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Accurately solving the Schrödinger equation for intricate systems remains a prominent challenge in physical sciences. Here we present QiankunNet, a neural network quantum state (NNQS) framework that combines Transformer architectures with efficient autoregressive sampling to solve the many-electron Schrödinger equation. At its core is a Transformer-based wave function ansatz that captures complex quantum correlations through attention mechanisms, effectively learning the structure of many-body states. The quantum state sampling employs layer-wise Monte Carlo tree search (MCTS) that naturally enforces electron number conservation while exploring orbital configurations. The framework incorporates physics-informed initialization using truncated configuration interaction solutions, providing principled starting points for variational optimization. Our systematic benchmarks demonstrate QiankunNet’s versatility across different chemical systems. For molecular systems up to 30 spin orbitals, we achieved correlation energies reaching 99.9% of the full configuration interaction (FCI) benchmark, setting a new standard for neural network quantum states. Most notably, in treating the Fenton reaction mechanism, a fundamental process in biological oxidative stress, QiankunNet successfully handled a large CAS(46e,26o) active space, enabling accurate description of the complex electronic structure evolution during Fe(II) to Fe(III) oxidation. Accurately solving the Schrödinger equation is challenging. Here, authors present QiankunNet, a Transformer-based framework that efficiently captures quantum correlations, achieving high accuracy in complex molecular systems using neural network quantum states.

Background Hypoxic tumors are refractory to DNA damage drugs. However, the underlying mechanism has yet to be elucidated. We aimed to identify lncRNAs that upregulated under hypoxia and their effects on colorectal cancer (CRC). Methods CRC cells were treated with 1% O 2 to identify lncRNAs that upregulated under hypoxia. We integrated these lncRNAs with RNA-seq of 4 paired CRC tissues and TCGA data to get candidate lncRNAs. Multiple in vitro and in vivo assays were used to explore the role of LUCAT1 in CRC. Results We identified a hypoxia-induced lncRNA LUCAT1 that facilitated the growth of CRC cells and contributed to drug resistance of CRC cells both in vitro and in vivo. Mechanically, LUCAT1 interacts with polypyrimidine tract binding protein 1 (PTBP1) in CRC cells, facilitates the association of a set of DNA damage related genes with PTBP1, thus resulting in altered alternative splicing of these genes. Moreover, ectopic expression of PTBP1 in CRC cells with knockdown of LUCAT1 abrogated the effects induced by LUCAT1 knockdown. Chemotherapeutics drug combined with LUCAT1 knockdown via antisense oligonucleotides (ASO) would get a better outcome in vivo, compared with group treated with chemotherapeutic drug only. Notably, LUCAT1 is upregulated in CRC tissues, compared to adjacent normal tissues; and CRC patients with higher LUCAT1 have a worse prognosis and poorly responded to chemotherapy in the clinic. Conclusions Our data suggested CRC cells utilizes LUCAT1 to develop resistance to DNA damage drugs, and disrupting the LUCAT1/PTBP1 axis might be a promising therapeutic strategy for refractory hypoxic tumors.

Epigenetic alteration is one of the hallmarks of colorectal cancer (CRC). Many driver genes are regulated by DNA methylation in CRC. However, the role of DNA methylation regulating lncRNAs remain elusive. Here, we identify that SNHG11 (small nucleolar RNA host gene 11) is upregulated by promotor hypomethylation in CRC and is associated with poor prognosis in CRC patients. SNHG11 can promote CRC cell migration and metastasis under hypoxia. Interestingly, the DNA-binding motif of SNHG11 is similar to that of HIF-1α. In addition, SNHG11-associated genes are enriched with members of the HIF-1 signaling pathway in CRC. Mechanistically, SNHG11 binds to the pVHLrecognition sites on HIF-1α, thus blocking the interaction of pVHL with HIF-1α and preventing its ubiquitination and degradation. Moreover, SNHG11 upregulates the expression of HIF-1α target genes, i.e., AK4, ENO1, HK2, and Twist1. Notably, SNHG11 can bind to the HRE sites in the promoter of these genes and increase their transcription. In summary, these results identify a SNHG11/ HIF-1α axis that plays a pivotal role in tumor invasion and metastasis.

Despite the rapidly identified numbers of lncRNA in humans, exploration of the molecular mechanisms of lncRNA is lagging, because the molecular mechanisms of lncRNA can be various and complex in different conditions. In this study, we found a new molecular mechanism for a versatile molecule, MIR22HG. MIR22HG is an lncRNA that contributes to the initiation and progression of many human cancers, including hepatocellular carcinoma (HCC). We report that MIR22HG was downregulated in 120 HCC samples compared with adjacent nontumor liver tissues. More interestingly, decreased expression of MIR22HG in HCC could predict poor prognosis of HCC patients. Knockdown of MIR22HG promoted the growth, migration and invasion of HCC cells. In exploring the molecular mechanism of MIR22HG, we found that MIR22HG functioned as a tumor suppressor in hepatocellular carcinomas, in part through serving as a competing endogenous RNA to modulate the miRNA‐10a‐5p level. Moreover, NCOR2 was verified to act as the downstream target gene of MIR22HG/miR‐10a‐5p. In addition, the MIR22HG/miRNA‐10a‐5p/NCOR2 axis inhibited the activation of the Wnt/β‐catenin pathway. Together, our results demonstrated that MIR22HG inhibited HCC progression in part through the miR‐10a‐5p/NCOR2 signaling axis and might act as a new prognostic biomarker for HCC patients. A schematic model depicting the molecular mechanism of MIR22HG in hepatocellular carcinoma.

Transcribed RNAs undergo various regulation and modification to become functional transcripts. Notably, cancer transcriptome has not been fully characterized at transcript resolution. Herein, we carry out a reference-based transcript assembly across >1000 cancer cell lines. We identify 498,255 transcripts, approximately half of which are unannotated. Unannotated transcripts are closely associated with cancer-related hallmarks and show clinical significance. We build a high-confidence RNA binding protein (RBP)-transcript regulatory network, wherein most RBPs tend to regulate transcripts involved in cell proliferation. We identify numerous transcripts that are highly associated with anti-cancer drug sensitivity. Furthermore, we establish RBP-transcript-drug axes, wherein PTBP1 is experimentally validated to affect the sensitivity to decitabine by regulating KIAA1522-a6 transcript. Finally, we establish a user-friendly data portal to serve as a valuable resource for understanding cancer transcriptome diversity and its potential clinical utility at transcript level. Our study substantially extends cancer RNA repository and will facilitate anti-cancer drug discovery. Modification of transcribed mRNAs enables regulation of transcription but its extent in cancer cells is incompletely understood. Here, the authors analyse transcript assembly in over 1000 cancer cell lines and find unannotated transcripts are common, and are associated with drug sensitivity.

Background Poly (ADP-ribose) polymerase inhibitor (PARPi) resistance poses a significant challenge in ovarian carcinoma (OC). While the role of DOT1L in cancer and chemoresistance is acknowledged, its specific role in PARPi resistance remains unclear. This study aims to elucidate the molecular mechanism of DOT1L in PARPi resistance in OC patients. Methods This study analyzed the expression of DOT1L in PARPi-resistant cell lines compared to sensitive ones and correlated it with clinical outcomes in OC patients. Comprehensive in vitro and in vivo functional experiments were conducted using cellular and mouse models. Molecular investigations, including RNA sequencing, chromatin immunoprecipitation (ChIP) and Cleavage Under Targets and Tagmentation (CUT&Tag) assays, were employed to unravel the molecular mechanisms of DOT1L-mediated PARPi resistance. Results Our investigation revealed a robust correlation between DOT1L expression and clinical PARPi resistance in non-BRCA mutated OC cells. Upregulated DOT1L expression in PARPi-resistant tissues was associated with diminished survival in OC patients. Mechanistically, we identified that PARP1 directly binds to the DOT1L gene promoter, promoting transcription independently of its enzyme activity. PARP1 trapping induced by PARPi treatment amplified this binding, enhancing DOT1L transcription and contributing to drug resistance. Sequencing analysis revealed that DOT1L plays a crucial role in the transcriptional regulation of PLCG2 and ABCB1 via H3K79me2. This established the PARP1-DOT1L-PLCG2/ABCB1 axis as a key contributor to PARPi resistance. Furthermore, we discovered that combining a DOT1L inhibitor with PARPi demonstrated a synergistic effect in both cell line-derived xenograft mouse models (CDXs) and patient-derived organoids (PDOs). Conclusions Our results demonstrate that DOT1L is an independent prognostic marker for OC patients. The PARP1-DOT1L/H3K79me2-PLCG2/ABCB1 axis is identified as a pivotal contributor to PARPi resistance. Targeted inhibition of DOT1L emerges as a promising therapeutic strategy for enhancing PARPi treatment outcomes in OC patients.

Inflammatory skin diseases are induced by disorders of the host defense system of the skin, which is composed of a barrier, innate and acquired immunity, as well as the cutaneous microbiome. These disorders are characterized by recurrent cutaneous lesions and intense itch, which seriously affecting life quality of people across all ages and ethnicities. To elucidate molecular factors for typical inflammatory skin diseases (such as psoriasis and atopic dermatitis), transcriptomic profiling assays have been largely performed. Additionally, single-cell RNA sequencing (scRNA-seq) as well as spatial transcriptomic profiling have revealed multiple potential translational targets and offered guides to improve diagnosis and treatment strategies for inflammatory skin diseases. High-throughput transcriptomics data has shown unprecedented power to disclose the complex pathophysiology of inflammatory skin diseases. Here, we will summarize discoveries from transcriptomics data and discuss how to maximize the transcriptomics data to propel the development of diagnostic biomarkers and therapeutic targets in inflammatory skin diseases.

The ubiquitin-specific protease (USP) family is a major member of the deubiquitinating enzyme family that plays important and diverse roles in multiple tumors. The roles and mechanisms of action of USP family members in ovarian cancer are not well understood. This study aimed to screen all the USP family members and explored the specific function of USP43 in ovarian cancer. The expression levels of USP family members in ovarian cancer were screened using bioinformatics analysis, and the specific function of USP43 was explored through in vitro and in vivo experiments. Functional assays, including cell viability, ferroptosis, and tumor xenograft models, were employed. In short, USP43 drives the ferroptosis suppression by activating the expression of SLC7A11 through FASN-HIF1α pathway. USP43 is an important prognostic factor for ovarian cancer, with its overexpression promoting ovarian cancer progression and its knockdown inhibiting it. Mechanistically, USP43, which is transcriptionally activated by YY1, stabilizes FASN through deubiquitination, and FASN activates SLC7A11 expression by stabilizing HIF1α. Furthermore, the combination of cisplatin and the SLC7A11 inhibitor HG106 significantly inhibits the growth of ovarian tumors. Thus, targeting the USP43-FASN-HIF1α-SLC7A11 axis can inhibit ferroptosis and promote platinum sensitivity in ovarian cancer.

PTEN is a crucial tumor suppressor and loss of PTEN protein is involved in various cancers. However, the detailed molecular mechanisms of PTEN loss in cancers remain elusive, especially the involvement of lncRNAs. Here, lncRNA RP11-295G20.2 is found to be significantly upregulated in hepatocellular carcinoma (HCC) and promotes the growth of liver cancer cells both in vitro and in vivo. Furthermore, RP11-295G20.2 inhibits autophagy in liver cancer cells. Interestingly, RP11-295G20.2 directly binds to the PTEN protein and leads to its degradation. RP11-295G20.2 expression is inversely correlated with PTEN protein expression in 82 TCGA/TCPA-LIHC samples. Surprisingly, RP11-295G20.2-induced PTEN degradation occurs through the lysosomal pathway instead of the proteasome pathway. RP11-295G20.2 binds to the N terminus of PTEN and facilitates the interaction of p62 with PTEN. Thus, PTEN is translocated into lysosomes and degraded. RP11-295G20.2 also influences AKT phosphorylation and forkhead box O 3a (FOXO3a) translocation into the nucleus, in turn regulating the transcription of autophagy-related genes. Collectively, RP11-295G20.2 directly binds to PTEN and enables its lysosomal degradation. This newly identified RP11-295G20.2/PTEN axis reveals an unexplored molecular mechanism regarding PTEN loss in liver cancer and might provide new therapeutic benefits for liver cancer patients.

This paper investigates the modal parameter identification technique utilizing a drop impact load. A 12-DOF mathematical model of a Railway Vehicle System (RWVS) was constructed, followed by theoretical calculations, simulation analysis, and tests with four different drop impact loads. The responses to these loads were inspected through FFT, and the modal frequencies of the RWVS were determined by the Peak Picking (P-P) method. The simulation demonstrated that four types of drop impact loads can activate the bounce, pitch, and roll modes of the RWVS. The theoretical calculation and simulation analysis revealed that the maximum error of modal frequency identification is no greater than 6.5%. The experimental results verified that the drop impact excitation method can be used to identify the modal parameters of a complex railway vehicle system, which is highly beneficial for the design and verification of the vehicle structure.