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Abstract Hafnium oxide (HfO2)‐based devices have been extensively evaluated for high‐speed and low‐power memory applications. Here, the influence of aluminum (Al) and lanthanum (La) co‐doping HfO2 thin films on the ferroelectric characteristics of hafnium‐based devices is investigated. Among devices with different La/Al ratios, the Al and La co‐doped hafnium oxide (HfAlAO) device with 4.2% Al and 2.17% La exhibited the excellent remanent polarization and thermostability. Meanwhile, first principal analyses verified that hafnium‐based thin films with 4.2% Al and 2.17% La promoted the formation of the o‐phase against the paraelectric phase, providing theoretical support for supporting experimental results. Furthermore, a vertical ferroelectric HfO2 memory based on 3D macaroni architecture is reported. The devices show excellent ferroelectric characteristics of 22 µC cm−2 under 4.5 MV cm−1 and minimal coercive field of ≈1.6 V. In addition, the devices exhibit great memory performance, including the response speed of device can achieve 20 ns and endurance characteristic can achieve 1010 cycles.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy and is unresponsive to conventional therapeutic modalities due to its high heterogeneity, expounding the necessity, and priority of searching for effective biomarkers and drugs. Autophagy, as an evolutionarily conserved biological process, is upregulated in PDAC and its regulation is linked to a poor prognosis. Increased autophagy sequestered MHC‐I on PDAC cells and weaken the antigen presentation and antitumor immune response, indicating the potential therapeutic strategies of autophagy inhibitors. Methods By performing 10 state‐of‐the‐art multi‐omics clustering algorithms, we constructed a robust PDAC classification model to reveal the autophagy‐related genes among different subgroups. Outcomes After building a more comprehensive regulating network for potential autophagy regulators exploration, we concluded the top 20 autophagy‐related hub genes (GAPDH, MAPK3, RHEB, SQSTM1, EIF2S1, RAB5A, CTSD, MAP1LC3B, RAB7A, RAB11A, FADD, CFKN2A, HSP90AB1, VEGFA, RELA, DDIT3, HSPA5, BCL2L1, BAG3, and ERBB2), six miRNAs, five transcription factors, and five immune infiltrated cells as biomarkers. The drug sensitivity database was screened based on the biomarkers to predict possible drug‐targeting signal pathways, hoping to yield novel insights, and promote the progress of the anticancer therapeutic strategy. Conclusion We succefully constructed an autophagy‐related mRNA/miRNA/TF/Immune cells network based on a 10 state‐of art algorithm multi‐omics analysis, and screened the drug sensitivity dataset for detecting potential signal pathway which might be possible autophagy modulators' targets.

Abstract Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor with different genetic and molecular alterations. Schemes for ccRCC classification system based on multiomics are urgent, to promote further biological insights. Two hundred and fifty‐five ccRCC patients with paired data of clinical information, transcriptome expression profiles, copy number alterations, DNA methylation, and somatic mutations were collected for identification. Bioinformatic analyses were performed based on our team's recently developed R package “MOVICS.” With 10 state‐of‐the‐art algorithms, we identified the multiomics subtypes (MoSs) for ccRCC patients. MoS1 is an immune exhausted subtype, presented the poorest prognosis, and might be caused by an exhausted immune microenvironment, activated hypoxia features, but can benefit from PI3K/AKT inhibitors. MoS2 is an immune “cold” subtype, which represented more mutation of VHL and PBRM1, favorable prognosis, and is more suitable for sunitinib therapy. MoS3 is the immune “hot” subtype, and can benefit from the anti‐PD‐1 immunotherapy. We successfully verified the different molecular features of the three MoSs in external cohorts GSE22541, GSE40435, and GSE53573. Patients that received Nivolumab therapy helped us to confirm that MoS3 is suitable for anti‐PD‐1 therapy. E‐MTAB‐3267 cohort also supported the fact that MoS2 patients can respond more to sunitinib treatment. We also confirm that SETD2 is a tumor suppressor in ccRCC, along with the decreased SETD2 protein level in advanced tumor stage, and knock‐down of SETD2 leads to the promotion of cell proliferation, migration, and invasion. In summary, we provide novel insights into ccRCC molecular subtypes based on robust clustering algorithms via multiomics data, and encourage future precise treatment of ccRCC patients.

Abstract Background The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients. Methods A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS. Conclusions We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

Abstract Accumulating evidence shows HOOK1 disordered in human malignancies. However, the clinicopathological and biological significance of HOOK1 in renal cell carcinoma (RCC) remains rarely studied. In this study, the authors demonstrate that HOOK1 is downregulated in RCC samples with predicted poorer clinical prognosis. Mechanistically, HOOK1 inhibits tumor growth and metastasis via canonical TGF‐β/ALK5/p‐Smad3 and non‐canonical TGF‐β/MEK/ERK/c‐Myc pathway. At the same time, HOOK1 inhibits RCC angiogenesis and sunitinib resistance by promoting degradation of TNFSF13B through the ubiquitin‐proteasome pathway. In addition, HOOK1 is transcriptionally regulated by nuclear factor E2F3 in VHL dependent manner. Notably, an agonist of HOOK1, meletin, is screened and it shows antitumor activity more effectively when combined with sunitinib or nivolumab than it is used alone. The findings reveal a pivotal role of HOOK1 in anti‐cancer treatment, and identify a novel therapeutic strategy for renal cell carcinoma.

The heterogeneity of the immune microenvironment leads to different responses in immune checkpoint blockade therapy. We aimed to propose a robust molecular classification system to investigate the relevance of the immune microenvironment subtype and prognosis of prostate cancer patients, as well as the therapeutic response to immune checkpoint blockade therapy. A total of 1,557 prostate cancer patients were enrolled, including 69 real‐world samples from our institute (titled the AHMU‐PC cohort). The non‐negative matrix factorization algorithm was employed to virtually microdissect patients. The immune enrichment was characterized by a high enrichment of T cell‐, B cell‐, NK cell‐, and macrophage‐associated signatures, by which patients were subclassified into nonimmune and immune classes. Subsequently, the immune class was dichotomized into immune‐activated and immune‐suppressed subtypes based on the stromal signature, represented by the activation of WNT/TGF‐β, TGF‐β1, and C‐ECM signatures. Approximately 14.9% to 24.3% of patients belonged to the immune‐activated subtype, which was associated with favorable recurrence‐free survival outcomes. In addition, patients in the immune‐activated subtype were predicted to benefit more from anti‐PD‐1/PD‐L1 therapy. In conclusion, our study identifies a novel immune molecular classifier that is closely related to clinical prognosis and provides novel insights into immunotherapeutic strategies for prostate cancer patients.

Abstract Prostate cancer (PCa), a severe health burden for males, accounts for the second frequent cancer and fifth tumor specific death cancer around the world. Several studies on tumor‐infiltrating immune cells (TIICs) have shown inconsistent and controversial results to PCa. We downloaded a gene expression matrix and clinical information from TCGA, and CIBERSORT was used to identify the proportion of TIICs. Wilcoxon's Sign Rank Test evaluated different gene expression levels in PCa and normal tissues. Kaplan‐Meier curves were used to evaluate the associations of TIICs and recurrence‐free survival (RFS). Finally, based on the preset P‐value of .05, the distribution of TIICs in 73 PCa tissues and 11 normal tissues was illustrated. Activated CD4+ T cells and M0 macrophages account for a high proportion in PCa tissues, while neutrophils and monocytes were found at a high density in normal tissues. Further results showed that the density of plasma cells, Treg cells and resting mast cells were associated with advanced PCa. Additionally, M2 macrophages affected the RFS of PCa patients, and AR was also involved. In the current study, we first evaluated the immune infiltration among PCa and revealed that M2 macrophages could predict the prognosis of PCa patients. Meanwhile, based on the LASSO regression analysis, we established a novel nomogram to assess the recurrence risk of PCa based on immune cell proportions and clinical features.