Oncogene goosecoid is transcriptionally regulated by E2F1 and correlates with disease progression in prostate cancer.

BACKGROUND: Although some well-established oncogenes are involved in cancer initiation and progression such as prostate cancer (PCa), the long tail of cancer genes remains to be defined. Goosecoid (GSC) has been implicated in cancer development. However, the comprehensive biological role of GSC in pan-cancer, specifically in PCa, remains unexplored. The aim of this study was to investigate the role of GSC in PCa development. METHODS: We performed a systematic bioinformatics exploration of GSC using datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus, German Cancer Research Center, and our in-house cohorts. First, we evaluated the expression of GSC and its association with patient prognosis, and identified GSC-relevant genetic alterations in cancers. Further, we focused on the clinical characterization and prognostic analysis of GSC in PCa. To understand the transcriptional regulation of GSC by E2F transcription factor 1 (E2F1), we performed chromatin immunoprecipitation quantitative polymerase chain reaction (qPCR). Functional experiments were conducted to validate the effect of GSC on the tumor cellular phenotype and sensitivity to trametinib. RESULTS: GSC expression was elevated in various tumors and significantly correlated with patient prognosis. The alterations of GSC contribute to the progression of various tumors especially in PCa. Patients with PCa and high GSC expression exhibited worse progression-free survival and biochemical recurrence outcomes. Further, GSC upregulation in patients with PCa was mostly accompanied with higher Gleason score, advanced tumor stage, lymph node metastasis, and elevated prostate-specific antigen (PSA) levels. Mechanistically, the transcription factor, E2F1, stimulates GSC by binding to its promoter region. Detailed experiments further demonstrated that GSC acted as an oncogene and influenced the response of PCa cells to trametinib treatment. CONCLUSIONS: GSC was highly overexpressed and strongly correlated with patient prognosis in PCa. We found that GSC, regulated by E2F1, acted as an oncogene and impeded the therapeutic efficacy of trametinib in PCa.

Integrating bioinformatic analysis and detailed experiments reveal an EMT-related biomarker for clear cell renal cell carcinoma.

BACKGROUND: Epithelial-mesenchymal transition (EMT) is associated with early recurrence and a poor prognosis in clear cell renal cell carcinoma (ccRCC). Studies have shown that EMT-related genes play an important regulatory role in tumor invasion, metastasis, and drug resistance, but the biological functions of EMT-related genes in ccRCC have not been specifically described. METHODS: The mRNA and clinicopathological data of 532 ccRCC and 72 normal samples were downloaded from The Cancer Genome Atlas as a training set. The gene expression matrix and survival data of 91 and 101 ccRCC samples were obtained from the International Cancer Genome Consortium and the ArrayExpress databases as validation sets, respectively. Univariate Cox analysis was used to identify and cluster prognostic genes, and multivariate Cox was performed to construct a prognostic signature. Moreover, CIBERSORT and CellMiner were used to assess immune cell infiltration and prognostic gene-drug sensitivity of the signature, respectively. Most importantly, we performed detailed experiments to verify the oncogenic function of a significant gene, OLFML2B, in vitro and in vivo. RESULTS: We constructed a prognostic signature including seven genes and divided patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly worse than that of the low-risk group through Kaplan-Meier survival analysis. Interestingly, significant differences were observed in clinical characteristics and immune cell infiltration between the two groups. In addition, a significant correlation was found between the expression of prognostic genes and the sensitivity of tumor cells to chemotherapeutics. Most importantly, OLFML2B was proved to contribute to the proliferation and metastasis of ccRCC through detailed functional experiments in vitro and in vivo, and its prognostic efficacy for ccRCC patients was affirmed. CONCLUSION: We identified the prognostic signature of seven genes based on EMT-related genes as prognostic biomarkers for ccRCC. Besides, OLFML2B was validated as a potential diagnostic and therapeutic target for ccRCC by our detailed experiments.

Targeting Alpha-Ketoglutarate Disruption Overcomes Immunoevasion and Improves PD-1 Blockade Immunotherapy in Renal Cell Carcinoma.

The Warburg effect-related metabolic dysfunction of the tricarboxylic acid (TCA) cycle has emerged as a hallmark of various solid tumors, particularly renal cell carcinoma (RCC). RCC is characterized by high immune infiltration and thus recommended for immunotherapeutic interventions at an advanced stage in clinical guidelines. Nevertheless, limited benefits of immunotherapy have prompted investigations into underlying mechanisms, leading to the proposal of metabolic dysregulation-induced immunoevasion as a crucial contributor. In this study, a significant decrease is found in the abundance of alpha-ketoglutarate (αKG), a crucial intermediate metabolite in the TCA cycle, which is correlated with higher grades and a worse prognosis in clinical RCC samples. Elevated levels of αKG promote major histocompatibility complex-I (MHC-I) antigen processing and presentation, as well as the expression of ß2-microglobulin (B2M). While αKG modulates broad-spectrum demethylation activities of histone, the transcriptional upregulation of B2M is dependent on the demethylation of H3K4me1 in its promoter region. Furthermore, the combination of αKG supplementation and PD-1 blockade leads to improved therapeutic efficacy and prolongs survival in murine models when compared to monotherapy. Overall, the findings elucidate the mechanisms of immune evasion in anti-tumor immunotherapies and suggest a potential combinatorial treatment strategy in RCC.

A novel gene signature related to oxidative stress predicts the prognosis in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is considered to be related to the worse prognosis, which might in part be attributed to the early recurrence and metastasis, compared with other type of kidney cancer. Oxidative stress refers to an imbalance between production of oxidants and antioxidant defense. Accumulative studies have indicated that oxidative stress genes contribute to the tumor invasion, metastasis and drug sensitivity. However, the biological functions of oxidative stress genes in ccRCC remain largely unknown. In this study, we identified 1,399 oxidative stress genes from GeneCards with a relevance score ≥7. Data for analysis were accessed from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database, and were utilized as training set and validation set respectively. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox were employed to construct a prognostic signature in ccRCC. Finally, a prognostic signature including four different oxidative stress genes was constructed from 1,399 genes, and its predictive performance was verified through Kaplan-Meier survival analysis and the receiver operating characteristic (ROC) curve. Interestingly, we found that there was significant correlation between the expression of oxidative stress genes and the immune infiltration and the sensitivity of tumor cells to chemotherapeutics. Moreover, the highest hazard ratio gene urocortin (UCN) was chosen for further study; some necessary vitro experiments proved that the UCN could promote the ability of ccRCC proliferation and migration and contribute to the degree of oxidative stress. In conclusion, it was promising to predict the prognosis of ccRCC through the four oxidative stress genes signature. UCN played oncogenic roles in ccRCC by influencing proliferation and oxidative stress pathway, which was expected to be the novel therapeutic target for ccRCC.

Metabolic classifications of renal cell carcinoma reveal intrinsic connections with clinical and immune characteristics.

BACKGROUND: Kidney cancer undergoes a dramatic metabolic shift and has demonstrated responsiveness to immunotherapeutic intervention. However, metabolic classification and the associations between metabolic alterations and immune infiltration in Renal cell carcinoma still remain elucidative. METHODS: Unsupervised consensus clustering was conducted on the TCGA cohorts for metabolic classification. GESA, mRNAsi, prognosis, clinical features, mutation load, immune infiltration and differentially expressed gene differences among different clusters were compared. The prognosis model and nomograms were constructed based on metabolic gene signatures and verified using external ICGC datasets. Immunohistochemical results from Human Protein Atlas database and Tongji hospital were used to validate gene expression levels in normal tissues and tumor samples. CCK8, apoptosis analysis, qPCR, subcutaneously implanted murine models and flowcytometry analysis were applied to investigate the roles of ACAA2 in tumor progression and anti-tumor immunity. RESULTS: Renal cell carcinoma was classified into 3 metabolic subclusters and the subcluster with low metabolic profiles displayed the poorest prognosis, highest invasiveness and AJCC grade, enhanced immune infiltration but suppressive immunophenotypes. ACAA2, ACAT1, ASRGL1, AKR1B10, ABCC2, ANGPTL4 were identified to construct the 6 gene-signature prognosis model and verified both internally and externally with ICGC cohorts. ACAA2 was demonstrated as a tumor suppressor and was associated with higher immune infiltration and elevated PD-1 expression of CD8+ T cells. CONCLUSIONS: Our research proposed a new metabolic classification method for RCC and revealed intrinsic associations between metabolic phenotypes and immune profiles. The identified gene signatures might serve as key factors bridging tumor metabolism and tumor immunity and warrant further in-depth investigations.

UBC9 deficiency enhances immunostimulatory macrophage activation and subsequent antitumor T cell response in prostate cancer.

The role of tumor-associated macrophages (TAMs), along with the regulatory mechanisms underlying distinct macrophage activation states, remains poorly understood in prostate cancer (PCa). Herein, we report that PCa growth in mice with macrophage-specific Ubc9 deficiency is substantially suppressed compared with that in wild-type littermates, an effect partially ascribed to the augmented CD8+ T cell response. Biochemical and molecular analyses revealed that signal transducer and activator of transcription 4 (STAT4) is a crucial UBC9-mediated SUMOylation target, with lysine residue 350 (K350) as the major modification site. Site-directed mutation of STAT4 (K350R) enhanced its nuclear translocation and stability, thereby facilitating the proinflammatory activation of macrophages. Importantly, administration of the UBC9 inhibitor 2-D08 promoted the antitumor effect of TAMs and increased the expression of PD-1 on CD8+ T cells, supporting a synergistic antitumor efficacy once it combined with the immune checkpoint blockade therapy. Together, our results demonstrate that ablation of UBC9 could reverse the immunosuppressive phenotype of TAMs by promoting STAT4-mediated macrophage activation and macrophage-CD8+ T cell crosstalk, which provides valuable insights to halt the pathogenic process of tumorigenesis.

GAS1RR, an immune-related enhancer RNA, is related to biochemical recurrence-free survival in prostate cancer.

Prostate cancer (PCa) is one of the malignant tumors of urinary system with a high morbidity. Enhancer RNA is a subclass of long non-coding RNA transcribed from active enhancer regions, which plays a critical role in gene transcriptional regulation. However, the role of enhancer RNA (eRNA) in PCa remains extremely mysterious. This study is aimed at exploring key prognostic eRNAs in PCa. First, we downloaded gene expression data and clinical data of 33 cancer types from UCSC Xena platform. Second, we selected reported putative eRNA-target pairs and performed the Kaplan-Meier survival and correlation analysis to determine the crucial eRNAs most related to biochemical recurrence (BCR)-free survival. Third, we explored the clinical characteristics with the key eRNA GAS1 adjacent regulatory RNA (GAS1RR) and performed a computational difference algorithm and the Cox regression analysis. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the underlying mechanisms. Finally, we used the pan-cancer data from The Cancer Genome Atlas (TCGA) and performed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) of 18 pairs of specimens to prove the results we acquired. Among all 2695 putative eRNAs, 6 pairs of eRNA-target genes were prominently related to BCR-free survival. Growth arrest-specific protein 1 (GAS1) was a target gene of GAS1RR (r = 0.86, P < 0.001). Patients with low GAS1RR expression were likely to have unfavorable clinical characteristics. The result of computational Cox regression analysis demonstrated that GAS1RR may predict the prognosis of PCa independently. RT-qPCR results illuminated that GAS1RR and GAS1 were both downregulated in PCa tissues, and they show a strong positive correlation. GO and KEGG analyses revealed biological processes that GAS1RR was mainly associated with. Immune infiltration analysis indicated that GAS1RR expression is correlated with the infiltration level of six kinds of immune cells. Our results suggest that GAS1RR may be clinically useful in the prediction of PCa prognosis. Moreover, it may also be a prognostic predictor and theoretic target with great promise in PCa.

The Investigation on Nosocomial Infection of Acinetobacter baumannii and the Clinical Analysis of Sequential Therapy of Cefoperazone/Sulbactam Sodium for Intracranial Infection.

Background: Intracranial infection is a serious complication after neurosurgery. According to a survey, the incidence of intracranial infection is about 2.2%-2.6%, and patients with severe symptoms may even pose a threat to their life safety. Objective: To explore the risk factors for intracranial infection caused by Acinetobacter baumannii after surgery and the clinical effect of sequential therapy of cefoperazone/sulbactam sodium. Methods: In this study, a retrospective study was used. In this case-control study, 48 cases of intracranial Acinetobacter baumannii infection after neurosurgery in our hospital from January 2016 to December 2021 were selected as the infection group, and 96 patients without intracranial infection after surgery during the same period were selected as the control group to study all kinds of related factors and analyze the risk factors for intracranial Acinetobacter baumannii infection; in addition, in accordance with the therapeutic regimen for anti-infection, the infection group was divided into the tigecycline group (patients with tigecycline therapy in this group) and the combined group (patients with tigecycline combined with cefoperazone/sulbactam sequential therapy), with 24 cases in each group in order to compare the therapeutic effects of the two groups. Results: Logistic regression factor model results show that increasing age of patients, surgical treatment for intracranial tumor or craniocerebral trauma, postoperative drainage time (≥3 days), and postoperative hospital stay (≥10 days) were the risk factors for postoperative intracranial infection of Acinetobacter baumannii in neurosurgical patients (P < 0.05), and postoperative prophylactic antibiotic treatment can reduce the incidence of intracranial infection (P < 0.05). The cerebrospinal fluid nucleated cell count, serum CRP, and serum PCT in the combined group 72 h after treatment were lower than those in the tigecycline group, and the difference was statistically significant (P < 0.05). Compared with the clinical efficacy after 72-hour treatment, the cure rate and effective rate in the combined treatment group were 83.33% and 16.67%, respectively, and those in the tigecycline group were 54.17% and 33.33%, respectively. The invalid interest rate was 12.50%, and the combined treatment group was superior to the tigecycline group (P < 0.05). Conclusion: For patients with craniocerebral surgery, targeted preventive interventions should be carried out for the risk factors that may lead to intracranial Acinetobacter baumannii infection. The clinical effect of tigecycline combined with cefoperazone and sulbactam sodium sequentially in the treatment of intracranial Acinetobacter baumannii infection is better.

Clinical characteristics and risk factors of fatal patients with COVID-19: a retrospective cohort study in Wuhan, China.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has caused a global pandemic, resulting in considerable mortality. The risk factors, clinical treatments, especially comprehensive risk models for COVID-19 death are urgently warranted. METHODS: In this retrospective study, 281 non-survivors and 712 survivors with propensity score matching by age, sex, and comorbidities were enrolled from January 13, 2020 to March 31, 2020. RESULTS: Higher SOFA, qSOFA, APACHE II and SIRS scores, hypoxia, elevated inflammatory cytokines, multi-organ dysfunction, decreased immune cell subsets, and complications were significantly associated with the higher COVID-19 death risk. In addition to traditional predictors for death risk, including APACHE II (AUC = 0.83), SIRS (AUC = 0.75), SOFA (AUC = 0.70) and qSOFA scores (AUC = 0.61), another four prediction models that included immune cells subsets (AUC = 0.90), multiple organ damage biomarkers (AUC = 0.89), complications (AUC = 0.88) and inflammatory-related indexes (AUC = 0.75) were established. Additionally, the predictive accuracy of combining these risk factors (AUC = 0.950) was also significantly higher than that of each risk group alone, which was significant for early clinical management for COVID-19. CONCLUSIONS: The potential risk factors could help to predict the clinical prognosis of COVID-19 patients at an early stage. The combined model might be more suitable for the death risk evaluation of COVID-19.