The survival prediction analysis and preliminary study of the biological function of YEATS2 in hepatocellular carcinoma.

Our study aims to develop and validate a novel molecular marker for the prognosis and diagnosis of hepatocellular carcinoma (HCC) MATERIALS & METHODS: We retrospectively analyzed mRNA expression profile and clinicopathological data of HCC patients fetched from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and The International Cancer Genome Consortium (ICGC) datasets. Univariate Cox regression analysis was performed to collect differentially expressed mRNA (DEmRNAs) from HCC and non-tumor tissues, and YEATS2, a prognostic marker, was identified by further analysis. ROC curve, survival analysis and multivariate Cox regression analysis as well as nomograms were used to evaluate the prognosis of this gene. Finally, the biological function of this gene was preliminarily discussed by using single gene Gene Set Enrichment Analysis (GSEA), and the YEATS2 overexpression and knockdown hepatoma cell line was used to verify the results in vitro and in vivo. Based on the clinical information of HCC in TCGA, GEO and ICGC databases, the gene YEATS2 with significant differences from HCC was identified. There was a statistical difference in the survival prognosis between the two databases and the ROC curve showed that the survival of HCC in both TCGA, GSE14520 and ICGC groups had a satisfactory predictive effect. Univariate and multivariate Cox regression analysis showed that YEATS2 was an independent prognostic factor for HCC, and Nomograms, which combined this prognostic feature with significant clinical features, provided an important reference for the clinical prognostic diagnosis of HCC. Next, we constructed overexpression and knockdown YEATS2 cell line in Hep3B and LM3 cells, and further proved that overexpression YEATS2 promote the proliferation and migration of HCC cells by CCK8, colony formation experiment, and transwell assays, and knockdown YEATS2 inhibited the proliferation and migration of HCC cells by CCK8, colony formation experiment, and transwell assays. Finally, the biological function of YEATS2 was preliminarily explored through GSEA analysis of a single gene, and it was found that it was significantly correlated with cell cycle and DNA repair, which provided us with ideas for further analysis. Furthermore, the knockdown of YEATS2 promoted radiation-induced DNA damage, enhanced radiosensitivity, and ultimately inhibited the proliferation of hepatocellular carcinoma cells in vitro and in vivo. Our study identified a promising prognostic marker for hepatocellular carcinoma that is useful for clinical decision-making and individualized treatment.

Long Y ，Wang W ，Liu S ，Wang X ，Tao Y ... -  《-》

Multiomics analysis reveals the involvement of NET1 in tumour immune regulation and malignant progression.

Neuroepithelial cell transforming gene 1 (NET1) is a member of the Ras homologue family member A (RhoA) subfamily of guanine nucleotide exchange factors and a key protein involved in the activation of Rho guanosine triphosphatases, which act as regulators of cell proliferation, cytoskeletal organization, and cell movement and are crucial for cancer spread. Research has shown that NET1 can regulate the malignant biological functions of tumour cells, such as growth, invasion, and metastasis, and it is closely related to the progression of pancreatic cancer, gastric cancer, and liver cancer. However, the comprehensive role and mechanistic function of NET1 in other types of cancer remain largely unexplored. A deeper understanding of the role of NET1 may provide new insights into the molecular mechanisms of cancer progression and metastasis. This study aims to fill this knowledge gap and provide a more comprehensive understanding of the role of NET1 in cancer biology. The Cancer Genome Atlas and Genotype-Tissue Expression databases were utilized to analyse the differential expression of NET1 in normal and cancer tissues. The prognostic value of NET1 in cancer was evaluated through log-rank tests and Cox regression models. Further analysis was conducted to assess the relationships between NET1 expression and clinical features, as well as its diagnostic value. We investigated potential factors contributing to genetic alterations in NET1 to elucidate the role of NET1 in cancer progression. We also explored the relationships between NET1 and genes associated with epigenetic modifications, oncogenes, and tumour characteristics, such as RNA stemness scores (RNAss), DNA stemness scores (DNAss), the tumour mutation burden (TMB), and microsatellite instability (MSI). Additionally, we analysed the associations between NET1 expression and immune cell infiltration, immunoregulatory genes, and sensitivity to therapeutic drugs. We conducted gene set enrichment analysis to further investigate the signalling pathways that might be affected by changes in NET1. The prognostic value of NET1 in triple-negative breast cancer (TNBC) was further validated using real-world and Gene Expression Omnibus (GEO) data. Finally, through both in vivo and in vitro experiments, we confirmed that the overexpression of NET1 contributed to the malignant progression of TNBC cells, and we explored the potential mechanism by which NET1 regulates malignant biological behaviour through cellular experiments. Our study revealed a higher expression level of NET1 in 18 types of tumour tissues than in their corresponding normal tissues. Specifically, we observed high expression of NET1 in LIHC, LUSC, PAAD, and BRCA tumour tissues, which was associated with a poor prognosis. In terms of gene alterations, "amplification", "mutation", and "deep deletion" were identified as the main types of changes occurring in NET1. Among these, "amplification" was predominantly observed in LIHC, LUSC, PAAD, and BRCA. Furthermore, a significant positive correlation was found between copy number variations and the NET1 expression level in various tumours, including LIHC, LUSC, PAAD, and BRCA. We also discovered that NET1 expression was positively correlated with the expression of genes related to epigenetic modification in almost all types of cancer and was related to the expression levels of numerous oncogenes. In certain tumours, a significant positive correlation was noted between the expression of NET1 and TMB, MSI, DNAss, and RNAss. Intriguingly, in most tumours, NET1 expression was strongly negatively correlated with the levels of infiltrating natural killer cells and M1 macrophages. Moreover, NET1 expression was significantly positively correlated with the expression of immune genes in nearly all types of cancer. An analysis of single-cell data revealed that NET1 was expressed primarily in malignant tumour cells in most tumours, with little to no expression in immune cells. Additionally, the expression level of NET1 was associated with sensitivity to various therapeutic drugs. Data from GEO and real-world studies indicated high expression of NET1 in TNBC tissues, which was correlated with a poor prognosis. Cellular experiments indicated that NET1 could regulate the proliferation, invasion, cell cycle, and apoptosis of TNBC cells. Furthermore, NET1 may mediate the malignant proliferation of tumour cells through the AKT signalling pathway. NET1 can serve as a potential prognostic marker for LIHC, LUSC, PAAD, and BRCA tumours. Real-world data further suggest that NET1 can also serve as a prognostic indicator for TNBC. High expression of NET1 may contribute to the malignant proliferation of TNBC cells, potentially through the AKT signalling pathway. Moreover, NET1 may contribute to the formation of an immunosuppressive microenvironment that can promote tumour progression. Therefore, targeting NET1 may represents a promising approach for inhibiting tumour progression.

Pang J ，Huang X ，Gao Y ，Guan X ，Xiong L ，Li L ，Yin N ，Dai M ，Han T ，Yi W ... -  《Scientific Reports》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Impact of residual disease as a prognostic factor for survival in women with advanced epithelial ovarian cancer after primary surgery.

Ovarian cancer is the seventh most common cancer among women and a leading cause of death from gynaecological malignancies. Epithelial ovarian cancer is the most common type, accounting for around 90% of all ovarian cancers. This specific type of ovarian cancer starts in the surface layer covering the ovary or lining of the fallopian tube. Surgery is performed either before chemotherapy (upfront or primary debulking surgery (PDS)) or in the middle of a course of treatment with chemotherapy (neoadjuvant chemotherapy (NACT) and interval debulking surgery (IDS)), with the aim of removing all visible tumour and achieving no macroscopic residual disease (NMRD). The aim of this review is to investigate the prognostic impact of size of residual disease nodules (RD) in women who received upfront or interval cytoreductive surgery for advanced (stage III and IV) epithelial ovarian cancer (EOC). To assess the prognostic impact of residual disease after primary surgery on survival outcomes for advanced (stage III and IV) epithelial ovarian cancer. In separate analyses, primary surgery included both upfront primary debulking surgery (PDS) followed by adjuvant chemotherapy and neoadjuvant chemotherapy followed by interval debulking surgery (IDS). Each residual disease threshold is considered as a separate prognostic factor. We searched CENTRAL (2021, Issue 8), MEDLINE via Ovid (to 30 August 2021) and Embase via Ovid (to 30 August 2021). We included survival data from studies of at least 100 women with advanced EOC after primary surgery. Residual disease was assessed as a prognostic factor in multivariate prognostic models. We excluded studies that reported fewer than 100 women, women with concurrent malignancies or studies that only reported unadjusted results. Women were included into two distinct groups: those who received PDS followed by platinum-based chemotherapy and those who received IDS, analysed separately. We included studies that reported all RD thresholds after surgery, but the main thresholds of interest were microscopic RD (labelled NMRD), RD 0.1 cm to 1 cm (small-volume residual disease (SVRD)) and RD > 1 cm (large-volume residual disease (LVRD)). Two review authors independently abstracted data and assessed risk of bias. Where possible, we synthesised the data in meta-analysis. To assess the adequacy of adjustment factors used in multivariate Cox models, we used the 'adjustment for other prognostic factors' and 'statistical analysis and reporting' domains of the quality in prognosis studies (QUIPS) tool. We also made judgements about the certainty of the evidence for each outcome in the main comparisons, using GRADE. We examined differences between FIGO stages III and IV for different thresholds of RD after primary surgery. We considered factors such as age, grade, length of follow-up, type and experience of surgeon, and type of surgery in the interpretation of any heterogeneity. We also performed sensitivity analyses that distinguished between studies that included NMRD in RD categories of < 1 cm and those that did not. This was applicable to comparisons involving RD < 1 cm with the exception of RD < 1 cm versus NMRD. We evaluated women undergoing PDS and IDS in separate analyses. We found 46 studies reporting multivariate prognostic analyses, including RD as a prognostic factor, which met our inclusion criteria: 22,376 women who underwent PDS and 3697 who underwent IDS, all with varying levels of RD. While we identified a range of different RD thresholds, we mainly report on comparisons that are the focus of a key area of clinical uncertainty (involving NMRD, SVRD and LVRD). The comparison involving any visible disease (RD > 0 cm) and NMRD was also important. SVRD versus NMRD in a PDS setting In PDS studies, most showed an increased risk of death in all RD groups when those with macroscopic RD (MRD) were compared to NMRD. Women who had SVRD after PDS had more than twice the risk of death compared to women with NMRD (hazard ratio (HR) 2.03, 95% confidence interval (CI) 1.80 to 2.29; I2 = 50%; 17 studies; 9404 participants; moderate-certainty). The analysis of progression-free survival found that women who had SVRD after PDS had nearly twice the risk of death compared to women with NMRD (HR 1.88, 95% CI 1.63 to 2.16; I2 = 63%; 10 studies; 6596 participants; moderate-certainty). LVRD versus SVRD in a PDS setting When we compared LVRD versus SVRD following surgery, the estimates were attenuated compared to NMRD comparisons. All analyses showed an overall survival benefit in women who had RD < 1 cm after surgery (HR 1.22, 95% CI 1.13 to 1.32; I2 = 0%; 5 studies; 6000 participants; moderate-certainty). The results were robust to analyses of progression-free survival. SVRD and LVRD versus NMRD in an IDS setting The one study that defined the categories as NMRD, SVRD and LVRD showed that women who had SVRD and LVRD after IDS had more than twice the risk of death compared to women who had NMRD (HR 2.09, 95% CI 1.20 to 3.66; 310 participants; I2 = 56%, and HR 2.23, 95% CI 1.49 to 3.34; 343 participants; I2 = 35%; very low-certainty, for SVRD versus NMRD and LVRD versus NMRD, respectively). LVRD versus SVRD + NMRD in an IDS setting Meta-analysis found that women who had LVRD had a greater risk of death and disease progression compared to women who had either SVRD or NMRD (HR 1.60, 95% CI 1.21 to 2.11; 6 studies; 1572 participants; I2 = 58% for overall survival and HR 1.76, 95% CI 1.23 to 2.52; 1145 participants; I2 = 60% for progression-free survival; very low-certainty). However, this result is biased as in all but one study it was not possible to distinguish NMRD within the < 1 cm thresholds. Only one study separated NMRD from SVRD; all others included NMRD in the SVRD group, which may create bias when comparing with LVRD, making interpretation challenging. MRD versus NMRD in an IDS setting Women who had any amount of MRD after IDS had more than twice the risk of death compared to women with NMRD (HR 2.11, 95% CI 1.35 to 3.29, I2 = 81%; 906 participants; very low-certainty). In a PDS setting, there is moderate-certainty evidence that the amount of RD after primary surgery is a prognostic factor for overall and progression-free survival in women with advanced ovarian cancer. We separated our analysis into three distinct categories for the survival outcome including NMRD, SVRD and LVRD. After IDS, there may be only two categories required, although this is based on very low-certainty evidence, as all but one study included NMRD in the SVRD category. The one study that separated NMRD from SVRD showed no improved survival outcome in the SVRD category, compared to LVRD. Further low-certainty evidence also supported restricting to two categories, where women who had any amount of MRD after IDS had a significantly greater risk of death compared to women with NMRD. Therefore, the evidence presented in this review cannot conclude that using three categories applies in an IDS setting (very low-certainty evidence), as was supported for PDS (which has convincing moderate-certainty evidence).

Bryant A ，Hiu S ，Kunonga PT ，Gajjar K ，Craig D ，Vale L ，Winter-Roach BA ，Elattar A ，Naik R ... -  《Cochrane Database of Systematic Reviews》

High expression of malic enzyme 1 predicts adverse prognosis in patients with cytogenetically normal acute myeloid leukaemia and promotes leukaemogenesis through the IL-6/JAK2/STAT3 pathways.

Progress in improving risk stratification methods for patients with cytogenetically normal acute myeloid leukaemia (CN-AML) remains limited. This study investigates the prognostic significance and potential functional mechanism of malic enzyme 1 (ME1) in CN-AML. Gene expression and clinical data of patients with CN-AML were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, which were subjected to analysis. The prognostic significance of ME1 was assessed through Kaplan-Meier survival analysis, as well as univariate and multivariate Cox regression analyses. A novel risk model based on ME1 expression levels was developed using TCGA data for predicting CN-AML prognosis. Furthermore, the impact of ME1 silencing on AML cell lines was investigated using the Cell Counting Kit-8 assay and flow cytometry. Gene set enrichment analysis (GSEA) analysis and Western blotting were performed to explore the mechanism of ME1 in CN-AML. CN-AML patients expressing higher ME1 levels exhibited shorter event-free survival (EFS) and overall survival (OS) compared to those with lower ME1 expression in the TCGA and multiple GEO datasets (all p < 0.05). Univariate and multivariate Cox regression analyses indicated that ME1 expression served as an independent prognostic factor for the EFS (p = 0.024 in TCGA, p = 0.035 in GSE6891) and OS (p = 0.039 in TCGA, p = 0.008 in GSE6891) in patients with CN-AML. The developed risk model demonstrated that patients with CN-AML in the high-risk group had worse survival than those in the low-risk group (hazard ratio: 2.67, 95% confidence interval: 1.54-4.65, p < 0.001) and exhibited strong predictive accuracy for 1-, 3- and 5-year OS (area under the curve = 0.69, 0.75, 0.79, respectively). ME1 knockdown significantly inhibited proliferation and increased apoptosis in AML cells (all p < 0.05). GSEA and Western blotting demonstrated that ME1 regulates the IL-6/JAK2/STAT3 pathway in CN-AML. Elevated ME1 expression serves as an indicator of poorer prognosis in patients with CN-AML, potentially facilitating leukaemogenesis through the IL-6/JAK2/STAT3 pathway. This suggests that ME1 could be a promising prognostic biomarker and therapeutic target for CN-AML.

Zhang C ，Li W ，Wu F ，Lu Z ，Zeng P ，Luo Z ，Cao Y ，Wen F ，Li J ，Chen X ，Wang F ... -  《-》