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Inorganic mixed‐halide CsPbX3‐based perovskite solar cells (PeSCs) are emerging as one of the most promising types of PeSCs on account of their thermostability compared to organic–inorganic hybrid counterparts. However, dissatisfactory device performance and high processing temperature impede their development for viable applications. Herein, a facile route is presented for tuning the energy levels and electrical properties of sol–gel‐derived ZnO electron transport material (ETM) via the doping of a classical alkali metal carbonate Cs2CO3. Compared to bare ZnO, Cs2CO3‐doped ZnO possesses more favorable interface energetics in contact with the CsPbI2Br perovskite layer, which can reduce the ohmic loss to a negligible level. The optimized PeSCs achieve an improved open‐circuit voltage of 1.28 V, together with an increase in fill factor and short‐circuit current. The optimized power conversion efficiencies of 16.42% and 14.82% are realized on rigid glass substrate and flexible plastic substrate, respectively. A high thermostability can be simultaneously obtained via defect passivation at the Cs2CO3‐doped ZnO/CsPbI2Br interface, and 81% of the initial efficiency is retained after aging for 200 h at 85 °C. An all‐inorganic mixed‐halide perovskite solar cell with a power conversion efficiency of 16.42% is realized by using a Cs2CO3‐doped ZnO electron transport layer, which ascribes to the interfacial energy level tuning for reducing ohmic loss at the contact and enlarging the built‐in potential. A high thermostability is simultaneously obtained via surface defect passivation for improving the CsPbI2Br film against phase transformation.

Inorganic mixed‐halide CsPbX 3 ‐based perovskite solar cells (PeSCs) are emerging as one of the most promising types of PeSCs on account of their thermostability compared to organic–inorganic hybrid counterparts. However, dissatisfactory device performance and high processing temperature impede their development for viable applications. Herein, a facile route is presented for tuning the energy levels and electrical properties of sol–gel‐derived ZnO electron transport material (ETM) via the doping of a classical alkali metal carbonate Cs 2 CO 3 . Compared to bare ZnO, Cs 2 CO 3 ‐doped ZnO possesses more favorable interface energetics in contact with the CsPbI 2 Br perovskite layer, which can reduce the ohmic loss to a negligible level. The optimized PeSCs achieve an improved open‐circuit voltage of 1.28 V, together with an increase in fill factor and short‐circuit current. The optimized power conversion efficiencies of 16.42% and 14.82% are realized on rigid glass substrate and flexible plastic substrate, respectively. A high thermostability can be simultaneously obtained via defect passivation at the Cs 2 CO 3 ‐doped ZnO/CsPbI 2 Br interface, and 81% of the initial efficiency is retained after aging for 200 h at 85 °C.

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer that rarely develops de novo in primary tumors and is commonly acquired during the development of treatment resistance. NEPC is characterized by gain of neuroendocrine markers and loss of androgen receptor (AR), making it resistant to current therapeutic strategies targeting the AR signaling axis. Here, we report that MCM2, MCM3, MCM4, and MCM6 (MCM2/3/4/6) are elevated in human NEPC and high levels of MCM2/3/4/6 are associated with liver metastasis and poor survival in prostate cancer patients. MCM2/3/4/6 are four out of six proteins that form a core DNA helicase (MCM2-7) responsible for unwinding DNA forks during DNA replication. Inhibition of MCM2-7 by treatment with ciprofloxacin inhibits NEPC cell proliferation and migration in vitro, significantly delays NEPC tumor xenograft growth, and partially reverses the neuroendocrine phenotype in vivo. Our study reveals the clinical relevance of MCM2/3/4/6 proteins in NEPC and suggests that inhibition of MCM2-7 may represent a new therapeutic strategy for NEPC.

Resistance to androgen deprivation therapy, or castration-resistant prostate cancer (CRPC), is often accompanied by metastasis and is currently the ultimate cause of prostate cancer-associated deaths in men. Recently, secondary hormonal therapies have led to an increase of neuroendocrine prostate cancer (NEPC), a highly aggressive variant of CRPC. Here, we identify that high levels of cell surface receptor Trop2 are predictive of recurrence of localized prostate cancer. Moreover, Trop2 is significantly elevated in CRPC and NEPC, drives prostate cancer growth, and induces neuroendocrine phenotype. Overexpression of Trop2 induces tumor growth and metastasis while loss of Trop2 suppresses these abilities in vivo. Trop2-driven NEPC displays a significant up-regulation of PARP1, and PARP inhibitors significantly delay tumor growth and metastatic colonization and reverse neuroendocrine features in Trop2-driven NEPC. Our findings establish Trop2 as a driver and therapeutic target for metastatic prostate cancer with neuroendocrine phenotype and suggest that high Trop2 levels could identify cancers that are sensitive to Trop2-targeting therapies and PARP1 inhibition.

Prostate cancer remains the most common non-cutaneous malignancy among men in the United States. To discover potential serum-based biomarkers for high-risk prostate cancer, we performed a high-multiplex immunoassay utilizing patient-matched pre-operative and post-operative serum samples from ten men with high-grade and high-volume prostate cancer. Our study identified six (CASP8, MSLN, FGFBP1, ICOSLG, TIE2 and S100A4) out of 174 proteins that were significantly decreased after radical prostatectomy. High levels of CASP8 were detected in pre-operative serum samples when compared to post-operative serum samples and serum samples from patients with benign prostate hyperplasia (BPH). By immunohistochemistry, CASP8 protein was expressed at higher levels in prostate cancer tissues compared to non-cancerous and BPH tissues. Likewise, CASP8 mRNA expression was significantly upregulated in prostate cancer when compared to benign prostate tissues in four independent clinical datasets. In addition, mRNA levels of CASP8 were higher in patients with recurrent prostate cancer when compared to patients with non-recurrent prostate cancer and high expression of CASP8 was associated with worse disease-free survival and overall survival in renal cancer. Together, our results suggest that CASP8 may potentially serve as a biomarker for high-risk prostate cancer and possibly renal cancer.

Distinguishing clinically significant from indolent prostate cancer (PC) is a major clinical challenge. We utilised targeted protein biomarker discovery approach to identify biomarkers specific for pro-metastatic PC. Serum samples from the cancer-free group; Cambridge Prognostic Group 1 (CPG1, low risk); CPG5 (high risk) and metastatic disease were analysed using Olink Proteomics panels. Tissue validation was performed by immunohistochemistry in a radical prostatectomy cohort ( n  = 234). We discovered that nine proteins (pleiotrophin (PTN), MK, PVRL4, EPHA2, TFPI-2, hK11, SYND1, ANGPT2, and hK14) were elevated in metastatic PC patients when compared to other groups. PTN levels were increased in serum from men with CPG5 compared to benign and CPG1. High tissue PTN level was an independent predictor of biochemical recurrence and metastatic progression in low- and intermediate-grade disease. These findings suggest that PTN may represent a novel biomarker for the presence of poor prognosis local disease with the potential to metastasise warranting further investigation.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide due to metastasis. Paraspeckle component 1 (PSPC1) upregulation has been identified as an HCC pro-metastatic activator associated with poor patient prognosis, but with a lack of targeting strategy. Here, we report that PSPC1, a nuclear substrate of PTK6, sequesters PTK6 in the nucleus and loses its metastasis driving capability. Conversely, PSPC1 upregulation or PSPC1-Y523F mutation promotes epithelial-mesenchymal transition, stemness, and metastasis via cytoplasmic translocation of active PTK6 and nuclear translocation of β-catenin, which interacts with PSPC1 to augment Wnt3a autocrine signaling. The aberrant nucleocytoplasmic shuttling of active PTK6/β-catenin is reversed by expressing the PSPC1 C-terminal interacting domain (PSPC1-CT131), thereby suppressing PSPC1/PTK6/β-catenin-activated metastasis to prolong the survival of HCC orthotopic mice. Thus, PSPC1 is the contextual determinant of the oncogenic switch of PTK6/β-catenin subcellular localizations, and PSPC1-CT131 functions as a dual inhibitor of PSPC1 and PTK6 with potential for improving cancer therapy. PSPC1 has a critical role in promoting EMT and metastasis. Here, the authors demonstrate that PSPC1 is the contextual determinant of the oncogenic switch of PTK6/β-catenin subcellular localizations to drive metastasis of hepatocellular carcinoma cells via a PSPC1/PTK6/β-catenin signaling.

Breast cancer remains the second most lethal cancer among women in the United States and triple-negative breast cancer is the most aggressive subtype with limited treatment options. Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. In this study, we demonstrate that Trop2 is overexpressed in triple-negative breast cancer (TNBC), and downregulation of Trop2 delays TNBC cell and tumor growth supporting the oncogenic role of Trop2 in breast cancer. Through proteomic profiling, we discovered a metabolic signature comprised of TALDO1, GPI, LDHA, SHMT2, and ADK proteins that were downregulated in Trop2-depleted breast cancer tumors. The identified oncogene-mediated metabolic gene signature is significantly upregulated in TNBC patients across multiple RNA-expression clinical datasets. Our study further reveals that the metabolic gene signature reliably predicts poor survival of breast cancer patients with early stages of the disease. Taken together, our study identified a new five-gene metabolic signature as an accurate predictor of breast cancer outcome.

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Recent self-supervised contrastive learning methods are powerful and efficient for robust representation learning, pulling semantic features from different cropping views of the same image while pushing other features away from other images in the embedding vector space. However, model training for contrastive learning is quite inefficient. In the high-dimensional vector space of the images, images can differ from each other in many ways. We address this problem with heuristic attention pixel-level contrastive loss for representation learning (HAPiCLR), a self-supervised joint embedding contrastive framework that operates at the pixel level and makes use of heuristic mask information. HAPiCLR leverages pixel-level information from the object’s contextual representation instead of identifying pair-wise differences in instance-level representations. Thus, HAPiCLR enhances contrastive learning objectives without requiring large batch sizes, memory banks, or queues, thereby reducing the memory footprint and the processing needed for large datasets. Furthermore, HAPiCLR loss combined with other contrastive objectives such as SimCLR or MoCo loss produces considerable performance boosts on all downstream tasks, including image classification, object detection, and instance segmentation.