Survival benefit of postoperative radioiodine therapy among patients with intermediate-risk differentiated thyroid carcinoma.

BACKGROUND: The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. METHODS: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). RESULTS: Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan-Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44-0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19-12.84]). CONCLUSIONS: RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans.

FTO blocks RNA translational activity via the loss of N6-methyladenosine methylation at 5' UTR regulated by RBM5 in cisplatin-resistant NSCLC.

N6-methyladenosine (m6A) methylation has been widely regarded in numerous biological functions including CR. Nonetheless, the molecular process of m6A methylation behind CR in non-small cell lung cancer (NSCLC) has no apparent significance. We identified in this study that the expression of FTO alpha-ketoglutarate dependent dioxygenase (FTO) was downregulated in CR NSCLC tissues and cells in vivo and in vitro. Additionally, RIP-seq indicated that loss of FTO contributed to the elevated m6A methylation at 5'-untranslated region of RNAs which were closely connected with tumor resistance and malignancy, and FTO exerted to exclude the recruitment of eIF3A to these target genes in CR NSCLC. Moreover, FTO-enriched transcripts displayed a reduced translational capability in CR NSCLC compared to the regular NSCLC cells. Finally, we also identified RNA binding motif protein 5 (RBM5) that could specially interact with FTO in regular NSCLC compared to CR NSCLC. Deficiency of RBM5 resulted in the abnormal recognition of transcripts by FTO, and led to the translation silencing of genes associated with CR such as ATP7A, ERCC1, CD99, CDKN3, XRCC5, and NOL3. Taken together, our data characterized FTO as a novel translation regulator and revealed the molecular mechanism on gene translation through the synergistic effects with RBM5 and m6A methylation in CR NSCLC cells.

IL-21-armored B7H3 CAR-iNKT cells exert potent antitumor effects.

CD1d-restricted invariant NKT (iNKT) cells play a critical role in tumor immunity. However, the scarcity and limited persistence restricts their development and clinical application. Here, we demonstrated that iNKT cells could be efficiently expanded using modified cytokines combination from peripheral blood mononuclear cells. Introduction of IL-21 significantly increased the frequency of CD62L-positive memory-like iNKT cells. iNKT cells armoring with B7H3-targeting second generation CAR and IL-21 showed potent tumor cell killing activity. Moreover, co-expression of IL-21 promoted the activation of Stat3 signaling and reduced the expression of exhaustion markers in CAR-iNKT cells in vitro. Most importantly, IL-21-arming significantly prolonged B7H3 CAR-iNKT cell proliferation and survival in vivo, thus improving their therapeutic efficacy in mouse renal cancer xerograph models without observed cytokine-related adverse events. In summary, these results suggest that B7H3 CAR-iNKT armored with IL-21 is a promising therapeutic strategy for cancer treatment.

AURKA inhibitor-induced PD-L1 upregulation impairs antitumor immune responses.

Introduction: Tumor immunotherapy targeting PD-L1 has emerged as one of the powerful tools for tumor therapy. Numerous studies indicate that tumor-targeted drugs critically have an influence on the interaction between the immune system and tumors by changing the expression of PD-L1, which is beneficial for immunotherapy. Our study provided novel evidence for improving the drug regimen in tumor targeted therapy and immunotherapy. Methods: The expression of PD-L1 on SKBR3, MDA-MB-231, MCF7, 4T1, MC38 and B16 cells was evaluated by flow cytometry after treatment with six preclinical targeted drugs (ARN-509, AZD3514, Galeterone, Neratinib, MLN8237 and LGK974). AURKA was knockdowned by using the specific siRNA or CRISPR-Cas9 technology. In the 4T1-breast tumor and colorectal cancer xenograft tumor models, we determined the number of infiltrated CD3+ and CD8+ T cells in tumor tissues by IHC. Results: We found that AURKA inhibitor MLN8237 promoted the expression of PD-L1 in a time- and concentration-dependent manner while exerted its antitumor effect. Knockdown of AURKA could induce the upregulation of PD-L1 on SKBR3 cells. MLN8237-induced PD-L1 upregulation was mainly associated with the phosphorylation of STAT3. In the 4T1-breast tumor xenograft model, the infiltrated CD3+ and CD8+ T cells decreased after treatment with MLN8237. When treated with MLN8237 in combination with anti-PD-L1 antibody, the volumes of tumor were significantly reduced and accompanied by increasing the infiltration of CD3+ and CD8+ T cells in colorectal cancer xenograft tumor model. Discussion: Our data demonstrated that MLN8237 improved the effect of immunology-related therapy on tumor cells by interacting with anti-PD-L1 antibody, which contributed to producing creative sparks for exploring the possible solutions to overcoming drug resistance to tumor targeted therapy.

Klotho reduces the risk of osteoporosis in postmenopausal women: a cross-sectional study of the National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: Osteoporosis (OP) is one of the diseases that endanger the health of the elderly population. Klotho protein is a hormone with anti-aging effects. A few studies have discussed the relationship between Klotho and OP. However, there is still a lack of research on larger populations. This study aims to evaluate the association between OP and Klotho in American postmenopausal women. METHODS: This is a retrospective study. We searched the National Health and Nutrition Examination Survey (NHANES) database and collected data of 3 survey cycles, finally involving 871 postmenopausal women over 50 years old in the present study. All participants took dual-energy X-ray absorptiometry examination and serum Klotho testing at the time of investigation. After adjusting the possible confounding variables, a multivariate regression model was employed to estimate the relationship between OP and Klotho proteins. Besides, the P for trend and restricted cubic spline (RCS) were applied to examine the threshold effect and calculate the inflection point. RESULTS: Factors influencing the occurrence of OP included age, ethnicity, body mass index and Klotho levels. Multivariate regression analysis indicated that the serum Klotho concentration was lower in OP patients than that in participants without OP (OR[log2Klotho] = 0.568, P = 0.027). The C-index of the prediction model built was 0.765, indicating good prediction performance. After adjusting the above-mentioned four variables, P values for trend showed significant differences between groups. RCSs revealed that when the Klotho concentration reached 824.09 pg/ml, the risk of OP decreased drastically. CONCLUSION: Based on the analysis of the data collected from the NHANES database, we propose a correlation between Klotho and postmenopausal OP. A higher serum Klotho level is related to a lower incidence of OP. The findings of the present study can provide guidance for research on diagnosis and risk assessment of OP.

Difference after radiotherapy observed in patients with nasopharyngeal carcinoma.

To evaluate the shrinkage rate of small cervical lymph nodes (SCLNs) at different levels in patients with nasopharyngeal carcinoma (NPC) treated with radiotherapy retrospectively. 96 adult patients with NPC who underwent intensity-modulated radiotherapy (IMRT) at our institution were analyzed and followed-up. Evaluation of the response (shrinking rate) of SCLNs was determined by the bidimensional tumor area. Binary logistic regression was conducted to explore the risk factors associated with the shrinking rate of SCLNs. Of the 96 patients included in this study, 1,194 SCLNs were identified. Among the SCLNs, 28.6% were level IIb and 21.3% were level IIa. SCLNs at level IIa (96.1%), tended to have a response effect of no change (NC) with shrinking rate <50% (odds ratio [OR]=0.007; 95% CI: 0.003-0.021, P=5.287×10-25). Conversely, the most proportionate share of SCLNs for shrinking rate ≥50% (complete response (CR) or partial response (PR)) was observed at level IIb (67.2%) (OR=6.104; 95% CI: 3.267-11.407, P=1.420×10-8). There was no significant difference of shrinking rate between irradiation doses of 60Gy and 63Gy. Most SCLNs at level IIa were not shrunk after radiotherapy. The irradiation dose of SCLNs at level IIa should be not more than 60Gy to reduce side effects.

GMP development and preclinical validation of CAR-T cells targeting a lytic EBV antigen for therapy of EBV-associated malignancies.

Introduction: Epstein-Barr virus (EBV) is a widely spread pathogen associated with lymphoproliferative diseases, B/ T/ NK cell lymphomas, nasopharyngeal carcinoma (NPC) and gastric carcinoma (GC). EBV lytic reactivations contribute to the genomic instability, inflammation and tumorigenesis of NPC, promoting cancer progression. Patients with NPC refractory to standard therapies show dismal survival. EBV gp350 is an envelope protein detectable in NPC specimens intracellularly and on the cell membrane of malignant cells, and is a potential viral antigen for T cell-directed immunotherapies. The potency of T cells engineered with a chimeric antigen receptor (CAR) targeting gp350 against EBV+ lymphoproliferative disease was previously shown. Methods: Here, we advanced towards preclinical and non-clinical developments of this virus-specific CAR-T cell immunotherapy against NPC. Different gp350CAR designs were inserted into a lentiviral vector (LV) backbone. Results: A construct expressing the scFv 7A1-anti-gp350 incorporating the CD8 transmembrane and CD28.CD3ζ signaling domain (ZT002) was selected. High titer ZT002 (~1x108 TU/ml) was manufactured in HEK 293T/17 suspension cells in serum free media as large-scale production under good manufacturing practices (GMP). A LV multiplicity of infection (MOI) of 1 resulted in high frequencies of functional gp350CAR+ T cells (>70%) at a low (<2) vector copy numbers in the genome. ZT002 was therefore used to establish gp350CAR-T batch run production methods. GMP upscaling and validation of T cell transduction and expansion in several runs resulted in average 3x109 gp350CAR-T cells per batch. >80% CD3+ gp350CAR-T cells bound to purified gp350 protein. In vitro cytotoxicity and cytokine secretion assays (IFN-γ and TNF-α) confirmed the specificity of gp350CAR-T cells against gp350+ NPC, GC and lymphoma cell targets. Immunocompromised B-NDG mice (NOD.CB17-PrkdcscidIl2rgtm1/Bcgen) were challenged s.c. with a EBV+ NPC C666.1 cell line expressing gp350 and then treated with escalating doses of gp350CAR-T cells or with non-transduced T cells. gp350CAR-T cells promoted antitumor responses, bio-distributed in several tissues, infiltrated in tumors and rejected gp350+ tumor cells. Discussion: These results support the use of gp350CAR-T cells generated with ZT002 as an Innovative New Drug to treat patients with solid and liquid EBV-associated malignancies.

Machine learning algorithms are comparable to conventional regression models in predicting distant metastasis of follicular thyroid carcinoma.

OBJECTIVE: Distant metastasis often indicates a poor prognosis, so early screening and diagnosis play a significant role. Our study aims to construct and verify a predictive model based on machine learning (ML) algorithms that can estimate the risk of distant metastasis of newly diagnosed follicular thyroid carcinoma (FTC). DESIGN: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. PATIENTS: A total of 5809 FTC patients were included in the data analysis. Among them, there were 214 (3.68%) cases with distant metastasis. METHOD: Univariate and multivariate logistic regression (LR) analyses were used to determine independent risk factors. Seven commonly used ML algorithms were applied for predictive model construction. We used the area under the receiver-operating characteristic (AUROC) curve to select the best ML algorithm. The optimal model was trained through 10-fold cross-validation and visualized by SHapley Additive exPlanations (SHAP). Finally, we compared it with the traditional LR method. RESULTS: In terms of predicting distant metastasis, the AUROCs of the seven ML algorithms were 0.746-0.836 in the test set. Among them, the Extreme Gradient Boosting (XGBoost) had the best prediction performance, with an AUROC of 0.836 (95% confidence interval [CI]: 0.775-0.897). After 10-fold cross-validation, its predictive power could reach the best [AUROC: 0.855 (95% CI: 0.803-0.906)], which was slightly higher than the classic binary LR model [AUROC: 0.845 (95% CI: 0.818-0.873)]. CONCLUSIONS: The XGBoost approach was comparable to the conventional LR method for predicting the risk of distant metastasis for FTC.

Post-irradiation intratumoral heterogeneity modulates response to immune checkpoint inhibition therapy in a murine melanoma model.

PURPOSE: The underlying mechanism for radiation as a potentiator of immune checkpoint inhibition (ICI) is unclear. We developed a novel murine model to investigate the effects of post-irradiation intratumoral heterogeneity (ITH) on response to ICI. EXPERIMENTAL DESIGN: Parental mouse melanoma B16F10 cells were irradiated in vitro (5Gy x 3 fractions), then an a priori determined number of resulting colonies were implanted in C57BL/6J immunocompetent mice creating syngeneic models of unirradiated (parental) and irradiated tumors with low (irradiated-L) and high (irradiated-H) ITH. Mice were treated with placebo, α-PD-L1, α-CTLA-4 or dual ICI. Murine tumors underwent whole exome sequencing (WES). Clinically correlated paired pre- and post-irradiation patient rectal adenocarcinoma samples underwent WES. RESULTS: Irradiated-L tumors showed increased tumor mutational burden (TMB) and a sustained decrease in ITH. Irradiated-L tumors were predicted to express five neoantigens with high variant allele frequency/clonal distribution. Mice with irradiated-L and irradiated-H versus parental B16F10 tumors demonstrated longer overall survival with dual ICI. Only mice with irradiated-L tumors experienced an overall survival benefit with single agent ICI. Clinically correlated rectal adenocarcinoma samples showed similarly increased TMB and decreased ITH following irradiation. CONCLUSIONS: Post-irradiation ITH modulates ICI response in a murine melanoma model. Irradiation may offer a mechanism to widen the therapeutic window of ICI.

Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.

AIMS/INTRODUCTION: To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. MATERIALS AND METHODS: This is a 3-year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10-fold cross-validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi-layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. RESULTS: A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10-fold cross-validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823-0.886) in the training set and 0.835 (95% CI 0.779-0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814-0.866) in the training set and 0.834 (95% CI 0.785-0.884) in the test set. CONCLUSIONS: In the real-world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value.

Surgical Methods and Social Factors Are Associated With Long-Term Survival in Follicular Thyroid Carcinoma: Construction and Validation of a Prognostic Model Based on Machine Learning Algorithms.

Background: This study aimed to establish and verify an effective machine learning (ML) model to predict the prognosis of follicular thyroid cancer (FTC), and compare it with the eighth edition of the American Joint Committee on Cancer (AJCC) model. Methods: Kaplan-Meier method and Cox regression model were used to analyze the risk factors of cancer-specific survival (CSS). Propensity-score matching (PSM) was used to adjust the confounding factors of different surgeries. Nine different ML algorithms,including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forests (RF), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GaussianNB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP),were used to build prognostic models of FTC.10-fold cross-validation and SHapley Additive exPlanations were used to train and visualize the optimal ML model.The AJCC model was built by multivariate Cox regression and visualized through nomogram. The performance of the XGBoost model and AJCC model was mainly assessed using the area under the receiver operating characteristic (AUROC). Results: Multivariate Cox regression showed that age, surgical methods, marital status, T classification, N classification and M classification were independent risk factors of CSS. Among different surgeries, the prognosis of one-sided thyroid lobectomy plus isthmectomy (LO plus IO) was the best, followed by total thyroidectomy (hazard ratios: One-sided thyroid LO plus IO, 0.086[95% confidence interval (CI),0.025-0.290], P<0.001; total thyroidectomy (TT), 0.490[95%CI,0.295-0.814], P=0.006). PSM analysis proved that one-sided thyroid LO plus IO, TT, and partial thyroidectomy had no significant differences in long-term prognosis. Our study also revealed that married patients had better prognosis than single, widowed and separated patients (hazard ratios: single, 1.686[95%CI,1.146-2.479], P=0.008; widowed, 1.671[95%CI,1.163-2.402], P=0.006; separated, 4.306[95%CI,2.039-9.093], P<0.001). Among different ML algorithms, the XGBoost model had the best performance, followed by Gaussian NB, RF, LR, MLP, LightGBM, AdaBoost, KNN and SVM. In predicting FTC prognosis, the predictive performance of the XGBoost model was relatively better than the AJCC model (AUROC: 0.886 vs. 0.814). Conclusion: For high-risk groups, effective surgical methods and well marital status can improve the prognosis of FTC. Compared with the traditional AJCC model, the XGBoost model has relatively better prediction accuracy and clinical usage.

YTHDF3 Facilitates eIF2AK2 and eIF3A Recruitment on mRNAs to Regulate Translational Processes in Oxaliplatin-Resistant Colorectal Cancer.

Oxaliplatin, as a first-line drug, frequently causes chemo-resistance in colorectal cancer (CRC). The role of N6-methyladenosine (m6A) modification in multiple biological functions has been well studied. However, the molecular mechanisms underlying m6A methylation in modulating anti-cancer drug resistance in CRC remain obscure. In the present study, we found that YTH m6A RNA-binding protein 3 (YTHDF3) was highly expressed in oxaliplatin-resistant (OXAR) CRC tissues and cells. Moreover, we observed that YTHDF3 could recognize the 5' untranslated region of significantly m6A-methylated RNAs, which were associated with tumor resistance and recruit eukaryotic translation initiation factor 3 subunit A (eIF3A) to facilitate the translation of these target genes. Furthermore, we determined that eukaryotic translation initiation factor 2 alpha kinase 2 (eIF2AK2) bridged YTHDF3 and eIF3A, enhancing the stability of the YTHDF3/eIF3A complex in OXAR CRC cells. Taken together, our data identified YTHDF3 as a novel hallmark and revealed the molecular mechanism of YTHDF3 on gene translation via coordination with eIF2AK2 in OXAR CRC cells.

Reassembling of albumin-bound paclitaxel mitigates myelosuppression and improves its antitumoral efficacy via neutrophil-mediated targeting drug delivery.

Albumin-bound paclitaxel (abPTX) has been widely used in cancer treatment. However, dose-related side effects, such as myelosuppression, restrict its clinical application. Cell-based targeting drug delivery is a promising way to mitigate systematic side-effects and improve antitumoral efficacy. In this study, we demonstrated that reassembled abPTX could be engulfed by neutrophils in vivo and delivered to tumor site, thus improving therapeutic efficacy and mitigating myelosuppression. First, in vitro analysis confirmed that reassembling of abPTX formed uniform and stable serum albumin nanoparticles (NP-abPTX) with size of 107.5 ± 2.29 nm and reserved the ability to kill tumor cells. Second, we found that NP-abPTX could be engulfed by activated neutrophil in vitro and in vivo but do not affect neutrophils' function, such as chemotaxis and activation. In a murine tumor model, we further proved that local radiotherapy (RT) induced inflammation activated peripheral neutrophils to capture venous infused NP-abPTX and carry them into tumor tissue. As compared to abPTX, infusion of NP-abPTX dramatically enhanced inhibition of tumor growth treated by local RT and mitigated hematotoxicity. Therefore, our study demonstrated a novel strategy to mitigate side-effects and to improve tumor killing efficacy of abPTX through neutrophil-mediated targeting drug delivery.

The Anti-fibrosis drug Pirfenidone modifies the immunosuppressive tumor microenvironment and prevents the progression of renal cell carcinoma by inhibiting tumor autocrine TGF-ß.

Transforming growth factor-ß (TGF-ß) plays a critical role in regulating cell growth and differentiation. Epithelial to mesenchymal transition (EMT) induced by TGF-ß promotes cancer cell migration, invasion, and proliferation. Pirfenidone (5-methyl-1-phenyl-2(1 H)-pyridone, PFD), an approved drug for treating pulmonary and renal fibrosis, is a potent TGF-ß inhibitor and found reduced incidence of lung cancer and alleviated renal function decline. However, whether PFD plays a role in controlling renal cancer progression is largely unknown. In the present study, we demonstrated that high TGF-ß1 expression was negatively associated with ten-year overall survival of patients with renal cancer. Functionally, blockade of TGF-ß signaling with PFD significantly suppressed the progression of renal cancer in a murine model. Mechanistically, we revealed that PFD significantly decreased the expression and secretion of TGF-ß both in vitro and in vivo tumor mouse model, which further prevented TGF-ß-induced EMT and thus cell proliferation, migration, and invasion. Importantly, the downregulation of TGF-ß upon PFD treatment shaped the immunosuppressive tumor microenvironment by limiting the recruitment of tumor-infiltrating MDSCs. Therefore, our study demonstrated that PFD prevents renal cancer progression by inhibiting TGF-ß production of cancer cells and downstream signaling pathway, which might be presented as a therapeutic adjuvant for renal cancer.

Nanoparticle Delivery of miR-122 Inhibits Colorectal Cancer Liver Metastasis.

Liver metastasis is a leading cause of cancer morbidity and mortality. Thus, there has been strong interest in the development of therapeutics that can effectively prevent liver metastasis. One potential strategy is to utilize molecules that have broad effects on the liver microenvironment, such as miR-122, a liver-specific miRNA that is a key regulator of diverse hepatic functions. Here we report the development of a nanoformulation miR-122 as a therapeutic agent for preventing liver metastasis. We engineered a galactose-targeted lipid calcium phosphate (Gal-LCP) nanoformulation of miR-122. This nanotherapeutic elicited no significant toxicity and delivered miR-122 into hepatocytes with specificity and high efficiency. Across multiple colorectal cancer liver metastasis models, treatment with Gal-LCP miR-122 treatment effectively prevented colorectal cancer liver metastasis and prolonged survival. Mechanistic studies revealed that delivery of miR-122 was associated with downregulation of key genes involved in metastatic and cancer inflammation pathways, including several proinflammatory factors, matrix metalloproteinases, and other extracellular matrix degradation enzymes. Moreover, Gal-LCP miR-122 treatment was associated with an increased CD8+/CD4+ T-cell ratio and decreased immunosuppressive cell infiltration, which makes the liver more conducive to antitumor immune response. Collectively, this work presents a strategy to improve cancer prevention and treatment with nanomedicine-based delivery of miRNA. SIGNIFICANCE: Highly specific and efficient delivery of miRNA to hepatocytes using nanomedicine has therapeutic potential for the prevention and treatment of colorectal cancer liver metastasis.

Protein tyrosine phosphatase non-receptor type 12 (PTPN12), negatively regulated by miR-106a-5p, suppresses the progression of hepatocellular carcinoma.

Protein tyrosine phosphatase non-receptor type 12 (PTPN12) is abnormally expressed in many human cancers. However, its role in hepatocellular carcinoma (HCC) is indeterminate. In this study, immunohistochemistry and Western blot were adopted to detect PTPN12 protein expression in HCC tissues and cell lines. MiR-106a-5p and PTPN12 mRNA expressions were determined by quantitative real-time polymerase chain reaction (qRT-PCR). siRNA was used to knockdown PTPN12 expression in HCC cells, and the multiplication, migration, and invasion of HCC cells were determined by cell counting kit 8 (CCK-8) and Transwell assays. The interaction between PTPN12 and miR-106a-5p was verified by dual-luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay. In the present study, we demonstrated that PTPN12 expression in HCC tissues and cells was significantly decreased, which was associated with the tumor size, TNM stage, and lymph node metastasis of HCC patients. Functionally, knocking down PTPN12 significantly promoted the multiplication, migration, invasion, and epithelial-mesenchymal transition (EMT) of HCC cells. PTPN12 was identified as the direct target of miR-106a-5p, and its expression was negatively modulated by miR-106a-5p. Besides, PTPN12 counteracted the promoting effects of miR-106a-5p on the viability, migration, invasion, and EMT of HCC cells. In conclusion, this study substantiates that PTPN12 inhibits the growth, migration, invasion, and EMT of HCC cells, and miR-106a-5p contributes to its dysregulation in HCC.

Co-immunizing with HMGB1 enhances anti-tumor immunity of B7H3 vaccine in renal carcinoma.

Metastatic renal carcinoma is a kind of tumor with high degree of malignancy, but there are no effective treatment methods and strategies at present. In this study, we designed a folate-grafted PEI600-CyD (H1) nanoparticle-mediated DNA vaccine containing an adjuvant of high mobility group box 1 protein (HMGB1) and a tumor-specific antigen of B7H3 (CD276) for renal carcinoma therapy. Mice bearing subcutaneous human B7H3 (hB7H3)-Renca tumor were immunized with H1-pHMGB1/pB7H3, H1-pB7H3, H1-pHMGB1, or Mock vaccine. Compared to other control groups, the growth of the tumor was significantly inhibited in H1-pHMGB1/pB7H3 vaccine group. The increased proportion and mature of CD11c+ DCs were observed in the spleen of H1-pHMGB1/pB7H3 treated mice. Likewise, HMGB1 promoted B7H3 vaccine to induce tumor-specific CD8+ T cell proliferation and CTL responses. Beyond that, H1-pHMGB1/pB7H3 vaccine strengthened the induction of functional CD8+ T cells. With the depletion of CD8+ T cells, the anti-tumor effect of H1-pHMGB1/pB7H3 also disappeared, indicating that CD8+ T cells are the key factor of the anti-tumor activity of the vaccine. So, to sum up, H1-pHMGB1/pB7H3 vaccine could achieve the desired anti-tumor effect by enhancing the response of tumor-specific functional CD8+ T cell responses. H1 nanoparticle-based vaccines may have great potential and prospect in the treatment of primary solid tumors.

Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population.

OBJECTIVE: To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40 years) based on data mining technology. MATERIALS AND METHODS: This was a 3-year retrospective cohort study, which belonged to the sub-cohort of the Chinese Reaction Study. The research period was from March 2011 to December 2014. A total of 1834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model. RESULTS: The predictive factors included in the prediction model were age, neck circumference, waist-to-height ratio, BMI, triglyceride, impaired fasting glucose, dyslipidemia, osteopenia, smoking history, and strenuous exercise. The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95% CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1 and 100%, the nomogram had a good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870. CONCLUSIONS: This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population. It also helps clinicians to identify groups at high risk of OP early and formulate personalized intervention measures.

Dietary inflammatory index and cardiorenal function in women with diabetes and prediabetes.

BACKGROUND AND AIMS: Previous studies had shed a new light on the importance of multiple inflammatory mechanisms in the pathogenesis of arterial stiffness. The dietary inflammatory index (DII®) is a new tool for estimating the overall inflammatory potential of the diet. The aim of this study is to assess the association of the inflammatory potential of diet with peripheral arterial stiffness and renal function in women with diabetes and prediabetes. METHODS AND RESULTS: This is an observational cross-sectional study. A total of 2644 females aged 45-75 years were included for the study. Dietary intake in the past 12 months was assessed by a validated China National Nutrition and Health Survey 2002 (CNHS2002) food-frequency questionnaire. Energy-adjusted DII (E-DII) scores were calculated from daily dietary information. In a multivariable linear regression analysis adjusted for potential confounders, E-DII was positively associated with brachial ankle pulse wave velocity (baPWV) in participants with diabetes (ß = 12.820; 95% CI = 2.565, 23.076; P = 0.014) and prediabetes (ß = 29.025; 95% CI = 1.110, 56.940; P = 0.042), but not in females with normal glucose homeostasis. In addition, per unit increase of E-DII was significantly associated with lower eGFR (ß = -1.363; 95% CI = -2.335, -0.392; P = 0.006) in patients with diabetes. CONCLUSION: We identified a direct association between E-DII and arterial stiffness, decreasedeGFR in middle-aged and elderly women with diabetes or prediabetes. Future studies are needed to verify and clarify the role of E-DII as an intervention target for cardiorenal complications of chronic hyperglycemia.