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
... -
《-》
Identification and validation of a novel nine-gene prognostic signature of stem cell characteristic in hepatocellular carcinoma.
Currently, cancer stem cells (CSCs) are regarded as the most promising target for cancer therapy due to their close association with tumor resistance, invasion, and recurrence. Thus, identifying CSCs-related genes and constructing a prognostic risk model associated with CSCs may be crucial for hepatocellular carcinoma (HCC) therapy. Xena Browser was used to download gene expression profiles and clinical data, while MSigDB was used to obtain genes associated with CSCs. Firstly, the non-negative matrix factorization (NMF) algorithm was used to cluster the HCC samples based on CSCs-related genes. To evaluate the predictive performance of the risk model, the receiver operating characteristic curves (ROC) and Kaplan-Meier analysis were used. The R package "rms" was used to construct the final nomogram based on risk scores and clinical characteristics. Based on 449 CSCs-related genes, a total of 588 HCC samples from TCGA-LIHC and ICGC-LIRI_JP were classified into four molecular subtypes with marked differences in survival and mRNA stemness index (mRNAsi) between subtypes. Univariate Cox regression, multivariate Cox regression, and LASSO regression analyses were performed on a total of 1417 differentially expressed genes (DEGs) between subtypes, and a nine-gene prognostic model was constructed with TTK, ST6GALNAC4, SPP1, SGCB, MEP1A, HTRA1, CD79A, C6, and ATP2A3. In both the training and testing sets and the external validation cohort, the risk model performed well in predicting HCC patients' survival. A nomogram was constructed and had high predictive efficacy in short-term survival. In comparison with the other two prognostic models, our nine-gene signature model performed best. We constructed a nine-gene signature model to predict the survival of HCC patients, which has good predictive efficacy and stability. The model may contribute to guiding the prognostic assessment of HCC patients in clinical practice.
An Y
，Liu W
，Yang Y
，Chu Z
，Sun J
... -
《-》
Pan-cancer analysis shows that BCAP31 is a potential prognostic and immunotherapeutic biomarker for multiple cancer types.
B-cell receptor-associated protein 31 (BCAP31) is a widely expressed transmembrane protein primarily located in the endoplasmic reticulum (ER), including the ER-mitochondria associated membranes. Emerging evidence suggests that BCAP31 may play a role in cancer development and progression, although its specific effects across different cancer types remain incompletely understood.
The raw data on BCAP31 expression in tumor and adjacent non-tumor (paracancerous) samples were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) and UCSC databases. We also examined the association between BCAP31 expression and clinicopathological factors. Using the Cox proportional hazards model, we found that high BCAP31 levels were linked to poor prognosis. To further explore BCAP31's role, we analyzed the relationship between copy number variations (CNV) and BCAP31 mRNA expression using data from The Cancer Genome Atlas (TCGA). Additionally, the association between BCAP31 expression and signature pathway scores from the MsigDB database provided insights into the tumor biology and immunological characteristics of BCAP31.We assessed the relationship between tumor immune infiltration and BCAP31 expression using the TIMER2 and ImmuCellAI databases. The ESTIMATE computational method was employed to estimate the proportion of immune cells infiltrating the tumors, as well as the stromal and immune components, based on TCGA data. To investigate drug sensitivity in relation to BCAP31 expression, we utilized GDSC2 data, which included responses to 198 medications. We explored the relationship between BCAP31 gene expression and response to immunotherapy. Additionally, the study involved culturing KYSE-150 cells under standard conditions and using siRNA-mediated knockdown of BCAP31 to assess its function. Key experiments included Western blotting (WB) to confirm BCAP31 knockdown, MTT assays for cell proliferation, colony formation assays for growth potential, Transwell assays for migration and invasion, and wound healing assays for motility. Additionally, immunohistochemistry (IHC) was performed on tumor and adjacent normal tissue samples to evaluate BCAP31 expression levels.
BCAP31 was found to be significantly overexpressed in several prevalent malignancies and was associated with poor prognosis. Cox regression analysis across all cancer types revealed that higher BCAP31 levels were predominantly linked to worse overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI). In most malignancies, increased BCAP31 expression was positively correlated with higher CNV. Additionally, BCAP31 expression was strongly associated with the tumor microenvironment (TME), influencing the levels of infiltrating immune cells, immune-related genes, and immune-related pathways. Drug sensitivity analysis identified six medications that showed a significant positive correlation with BCAP31 expression. Furthermore, BCAP31 expression impacted the outcomes and prognosis of cancer patients undergoing immune therapy. The functional assays demonstrated that BCAP31 knockdown in KYSE-150 cells significantly inhibited cell migration, invasion, and proliferation while enhancing colony formation ability. WB and immunohistochemistry analyses confirmed elevated BCAP31 expression in tumor tissues compared to adjacent normal tissues in esophageal cancer, lung adenocarcinoma, and gastric adenocarcinoma.
BCAP31 has the potential to serve as a biomarker for cancer immunology, particularly in relation to immune cell infiltration, and as an indicator of poor prognosis. These findings provide a new perspective that could inform the development of more targeted cancer therapy strategies.
Sun Y
，Li Z
，Liu J
，Xiao Y
，Pan Y
，Lv B
，Wang X
，Lin Z
... -
《Frontiers in Immunology》
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
... -
《-》
ResearchGate
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钛学术
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