Characterization of huntingtin interactomes and their dynamic responses in living cells by proximity proteomics.

Huntingtin (Htt) is a large protein without clearly defined molecular functions. Mutation in this protein causes Huntington's disease (HD), a fatal inherited neurodegenerative disorder. Identification of Htt-interacting proteins by the traditional approaches including yeast two-hybrid systems and affinity purifications has greatly facilitated the understanding of Htt function. However, these methods eliminated the intracellular spatial information of the Htt interactome during sample preparations. Moreover, the temporal changes of the Htt interactome in response to acute cellular stresses cannot be easily resolved with these approaches. Ascorbate peroxidase (APEX2)-based proximity labeling has been used to spatiotemporally investigate protein-protein interactions in living cells. In this study, we generated stable human SH-SY5Y cell lines expressing full-length Htt23Q and Htt145Q with N-terminus tagged Flag-APEX2 to quantitatively map the spatiotemporal changes of Htt interactome to a mild acute proteotoxic stress. Our data revealed that normal and mutant Htt (muHtt) are associated with distinct intracellular microenvironments. Specifically, mutant Htt is preferentially associated with intermediate filaments and myosin complexes. Furthermore, the dynamic changes of Htt interactomes in response to stress are different between normal and mutant Htt. Vimentin is identified as one of the most significant proteins that preferentially interacts with muHtt in situ. Further functional studies demonstrated that mutant Htt affects the vimentin's function of regulating proteostasis in healthy and HD human neural stem cells. Taken together, our data offer important insights into the molecular functions of normal and mutant Htt by providing a list of Htt-interacting proteins in their natural cellular context for further studies in different HD models.

Assessment of brain cancer atlas maps with multimodal imaging features.

BACKGROUND: Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. MAIN TEXT: Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. CONCLUSIONS: The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.

High-dimensional role of AI and machine learning in cancer research.

The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as 'Intelligent Systems' (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients' outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).

Impaired Restoration of Global Protein Synthesis Contributes to Increased Vulnerability to Acute ER Stress Recovery in Huntington's Disease.

Neurons are susceptible to different cellular stresses and this vulnerability has been implicated in the pathogenesis of Huntington's disease (HD). Accumulating evidence suggest that acute or chronic stress, depending on its duration and severity, can cause irreversible cellular damages to HD neurons, which contributes to neurodegeneration. In contrast, how normal and HD neurons respond during the resolution of a cellular stress remain less explored. In this study, we challenged normal and HD cells with a low-level acute ER stress and examined the molecular and cellular responses after stress removal. Using both striatal cell lines and primary neurons, we first showed the temporal activation of p-eIF2α-ATF4-GADD34 pathway in response to the acute ER stress and during recovery between normal and HD cells. HD cells were more vulnerable to cell death during stress recovery and were associated with increased number of apoptotic/necrotic cells and decreased cell proliferation. This is also supported by the Gene Ontology analysis from the RNA-seq data which indicated that "apoptosis-related Biological Processes" were more enriched in HD cells during stress recovery. We further showed that HD cells were defective in restoring global protein synthesis during stress recovery and promoting protein synthesis by an integrated stress response inhibitor, ISRIB, could attenuate cell death in HD cells. Together, these data suggest that normal and HD cells undergo distinct mechanisms of transcriptional reprogramming, leading to different cell fate decisions during the stress recovery.

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing.

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.

Data-driven clinical decision processes: it's time.

Changes and transformations enabled by Big Data have direct effects on Translational Medicine. At one end, superior precision is expected from a more data-intensive and individualized medicine, thus accelerating scientific discovery and innovation (in diagnosis, therapy, disease management etc.). At the other end, the scientific method needs to adapt to the increased diversity that data present, and this can be beneficial because potentially revealing greater details of how a disease manifests and progresses. Patient-focused health data provides augmented complexity too, far beyond the simple need of testing hypotheses or validating models. Clinical decision support systems (CDSS) will increasingly deal with such complexity by developing efficient high-performance algorithms and creating a next generation of inferential tools for clinical use. Additionally, new protocols for sharing digital information and effectively integrating patients data will need to be CDSS-embedded features in view of suitable data harmonization aimed at improved diagnosis, therapy assessment and prevention.

The landscape of BRAF transcript and protein variants in human cancer.

BACKGROUND: The BRAF protein kinase is widely studied as a cancer driver and therapeutic target. However, the regulation of its expression is not completely understood. RESULTS: Taking advantage of the RNA-seq data of more than 4800 patients belonging to 9 different cancer types, we show that BRAF mRNA exists as a pool of 3 isoforms (reference BRAF, BRAF-X1, and BRAF-X2) that differ in the last part of their coding sequences, as well as in the length (BRAF-ref: 76 nt; BRAF-X1 and BRAF-X2: up to 7 kb) and in the sequence of their 3'UTRs. The expression levels of BRAF-ref and BRAF-X1/X2 are inversely correlated, while the most prevalent among the three isoforms varies from cancer type to cancer type. In melanoma cells, the X1 isoform is expressed at the highest level in both therapy-naïve cells and cells with acquired resistance to vemurafenib driven by BRAF gene amplification or expression of the Δ[3-10] splicing variant. In addition to the BRAF-ref protein, the BRAF-X1 protein (the full length as well as the Δ[3-10] variant) is also translated. The expression levels of the BRAF-ref and BRAF-X1 proteins are similar, and together they account for BRAF functional activities. In contrast, the endogenous BRAF-X2 protein is hard to detect because the C-terminal domain is selectively recognized by the ubiquitin-proteasome pathway and targeted for degradation. CONCLUSIONS: By shedding light on the repertoire of BRAF mRNA and protein variants, and on the complex regulation of their expression, our work paves the way to a deeper understanding of a crucially important player in human cancer and to a more informed development of new therapeutic strategies.

Pathway landscapes and epigenetic regulation in breast cancer and melanoma cell lines.

BACKGROUND: Epigenetic variation is a main regulation mechanism of gene expression in various cancer histotypes, and due to its reversibility, the potential impact in therapy can be very relevant. METHODS: Based on a selected pair, breast cancer (BC) and melanoma, we conducted inference analysis in parallel on a few cell lines (MCF-7 for BC and A375 for melanoma). Starting from differential expression after treatment with a demethylating agent, the 5-Aza-2'-deoxycytidine (DAC), we provided pathway enrichment analysis and gene regulatory maps with cross-linked microRNAs and transcription factors. RESULTS: Several oncogenic signaling pathways altered upon DAC treatment were detected with significant enrichment. We represented the association between these cancers by depicting the landscape of common and specific variation affecting them.

Translating Data Science Results into Precision Oncology Decisions: A Mini Review.

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Skin Extracellular Matrix Breakdown Following Paclitaxel Therapy in Patients with Chemotherapy-Induced Peripheral Neuropathy.

The chemotherapeutic agent paclitaxel causes peripheral neuropathy, a dose-limiting side effect, in up to 68% of cancer patients. In this study, we investigated the impact of paclitaxel therapy on the skin of breast cancer patients with chemotherapy-induced peripheral neuropathy (CIPN), building upon previous findings in zebrafish and rodents. Comprehensive assessments, including neurological examinations and quality of life questionnaires, were conducted, followed by intraepidermal nerve fiber (IENF) density evaluations using skin punch biopsies. Additionally, RNA sequencing, immunostaining for Matrix-Metalloproteinase 13 (MMP-13), and transmission electron microscopy provided insights into molecular and ultrastructural changes in this skin. The results showed no significant difference in IENF density between the control and CIPN patients despite the presence of patient-reported CIPN symptoms. Nevertheless, the RNA sequencing and immunostaining on the skin revealed significantly upregulated MMP-13, which is known to play a key role in CIPN caused by paclitaxel therapy. Additionally, various genes involved in the regulation of the extracellular matrix, microtubules, cell cycle, and nervous system were significantly and differentially expressed. An ultrastructural examination of the skin showed changes in collagen and basement membrane structures. These findings highlight the presence of CIPN in the absence of IENF density changes and support the role of skin remodeling as a major contributor to CIPN.

The impact of vitamin D on cancer: A mini review.

In this review, we summarize the most recent advances in vitamin D cancer research to provide molecular clarity, as well as its translational trajectory across the cancer landscape. Vitamin D is well known for its role in regulating mineral homeostasis; however, vitamin D deficiency has also been linked to the development and progression of a number of cancer types. Recent epigenomic, transcriptomic, and proteomic studies have revealed novel vitamin D-mediated biological mechanisms that regulate cancer cell self-renewal, differentiation, proliferation, transformation, and death. Tumor microenvironmental studies have also revealed dynamic relationships between the immune system and vitamin D's anti-neoplastic properties. These findings help to explain the large number of population-based studies that show clinicopathological correlations between circulating vitamin D levels and risk of cancer development and death. The majority of evidence suggests that low circulating vitamin D levels are associated with an increased risk of cancers, whereas supplementation alone or in combination with other chemo/immunotherapeutic drugs may improve clinical outcomes even further. These promising results still necessitate further research and development into novel approaches that target vitamin D signaling and metabolic systems to improve cancer outcomes.

Vitamin D inhibits osteosarcoma by reprogramming nonsense-mediated RNA decay and SNAI2-mediated epithelial-to-mesenchymal transition.

Osteosarcomas are immune-resistant and metastatic as a result of elevated nonsense-mediated RNA decay (NMD), reactive oxygen species (ROS), and epithelial-to-mesenchymal transition (EMT). Although vitamin D has anti-cancer effects, its effectiveness and mechanism of action against osteosarcomas are poorly understood. In this study, we assessed the impact of vitamin D and its receptor (VDR) on NMD-ROS-EMT signaling in in vitro and in vivo osteosarcoma animal models. Initiation of VDR signaling facilitated the enrichment of EMT pathway genes, after which 1,25(OH)2D, the active vitamin D derivative, inhibited the EMT pathway in osteosarcoma subtypes. The ligand-bound VDR directly downregulated the EMT inducer SNAI2, differentiating highly metastatic from low metastatic subtypes and 1,25(OH)2D sensitivity. Moreover, epigenome-wide motif and putative target gene analysis revealed the VDR's integration with NMD tumorigenic and immunogenic pathways. In an autoregulatory manner, 1,25(OH)2D inhibited NMD machinery genes and upregulated NMD target genes implicated in anti-oncogenic activity, immunorecognition, and cell-to-cell adhesion. Dicer substrate siRNA knockdown of SNAI2 revealed superoxide dismutase 2 (SOD2)-mediated antioxidative responses and 1,25(OH)2D sensitization via non-canonical SOD2 nuclear-to-mitochondrial translocalization leading to overall ROS suppression. In a mouse xenograft metastasis model, the therapeutically relevant vitamin D derivative calcipotriol inhibited osteosarcoma metastasis and tumor growth shown for the first time. Our results uncover novel osteosarcoma-inhibiting mechanisms for vitamin D and calcipotriol that may be translated to human patients.

Vitamin D inhibits osteosarcoma by reprogramming nonsense-mediated RNA decay and SNAI2-mediated epithelial-to-mesenchymal transition.

Osteosarcomas are immune-resistant and metastatic as a result of elevated nonsense-mediated RNA decay (NMD), reactive oxygen species (ROS), and epithelial-to-mesenchymal transition (EMT). Although vitamin D has anti-cancer effects, its effectiveness and mechanism of action against osteosarcomas are poorly understood. In this study, we assessed the impact of vitamin D and its receptor (VDR) on the NMD-ROS-EMT signaling axis in in vitro and in vivo osteosarcoma animal models. Initiation of VDR signaling facilitated the enrichment of EMT pathway genes, after which 1,25(OH) 2 D, the active vitamin D derivative, inhibited the EMT pathway in osteosarcoma subtypes. The ligand-bound VDR directly downregulated the EMT inducer SNAI2 , differentiating highly metastatic from low metastatic subtypes and 1,25(OH) 2 D sensitivity. Moreover, epigenome-wide motif and putative target gene analysis revealed the VDR’s integration with NMD tumorigenic and immunogenic pathways. In an autoregulatory manner, 1,25(OH) 2 D inhibited NMD machinery genes and upregulated NMD target genes implicated in anti-oncogenic activity, immunorecognition, and cell-to-cell adhesion. Dicer substrate siRNA knockdown of SNAI2 revealed superoxide dismutase 2 (SOD2)-mediated antioxidative responses and 1,25(OH) 2 D sensitization via non-canonical SOD2 nuclear-to-mitochondrial translocalization leading to overall ROS suppression. In a mouse xenograft metastasis model, the therapeutically relevant vitamin D derivative calcipotriol inhibited osteosarcoma metastasis and tumor growth shown for the first time. Our results uncover novel osteosarcoma-inhibiting mechanisms for vitamin D and calcipotriol that may be translated to human patients.

A signature of five 7-methylguanosine-related genes is a prognostic marker for lung squamous cell carcinoma.

Background: N7-methylguanosine (m7G) is an important posttranscriptional modification affecting mRNA and tRNA functions and stability. The genes regulating the m7G process have been previously found involved in the carcinogenesis process. We aimed to analyze the role of m7G-related genes as potential prognostic markers for lung squamous cell carcinoma (LSCC). Methods: Twenty-nine m7G-related genes were selected for the analysis in the LSCC cohort of the Cancer Genome Atlas (TCGA). Univariate, multivariate, and Kaplan-Meier analyses were used to evaluate the predictive value of risk model developed with m7G signature for overall survival (OS).. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of differentially expressed genes (DEGs) were performed for high- and low-risk LSCC groups. Results: We identified 17 differentially expressed m7G methylation-related genes in LSCC versus normal tissues. The expression of five m7G-related genes (EIF3D, LSM1, NCBP2, NUDT10, and NUDT11) was identified as an independent prognostic marker for OS in LSCC patients. A risk model with these five m7G-related genes predicted 2-, and 3-year survival rates of 0.623 and 0.626, respectively. The risk score significantly correlated with OS: LSCC patients with a higher risk score had shorter OS (P<0.01) and it was associated with lower immune response (P<0.01). Conclusions: We developed a novel m7G-related gene signature that can be of great utility to predict the prognosis for patients with LSCC.

Low Dopamine D2 Receptor Expression Drives Gene Networks Related to GABA, cAMP, Growth and Neuroinflammation in Striatal Indirect Pathway Neurons.

Background: A salient effect of addictive drugs is to hijack the dopamine reward system, an evolutionarily conserved driver of goal-directed behavior and learning. Reduced dopamine type 2 receptor availability in the striatum is an important pathophysiological mechanism for addiction that is both consequential and causal for other molecular, cellular, and neuronal network differences etiologic for this disorder. Here, we sought to identify gene expression changes attributable to innate low expression of the Drd2 gene in the striatum and specific to striatal indirect medium spiny neurons (iMSNs). Methods: Cre-conditional, translating ribosome affinity purification (TRAP) was used to purify and analyze the translatome (ribosome-bound messenger RNA) of iMSNs from mice with low/heterozygous or wild-type Drd2 expression in iMSNs. Complementary electrophysiological recordings and gene expression analysis of postmortem brain tissue from human cocaine users were performed. Results: Innate low expression of Drd2 in iMSNs led to differential expression of genes involved in GABA (gamma-aminobutyric acid) and cAMP (cyclic adenosine monophosphate) signaling, neural growth, lipid metabolism, neural excitability, and inflammation. Creb1 was identified as a likely upstream regulator, among others. In human brain, expression of FXYD2, a modulatory subunit of the Na/K pump, was negatively correlated with DRD2 messenger RNA expression. In iMSN-TRAP-Drd2HET mice, increased Cartpt and reduced S100a10 (p11) expression recapitulated previous observations in cocaine paradigms. Electrophysiology experiments supported a higher GABA tone in iMSN-Drd2HET mice. Conclusions: This study provides strong molecular evidence that, in addiction, inhibition by the indirect pathway is constitutively enhanced through neural growth and increased GABA signaling.

Multiscale characterization of signaling network dynamics through features.

Inference methods applied to biological networks suffer from a main criticism: as the latter reflect associations measured under static conditions, an evaluation of the underlying modular organization can be biologically meaningful only if the dynamics can also be taken into consideration. The same limitation is present in protein interactome networks. Given the substantial uncertainty characterizing protein interactions, we identify at least three aspects that must be considered for inference purposes: 1. Coverage, which for most organisms is only partial; 2. Stochasticity, affecting both the high-throughput experimental and the computational settings from which the interactions are determined, and leading to suboptimal measurement accuracy; 3. Information variety, due to the heterogeneity of technological and biological sources generating the data. Consequently, advances in inference methods require adequate treatment of both system uncertainty and dynamical aspects. Feasible solutions are often made possible by data (omic) integration procedures that complement the experimental design and the computational approaches for network modeling. We present a multiscale stochastic approach to deal with protein interactions involved in a well-known signaling network, and show that based on some topological network features, it is possible to identify timescales (or resolutions) that characterize complex pathways.

Non-Coding RNAs Are Brokers in Breast Cancer Interactome Networks and Add Discrimination Power between Subtypes.

Despite the power of high-throughput genomics, most non-coding RNA (ncRNA) biotypes remain hard to identify, characterize, and validate. This is a clear indication that intensive next-generation sequencing research has led to great efficiency and accuracy in detecting ncRNAs, but not in their functionalization. Computational scientists continue to support the discovery process by spotting significant data features (expression or mutational profiles), elucidating phenotype uncertainty, and delineating complex regulation landscapes for biological pathways and pathophysiological processes. With reference to transcriptome regulation dynamics in cancer, this work introduces a novel network-driven inference approach designed to reveal the potential role of computationally identified ncRNAs in discriminating between breast cancer (BC) subtypes beyond the traditional gene expression signatures. As heterogeneity cast in the subtypes is a characteristic of most cancers, the proposed approach is generalizable beyond BC. Expression profiles of a wide transcriptome spectrum were obtained for a number of BC patients (and controls) listed in TCGA and processed with RNA-Seq. The well-known PAM50 subtype signature was available for the samples and used to move from differentially expressed transcript profiles to subtype-specific biclusters associating gene patterns with patients. Co-expressed gene networks were then generated and annotations were provided, focusing on the biclusters with basal and luminal signatures. These were used to build template maps, i.e., networks in which to embed the ncRNAs and contextually functionalize them based on their interactors. This inference approach is able to assess the influence of ncRNAs at the level of BC subtype. Network topology was considered through the brokerage measure to account for disruptiveness effects induced by the removal of nodes corresponding to ncRNAs. Equivalently, it is shown that ncRNAs can act as brokers of network interactome dynamics, and removing them allows the refinement of subtype-related characteristics previously obtained by gene signatures only. The results of the study elucidate the role of pseudogenes in two major BC subtypes, considering the contextual annotations. Put into a wider perspective, ncRNA brokers may help predictive functionalization studies targeted to new disease phenotypes, for instance those linked to the tumor microenvironment or metabolism, or those specifically involving metastasis. Overall, the approach may represent an in silico prioritization strategy toward the systems identification of new diagnostic and prognostic biomarkers.

Vitamin D Modulation of Mitochondrial Oxidative Metabolism and mTOR Enforces Stress Adaptations and Anticancer Responses.

The relationship between the active form of vitamin D3 (1,25-dihydroxyvitamin D, 1,25(OH)2D) and reactive oxygen species (ROS), two integral signaling molecules of the cell, is poorly understood. This is striking, given that both factors are involved in cancer cell regulation and metabolism. Mitochondria (mt) dysfunction is one of the main drivers of cancer, producing more mitochondria, higher cellular energy, and ROS that can enhance oxidative stress and stress tolerance responses. To study the effects of 1,25(OH)2D on metabolic and mt dysfunction, we used the vitamin D receptor (VDR)-sensitive MG-63 osteosarcoma cell model. Using biochemical approaches, 1,25(OH)2D decreased mt ROS levels, membrane potential (ΔΨmt), biogenesis, and translation, while enforcing endoplasmic reticulum/mitohormetic stress adaptive responses. Using a mitochondria-focused transcriptomic approach, gene set enrichment and pathway analyses show that 1,25(OH)2D lowered mt fusion/fission and oxidative phosphorylation (OXPHOS). By contrast, mitophagy, ROS defense, and epigenetic gene regulation were enhanced after 1,25(OH)2D treatment, as well as key metabolic enzymes that regulate fluxes of substrates for cellular architecture and a shift toward non-oxidative energy metabolism. ATACseq revealed putative oxi-sensitive and tumor-suppressing transcription factors that may regulate important mt functional genes such as the mTORC1 inhibitor, DDIT4/REDD1. DDIT4/REDD1 was predominantly localized to the outer mt membrane in untreated MG-63 cells yet sequestered in the cytoplasm after 1,25(OH)2D and rotenone treatments, suggesting a level of control by membrane depolarization to facilitate its cytoplasmic mTORC1 inhibitory function. The results show that 1,25(OH)2D activates distinct adaptive metabolic responses involving mitochondria to regain redox balance and control the growth of osteosarcoma cells. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

PTS is activated by ATF4 and promotes lung adenocarcinoma development via the Wnt pathway.

Background: The effects and mechanism of 6-pyruvoyl-tetrahydropterin synthase (PTS) on lung adenocarcinoma (LUAD) were studied in LUAD cells and mice with subcutaneously transplanted tumors. Methods: PTS level in tissues and cells was tested by immunohistochemistry, western blot, and quantitative real-time polymerase chain reaction (qRT-PCR). The impacts of PTS on cell viability, proliferation, apoptosis, invasion, and migration were determined by Cell Counting Kit-8 (CCK-8), colony formation assay, flow cytometry, transwell assay, and wound healing assay, respectively. The Cancer Genome Atlas (TCGA) analysis and dual luciferase assay were conducted to predict and verify the relationship between PTS and activating transcription factor 4 (ATF4). A mouse model was established by subcutaneous injection with cancer cells. Tumor volume was calculated as V = ab2/2. Ki67 and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining were used to measure cell proliferation and apoptosis in tumors. Results: PTS was highly expressed in LUAD. Higher PTS level was correlated with late clinical stages and poor survival of patients. Down-regulation of PTS inhibited the viability and proliferation and induced apoptosis of LUAD cells. PTS was activated by ATF4, and up-regulation of ATF4 reversed the inhibitory effect of PTS silencing on LUAD cells. Silencing of PTS inhibited the Wnt pathway. Down-regulation of PTS inhibited tumor growth in mice. Conclusions: PTS was highly expressed in LUAD. PTS was activated by ATF4 and promoted LUAD development via the Wnt pathway.