Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

SNHG16/miR-205/HDAC5 is involved in the progression of renal fibrosis.

Renal interstitial fibrosis (RIF) represents an irreversible and progressive pathological manifestation of chronic renal disease, which ultimately leads to end-stage renal disease. Long noncoding RNAs (lncRNAs) have been suggested to be involved in the progression of RIF. Small nucleolar RNA host gene 16 (SNHG16), a member of lncRNAs, has been found to be involved in the progression of pulmonary fibrosis. This paper first researched the effect of SNHG16 on renal fibrosis. We established a unilateral ureteral obstruction (UUO)-induced mouse RIF model by ligation of the left ureter to evaluate the biological function of SNHG16 in RIF. As a result, SNHG16 was upregulated in UUO-induced renal fibrotic tissues. Knockdown of SNHG16 inhibited RIF and reduced alpha-smooth muscle actin (α-SMA), fibronectin, and college IV expression. miR-205 was a target of SNHG16, and downregulated in UUO-induced renal fibrotic tissues. Inhibition of miR-205 promoted RIF and increased the expression of α-SMA, college IV, and fibronectin. Overexpression of SNHG16 promoted the UUO-induced RIF, but miR-205 abrogated this effect of SNHG16. Histone deacetylase 5 (HDAC5) showed high expression in UUO-induced renal fibrotic tissues. Knockdown of HDAC5 significantly reduced α-SMA, fibronectin, and college IV expression in renal tissues of UUO-induced mice. Inhibition of miR-205 promoted HDAC5 expression, but knockdown of SNHG16 inhibited HDAC5 expression in renal tissues of UUO-induced mice. In conclusion, SHNG16 is highly expressed in renal fibrotic tissues of UUO-induced mice. Knockdown of SHNG16 may prevent UUO-induced RIF by indirectly upregulating HDAC5 via targeting miR-205. SHNG16 may be novel target for treating renal fibrosis.

MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study.

OBJECTIVES: Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. METHODS: We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. RESULTS: The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. CONCLUSIONS: This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status. CLINICAL RELEVANCE STATEMENT: This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism. KEY POINTS: • The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery. • The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning. • With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Complementary Operando Electrochemical Quartz Crystal Microbalance and UV/Vis Spectroscopic Studies: Acetate Effects on Zinc-Manganese Batteries.

Zinc-ion batteries, in which zinc ions and protons do intercalation and de-intercalation during battery cycling with various proposed mechanisms under debate, have been studied. Recently, electrolytic zinc-manganese batteries, exhibiting the pure dissolution-deposition behavior with a large charge capacity, have been accomplished through using electrolytes with Lewis acid. However, the complicated chemical environment and mixed products hinder the investigation though it is crucial to understand the detailed mechanism. Here, cyclic voltammetry coupled electrochemical quartz crystal microbalance (EQCM) and ultraviolet-visible spectrophotometry (UV-Vis) are respectively, for the very first time, used to study the transition from zinc-ion batteries to zinc electrolytic batteries by the continuous addition of acetate ions. These complementary techniques operando trace the mass and the composition evolution. The observed formation and dissolution of zinc hydroxide sulfate (ZHS) and manganese oxides evince the effect of acetate ions on zinc-manganese batteries from an alternative perspective. Both the amount of acetate and the pH value have large impacts on the capacity and Coulombic efficiency of the MnO2 electrode, and thus they should be optimized when constructing a full zinc-manganese battery with high rate capability and reversibility.

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy.

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC). METHODS: This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers. RESULTS: For each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts. CONCLUSIONS: The tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.

Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study.

OBJECTIVES: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. METHODS: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. RESULTS: The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0-65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. CONCLUSIONS: This study demonstrated that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. KEY POINTS: • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.

Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer.

BACKGROUND: One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance. OBJECTIVE: To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. METHODS: Clinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set. RESULTS: A total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC. CONCLUSION: This study developed and validated a pretreatment multiparametric MRI-based radiomic-clinical combined model and showed good performance in predicting endocrine resistance.

Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.

BACKGROUND: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer. METHODS: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558. FINDINGS: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics. FUNDING: No funding.

Clinical Evidence for Locoregional Surgery of the Primary Tumor in Patients with De Novo Stage IV Breast Cancer.

BACKGROUND: Whether primary tumor surgery is better than no surgery in patients with de novo stage IV breast cancer remains controversial. METHODS: This study combined prospective clinical trials and a multicenter cohort to evaluate the impact of locoregional surgery in de novo stage IV breast cancer. The GRADE approach was used to assess the quality of evidence in meta-analysis, and propensity score matching analysis was used in the cohort study. This study was registered with PROSPERO CRD42016043766 and ClinicalTrials.gov NCT04456855. RESULTS: A total of 1110 patients from six trials and 353 patients from the cohort study were included. The meta-analysis showed that compared with no surgery, locoregional surgery did not prolong overall survival (hazard ratio [HR] = 0.90, P = 0.40; moderate-quality) but had a significantly longer locoregional progression-free survival (HR = 0.23, P < 0.001; moderate-quality). The subgroup analysis of solitary bone-only metastasis (HR = 0.47, P = 0.04; high-quality) resulted in prolonged overall survival. In the cohort study, locoregional surgery showed a survival benefit (HR = 0.63, P = 0.041) before matching, but not (HR = 0.84, P = 0.579) after matching. Patients with bone-only metastasis showed a survival advantage in surgery compared with no surgery before matching (HR = 0.36, P = 0.034) as well as after matching (HR = 0.18, P = 0.017). CONCLUSIONS: This study indicated that locoregional surgery had a significantly longer locoregional progression-free survival than no surgery in de novo stage IV breast cancer, and patients with bone-only metastasis tended to show an overall survival benefit from surgery.

Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

Importance: Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking. Objective: To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer. Design, Setting, and Participants: This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020. Exposure: Clinical and DCE-MRI radiomic signatures. Main Outcomes and Measures: The primary end points were ALNM and DFS. Results: This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone. Conclusions and Relevance: This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.

Association of Long Noncoding RNA Biomarkers With Clinical Immune Subtype and Prediction of Immunotherapy Response in Patients With Cancer.

Importance: Long noncoding RNAs (lncRNAs) are involved in innate and adaptive immunity in cancer by mediating the functional state of immunologic cells, pathways, and genes. However, whether lncRNAs are associated with immune molecular classification and clinical outcomes of cancer immunotherapy is largely unknown. Objectives: To explore lncRNA-based immune subtypes associated with survival and response to cancer immunotherapy and to present a novel lncRNA score for immunotherapy prediction using computational algorithms. Design, Setting, and Participants: In this cohort study, an individual patient analysis based on a phase 2, single-arm clinical trial and multicohort was performed from June 25 through September 30, 2019. Data are from the phase 2 IMvigor210 trial and from The Cancer Genome Atlas (TCGA). The study analyzed lncRNA and genomic data of 348 patients with bladder cancer from the IMvigor210 trial and 71 patients with melanoma from TCGA who were treated with immunotherapy. In addition, a pancancer multicohort that included 2951 patients was obtained from TCGA. Main Outcomes and Measures: The primary end point was overall survival (OS). Results: Among 348 patients from the IMvigor210 trial (272 [78.2%] male) and 71 patients with melanoma from TCGA (mean [SD] age, 58.3 [13.4] years; 37 [52.1%] female), 4 distinct classes with statistically significant differences in OS (median months, not reached vs 9.6 vs 8.1 vs 6.7 months; P = .002) were identified. The greatest OS benefit was obtained in the immune-active class, as characterized by the immune-functional lncRNA signature and high CTL infiltration. Patients with low vs high lncRNA scores had statistically significantly longer OS (hazard ratio, 0.32; 95% CI, 0.24-0.42; P < .001) in the IMvigor210 trial and across various cancer types. The lncRNA score was associated with immunotherapeutic OS benefit in the IMvigor210 trial cohort (area under the curve [AUC], 0.79 at 12 months and 0.77 at 20 months) and in TCGA melanoma cohort (AUC, 0.87 at 24 months), superior tumor alteration burden, programmed cell death ligand 1 (PD-L1) expression, and cytotoxic T-lymphocyte (CTL) infiltration. Addition of the lncRNA score to the combination of tumor alteration burden, PD-L1 expression, and CTL infiltration to build a novel multiomics algorithm correlated more strongly with OS in the IMvigor210 trial cohort (AUC, 0.81 at 12 months and 0.80 at 20 months). Conclusions and Relevance: This study identifies novel lncRNA-based immune classes in cancer immunotherapy and recommends immunotherapy for patients in the immune-active class. In addition, the study recommends that the lncRNA score should be integrated into multiomic panels for precision immunotherapy.

Paris polyphylla 26 triggers G2/M phase arrest and induces apoptosis in HepG2 cells via inhibition of the Akt signaling pathway.

OBJECTIVES: Paris polyphylla 26 (PP-26) is a monomer purified from Paris polyphylla, which has traditionally been used as an antimicrobial, hemostatic, and anticancer agent in China. The anti-proliferation effect and underlying molecular mechanism of PP-26 were investigated in vitro. METHODS: The effects of PP-26 on various tumor cells were detected by MTT assay. PP-26-affected cell cycle and cell cycle-related proteins in HepG2 cells were detected by flow cytometry and western blotting, respectively. Apoptosis in response to PP-26 was assessed by Hoechst 33258 staining and flow cytometry. PP-26-affected apoptosis-related proteins and Akt signaling were detected by western blotting. The inhibitory effect of PP-26 on HepG2 cells, when combined with 5-fluorouracil (5-FU), was also assessed. RESULTS: PP-26 inhibited proliferation of HepG2 cells in a dose-dependent manner by triggering G2/M-phase arrest. Moreover, PP-26 induced apoptosis of HepG2 cells. Expression levels of apoptosis proteins caspase 9, caspase 3, PARP, Bcl-2, Bcl-xL, and Mcl-1 were downregulated, while the expression level of apoptosis protein Bax was upregulated. Expression levels of p-Akt, p-GSK-3ß, and p-Foxo3 were downregulated. Combination with PP-26 enhanced 5-FU inhibition of HepG2 cell proliferation. CONCLUSIONS: PP-26 triggers G2/M-phase arrest and induces apoptosis in HepG2 cells via inhibition of the Akt signaling pathway.

Memory T cells skew toward terminal differentiation in the CD8+ T cell population in patients with acute myeloid leukemia.

Stem cell memory T (TSCM) and central memory T (TCM) cells can rapidly differentiate into effector memory (TEM) and terminal effector (TEF) T cells, and have the most potential for immunotherapy. In this study, we found that the frequency of TSCM and TCM cells in the CD8+ population dramatically decreased together with increases in TEM and TEF cells, particularly in younger patients with acute myeloid leukemia (AML) (< 60 years). These alterations persisted in patients who achieved complete remission after chemotherapy. The decrease in TSCM and TCM together with the increase in differentiated TEM and TEF subsets in CD8+ T cells may explain the reduced T cell response and subdued anti-leukemia capacity in AML patients.

Downregulated miR-17, miR-29c, miR-92a and miR-214 may be related to BCL11B overexpression in T cell acute lymphoblastic leukemia.

AIM: BCL11B overexpression is a characteristic of most T cell acute lymphoblastic leukemia (T-ALL) cases, and downregulated BCL11B in leukemic T cells inhibits cell proliferation and induces apoptosis. The purpose of this study was to analyze the miRNA expression pattern that may be related to BCL11B regulation in T-ALL. METHODS: Quantitative real-time PCR was used to detect the miRNAs miR-17-3p, miR-17-5p, miR-29c-3p, miR-92a-3p, miR-214-3p and miR-214-5p, the BCL11B expression level in peripheral blood mononuclear cells which was obtained from 17 de novo and untreated T-ALL patients, and 15 healthy individuals (HIs) served as control. Correlations between the relative miRNA expression levels and BCL11B were analyzed. RESULTS: Based on the computational prediction that certain miRNAs bind the BCL11B 3'-UTR, miR-17-3p, miR-17-5p, miR-29c-3p, miR-92a-3p, miR-214-3p and miR-214-5p were found to be candidates for regulating BCL11B. The expression levels of the six miRNAs were decreased compared with HIs, and with the exception of miR-17-5p, statistically significant differences in expression levels were found in the T-ALL group. Moreover, while significantly higher BCL11B expression was found in the T-ALL group, a negative trend in the correlation level for all six miRNAs could be found in all groups; however, statistical significance was only found for miR-214-3p in the T-ALL group. CONCLUSION: miRNA downregulation together with BCL11B upregulation suggests that miR-17, miR-29c, miR-92a and miR-214 might be involved in BCL11B regulation. The therapeutic promise of regulating the expression of these miRNAs for T-ALL therapy may be considered in the future.