frequency-mutation genes and the clinical data.

RESULTS

According to the results of MMR immunohistochemistry and MLH1 methylation, the sporadic endometrial cancer group included 361 patients and the suspected Lynch syndrome-associated endometrial cancer group included 16 patients in this study. In the clinical analysis, the average age of the suspected Lynch syndrome-associated endometrial cancer patients was 45.50 ± 11.50 years, which was significantly younger than the 51.17 ± 10.03 years of the sporadic endometrial cancer patients (P = 0.028). The average BMI of the suspected Lynch syndrome-associated endometrial cancer patients was 23.43 kg/m² (CI: 20, 30), which was lower than the 26.50 kg/m² of the sporadic endometrial cancer patients (P = 0.028). Combined with the WES data, MASP2, NADK and RNF223 were identified as three specific mutation sites related to age, FIGO stage and histology.

CONCLUSIONS

Compared with the suspected endometrial cancer patients, the Lynch syndrome-associated endometrial cancer patients were younger and less obese. Mutations in MASP2, NADK and RNF223 might be regarded as genetic endometrial cancer features related to clinical characteristics.

Molecular Profiling Associated with Calcium/Calmodulin-Dependent Protein Kinase Kinase 2 (CAMKK2)-Mediated Carcinogenesis in Gastric Cancer

Gastric cancer is the fifth most common cancer and the third leading cause of cancer-related death worldwide. We showed previously that calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2), a serine-threonine kinase, is highly expressed in gastric cancer and leads to progression. In the present study, we identified the molecular networks involved in CAMKK2-mediated progression of gastric adenocarcinoma. Treatment of gastric cancer cell lines with a CAMKK2 inhibitor, STO-609, resulted in decreased cell migration, invasion, and colony-forming ability and a G1/S-phase arrest. In addition, tandem mass tag (TMT)-based quantitative proteomic analysis resulted in the identification of 7609 proteins, of which 219 proteins were found to be overexpressed and 718 downregulated (1.5-fold). Our data identified several key downregulated proteins involved in cell division and cell proliferation, which included DNA replication licensing factors, replication factor C, origin recognition complex, replication protein A and GINS, and mesenchymal markers, upon CAMKK2 inhibition. Immunoblotting and immunofluorescence results showed concordance with our mass spectroscopy data. Taken together, our study supports CAMKK2 as a novel therapeutic target in gastric cancer.

Prospective development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with recurrent/metastatic cancer to immune checkpoint inhibitors

Background

The predictive power of novel biological markers for treatment response to immune checkpoint inhibitors (ICI) is still not satisfactory for the majority of patients with cancer. One should identify valid predictive markers in the peripheral blood, as this is easily available before and during treatment. The current interim analysis of patients of the ST-ICI cohort therefore focuses on the development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with metastatic cancer to ICI targeting the programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) axis.

Methods

A total of 104 patients were prospectively enrolled. 54 immune cell subsets were prospectively analyzed in patients’ peripheral blood by multicolor flow cytometry before treatment with ICI (pre-ICI; n=89), and after the first application of ICI (n=65). Pre-ICI, patients were randomly allocated to a training (n=56) and a validation cohort (n=33). Univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator Cox model were used to create a predictive immune signature, which was also checked after the first ICI, to consider the dynamics of changes in the immune status.

Results

Whole blood samples were provided by 89 patients pre-ICI and by 65 patients after the first ICI. We identified a LIPS which is based on five immune cell subtypes: CD14^high monocytes, CD8+/PD-1⁺ T cells, plasmacytoid dendritic cells, neutrophils, and CD3⁺/CD56⁺/CD16⁺ natural killer (NK)T cells. The signature achieved a high accuracy (C-index 0.74 vs 0.71) for predicting overall survival (OS) benefit in both the training and the validation cohort. In both cohorts, the low-risk group had significantly longer OS than the high-risk group (HR 0.26, 95% CI 0.12 to 0.56, p=0.00025; HR 0.30, 95% CI 0.10 to 0.91, p=0.024, respectively). Regarding the whole cohort, LIPS also predicted progression-free survival (PFS). The identified LIPS was not affected by clinicopathological features with the exception of brain metastases. NKT cells and neutrophils of the LIPS can be used as dynamic predictive biomarkers for OS and PFS after first administration of the ICI.

Conclusion

Our study identified a predictive LIPS for survival of patients with cancer treated with PD-1/PD-L1 ICI, which is based on immune cell subsets in the peripheral whole blood.

Trial registration number

NCT03453892.

Comprehensive analysis of ceRNA network related to lincRNA in glioblastoma and prediction of clinical prognosis

BACKGROUND

Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM).

METHODS

We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model.

RESULTS

A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM.

CONCLUSION

We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

Identification of a survival-related signature for sarcoma patients through integrated transcriptomic and proteomic profiling analyses

Sarcoma (SARC) represents a group of highly histological and molecular heterogeneous rare malignant tumors with poor prognosis. There are few proposed classifiers for predicting patient's outcome. The Cancer Proteome Atlas (TPCA) and The Cancer Genome Atlas (TCGA) databases provide multi-omics datasets that enable a comprehensive investigation for this disease. The proteomic expression profile of SARC patients along with the clinical information was downloaded. 55 proteins were found to be associated with overall survival (OS) of patients using univariate Cox regression analysis. We developed a prognostic risk signature that comprises seven proteins (AMPKALPHA, CHK1, S6, ARID1A, RBM15, ACETYLATUBULINLYS40, and MSH6) with robust predictive performance using multivariate Cox stepwise regression analysis. Additionally, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters. Patients in high-risk group also have worse progression free intervals (PFI) than that of patients in low-risk group, but not for disease free intervals (DFI). The signature was validated using transcriptomic profile of SARC patients from TCGA. Potential mechanisms between high- and low-risk groups were identified using differentially expressed genes (DEGs) analysis. These DEGs were primarily enriched in RAS and MPAK signaling pathways. The signature protein molecules are candidate biomarkers for SARC, and the analysis of computational biology in tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinctly immune landscapes of high- and low-risk patients. Together, we constructed a prognostic signature for predicting outcomes for SARC integrating proteomic and transcriptomic profiles, this might have value in guiding clinical practice.

A Novel Tool to Predict Early Death in Uterine Sarcoma Patients: A Surveillance, Epidemiology, and End Results-Based Study

Background

Uterine sarcoma is a rare gynecologic tumor with a high degree of malignancy. There is a lack of effective prognostic tools to predict early death of uterine sarcoma.

Methods

Data on patients with uterine sarcoma registered between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) data. Important independent prognostic factors were identified by univariate and multivariate logistic regression analyses to construct a nomogram for total early deaths and cancer-specific early deaths.

Results

A total of 5,274 patients with uterine sarcoma were included in this study. Of which, 397 patients experienced early death (≤3 months), and 356 of whom died from cancer-specific causes. A nomogram for total early deaths and cancer-specific early deaths was created using data on age, race, tumor size, the International Federation of Gynecology and Obstetrics (FIGO) staging, histological classification, histological staging, treatment (surgery, radiotherapy, chemotherapy), and brain metastases. On comparing the C-index, area under the curve, and decision curve analysis, the created nomogram showed better predictive power and clinical practicality than one made exclusively with FIGO staging. Calibration of the nomogram by internal validation showed good consistency between the predicted and actual early death.

Conclusions

Nomograms that include clinical characteristics can provide a better prediction of the risk of early death for uterine sarcoma patients than nomograms only comprising the FIGO stage system. In doing so, this tool can help in identifying patients at high risk for early death because of uterine sarcoma.

Identification of Pannexin 2 as a Novel Marker Correlating with Ferroptosis and Malignant Phenotypes of Prostate Cancer Cells

Purpose

Prostate cancer (PCa) is a widespread urinary neoplasm and one of the most prevalent and second most frequent malignancies diagnosed in males worldwide. This study aimed to identify a candidate marker and explore its molecular mechanism in PCa.

Methods

Gene expression datasets, GSE55945 (n=21) and GSE46602 (n=50), were downloaded from the Gene Expression Omnibus database. Bioinformatic approaches were applied to identify potential markers. Effects of the candidate marker on proliferation, migration, invasion, and ferroptosis (ferrous iron and malondialdehyde (MDA)) in PCa cells and its mechanism were assessed after performing cell transfection.

Results

A total of 1435 common differentially expressed genes were identified in GSE55945 and GSE46602. Five key gene modules were listed based on a protein–protein interaction network, containing five hub genes. Pannexin 2 (PANX2), a candidate marker was identified, and findings revealed substantial upregulation of its expression levels in PCa cell lines. Blocking expression of PANX2 resulted in suppression of proliferation, migration, and invasion in PCa cells, while increasing ferrous iron and MDA levels. However, these effects were rescued by Nrf2 activator, oltipraz. The Nrf2 signaling pathway was consequently applied to determine underlying mechanism of PANX2 in PCa cells. We established that silencing PANX2 remarkably reduced protein expression levels in members of Nrf2 signaling pathway (Nrf2, HO-1, and FTH1).

Conclusion

Our study demonstrated that PANX2 is implicated in the pathogenesis of PCa, which regulates malignant phenotypes and ferroptosis through Nrf2 signaling pathway, and maybe a potential therapeutic target for PCa.