Biochemical characterization and metabolic reprogramming of amino acids in Soybean roots under drought stress.

Amino acids play important roles in stress resistance, plant growth, development, and quality, with roots serving as the primary organs for drought response. We conducted biochemical and multi-omics analyses to investigate the metabolic processes of root amino acids in drought-resistant (HN44) and drought-sensitive (HN65) soybean (Glycine max) varieties. Our analysis revealed an increase in total amino acid content in both varieties, with phenylalanine, proline, and methionine accumulating in both. Additionally, several amino acids exhibited significant decreases in HN65 but slight increases in HN44. Multi-omics association analysis identified 13 amino acid-related pathways. We thoroughly examined the changes in genes and metabolites involved in various amino acid metabolism/synthesis and determined core genes and metabolites through correlation networks. The phenylalanine, tyrosine, and tryptophan metabolic pathways and proline, glutamic acid and sulfur-containing amino acid pathways were particularly important for drought resistance. Some candidate genes, such as ProDH and P4HA family genes, and metabolites, such as O-acetyl-L-serine, directly affected up- and downstream metabolism to induce drought resistance. This study provided a basis for soybean drought resistance breeding.

The application potential of mepiquat chloride in soybean: improvement of yield characteristics and drought resistance.

BACKGROUND: Drought can result in yield losses, the application of plant growth regulators is an effective measure to improve drought resistance and yield. The objective of the study was to explore the application potential of mepiquat chloride (MC) in regulating soybean yield and drought resistance. METHODS: In this study, a three-year field experiment was designed and combined with drought experiments to measure the yield of popularized varieties during 2021-2022 and drought-resistant and drought-sensitive varieties were selected, and planted in the field in 2023. RESULTS: MC increased the yield of HN84 and HN87 for two consecutive years from 2021 to 2022 and improved their physiological characteristics under field conditions. Under M200 treatment, the yield of HN84 increased by 6.93% and 9.46%, and HN87 increased by 11.11% and 15.72%. Different concentrations of MC have different effects on soybeans. The maximum increase of SOD, POD and proline in HN84 under M400 treatment reached 71.92%, 63.26% and 71.54%, respectively; the maximum increase of SOD, POD and proline in HN87 under M200 treatment reached 21.96%, 93.49% and 40.45%, respectively. In 2023, the foliar application of MC improved the physiological characteristics of HN44 and HN65 under drought-stress conditions. On the eighth day of drought treatment, compared to the drought treatment, the leaf and root dry weight of HN44 under M100 treatment increased by 17.91% and 32.76%, respectively; the dry weight of leaves and roots of HN65 increased by 20.74% and 29.29% under M200 treatment, respectively. MC also reduced malondialdehyde (MDA) content, decreased antioxidant enzyme activity and proline content. In addition, different concentrations of MC increased the chlorophyll fluorescence parameters (Fs, Fv/Fm, YII, and SPAD). In the field, the plant height of the two varieties decreased significantly, the yield increased, the number of two-grain and three-grain pods increased, and the stem length at the bottom and middle decreased with MC induction. CONCLUSIONS: The application of 100-200 mg/L MC effectively improved drought resistance and increased yield. This study provided support for the rational application of MC in soybean production.

Current views of drought research: experimental methods, adaptation mechanisms and regulatory strategies.

Drought stress is one of the most important abiotic stresses which causes many yield losses every year. This paper presents a comprehensive review of recent advances in international drought research. First, the main types of drought stress and the commonly used drought stress methods in the current experiment were introduced, and the advantages and disadvantages of each method were evaluated. Second, the response of plants to drought stress was reviewed from the aspects of morphology, physiology, biochemistry and molecular progression. Then, the potential methods to improve drought resistance and recent emerging technologies were introduced. Finally, the current research dilemma and future development direction were summarized. In summary, this review provides insights into drought stress research from different perspectives and provides a theoretical reference for scholars engaged in and about to engage in drought research.

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.

SIRT2 promotes base excision repair by transcriptionally activating OGG1 in an ATM/ATR-dependent manner.

Sirtuin 2 (SIRT2) regulates the maintenance of genome integrity by targeting pathways of DNA damage response and homologous recombination repair. However, whether and how SIRT2 promotes base excision repair (BER) remain to be determined. Here, we found that independent of its catalytic activity SIRT2 interacted with the critical glycosylase OGG1 to promote OGG1 recruitment to its own promoter upon oxidative stress, thereby enhancing OGG1 promoter activity and increasing BER efficiency. Further studies revealed that SIRT2 was phosphorylated on S46 and S53 by ATM/ATR upon oxidative stress, and SIRT2 phosphorylation enhanced the SIRT2-OGG1 interaction and mediated the stimulatory effect of SIRT2 on OGG1 promoter activity. We also characterized 37 cancer-derived SIRT2 mutants and found that 5 exhibited the loss of the stimulatory effects on OGG1 transcription. Together, our data reveal that SIRT2 acts as a tumor suppressor by promoting OGG1 transcription and increasing BER efficiency in an ATM/ATR-dependent manner.

Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge.

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking.

Molecular mechanism of drought resistance in soybean roots revealed using physiological and multi-omics analyses.

Soybeans are one of the most cultivated crops worldwide and drought can seriously affect their growth and development. Many studies have elucidated the mechanisms through which soybean leaves respond to drought; however, little is known about these mechanisms in roots. We used two soybean varieties with different drought tolerances to study the morphological, physiological, and molecular response mechanisms of the root system to drought stress in seedlings. We found that drought stress led to a significant decrease in the root traits and an increase in antioxidant enzyme activity in the two varieties. Drought-resistant varieties accumulate large amounts of flavonoids and phenolic acids at the metabolic level, which causes variations in drought resistance. Additionally, differences in gene expression and drought-resistance pathways between the two varieties were clarified using transcriptome analysis. Through a multi-omics joint analysis, phenylpropanoid and isoflavonoid biosynthesis were identified as the core drought resistance pathways in soybean roots. Candidate genes and marker metabolites affecting drought resistance were identified. The phenylpropanoid pathway confers drought tolerance to roots by maintaining a high level of POD activity and mediates the biosynthesis of various secondary drought-resistant metabolites to resist drought stress. This study provides useful data for investigating plant root drought responses and offers theoretical support for plant breeding for drought resistance.

Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models.

Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper makes a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions.

Spatial pattern characteristics and influencing factors of mountainous rural settlements in metropolitan fringe area: A case study of Pingnan County, Fujian Province.

Amid global industrialization and urbanization, mountainous rural settlements, especially those in metropolitan fringe area, are experiencing significant spatial changes in location and scale. This study takes Pingnan County, Fujian Province, China, as an example. Utilizing land use data and employing methods including standard deviation ellipse, average nearest neighbor index, kernel density estimation, spatial hotspot detection, binary logistic regression model, and Geodetector, this study aims to scientifically identify the spatial pattern characteristics and influencing factors of its settlements. The results show that: (1) The spatial distribution of settlements in Pingnan County is biased toward the southern part of the county; the center of settlement's spatial distribution is located south of the junction of Gufeng Town and Pingcheng Town; the spatial distribution trend of settlements is north-east-southwest. Settlements are generally aggregated, and the aggregation degree of Gufeng Town is obviously lower than that of other towns. (2) The density distribution of settlements presents a "core-periphery" structure and a "north-south linear" structure in space; the spatial pattern characteristics show high-density, large patches in Gufeng Town, high-density, small patches in Changqiao Town, Tangkou Town and Gantang Town, and medium-density or low-density, small patches in other towns. (3) Settlement location is mainly affected by the elevation, distance to cultivated land, and distance to main roads, while settlement scale is mainly affected by slope, relief degree of land surface, and distance to urban centers. The interaction between these factors exhibits enhancement effects, with natural terrain and location conditions exerting the most prominent influence. These findings underscore the strong constraints posed by natural topography on mountainous rural settlements in metropolitan fringe areas, coupled with a more pronounced influence from socio-economic factors. The study's results hold significant implications for optimizing the layout of such settlements, guiding land spatial planning, and promoting rural revitalization.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Graph model for multiple scattering in lithium niobate on insulator integrated photonic networks.

We present a graph-based model for multiple scattering of light in integrated lithium niobate on insulator (LNOI) networks, which describes an open network of single-mode integrated waveguides with tunable scattering at the network nodes. We first validate the model at small scale with experimental LNOI resonator devices and show consistent agreement between simulated and measured spectral data. Then, the model is used to demonstrate a novel platform for on-chip multiple scattering in large-scale optical networks up to few hundred nodes, with tunable scattering behaviour and tailored disorder. Combining our simple graph-based model with material properties of LNOI, this platform creates new opportunities to control randomness in large optical networks.

PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI.

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

Characterization and sources of water-soluble inorganic ions during sulfate-driven and nitrate-driven haze on the largest loess accumulation plateau.

With the rapid reduction of anthropogenic SO2 emissions, the critical driver of haze in China has shifted from being dominated by sulfate to alternating sulfate and nitrate. Haze induced by different driver species may differ in the chemical forms of water-soluble inorganic ions (WSIIs). The unique topography and high-emission industrial agglomeration of the Loess Plateau determine its severe local PM2.5 pollution and influence global weather patterns through the outward export of pollutants. PM2.5 samples were conducted in Pingyao, on the eastern Loess Plateau of China, in autumn and winter. The average mass of PM2.5 was 88.82 ± 57.37 µg/m3; sulfate, nitrate, and ammonium were the dominant component. The chemical form of the ion was dominated by (NH4)2SO4, NH4NO3, NaNO3 and KNO3 during the nitrate-driven (ND) haze, while (NH4)2SO4, NH4HSO4, NH4NO3, NaNO3 and KNO3 were predominant species during the sulfate-driven (SD) haze. Heterogeneous oxidation reactions dominated the mechanism of sulfate formation. Primary sulfate emissions or other generation pathways contributed to sulfate formation during the SD haze. The gas-phase homogeneous reaction of NO2 and NH3 dominates the nitrate generation during the ND haze. The heterogeneous reactions also played an essential role during the SD haze. Nitrate aerosol (42.30%) and coal and biomass combustion (23.23%) were the dominant sources of WSIIs during the ND haze. In comparison, nitrate aerosol (31.80%) and sulfate aerosol (25.08%) were considered the primary control direction during the SD haze. The chemical characteristics and sources of aerosols under various types of haze differ significantly, and knowledge gained from this investigation provides insight into the causes of heavy haze.

Mechanism of Mepiquat Chloride Regulating Soybean Response to Drought Stress Revealed by Proteomics.

Soybeans are the main sources of oil and protein for most of the global population. As the population grows, so does the demand for soybeans. However, drought is a major factor that limits soybean production. Regulating soybean response to drought stress using mepiquat chloride (MC) is a feasible method; however, its mechanism is still unclear. This study used PEG-6000 to simulate drought stress and quantitative proteomic techniques to reveal changes in Heinong44 (HN44) and Heinong65 (HN65) subjected to drought following the application of 100 mg/L of MC. The results showed that SOD in HN44 did not change significantly but decreased by 22.61% in HN65 after MC pretreatment, and MDA content decreased by 22.75% and 21.54% in HN44 and HN65, respectively. Furthermore, MC improved the GSH-ASA cycle and simultaneously promoted the Calvin cycle process to enable the plant to maintain a certain carbon assimilation rate under osmotic stress. In addition, MC upregulated some proteins during gluconeogenesis and starch metabolism and increased soluble sugar content by 8.41% in HN44. MC also reduced ribosomal protein abundance, affecting translation and amino acid metabolism. In summary, MC improved GSH-ASA cycle and Calvin cycle under stress to alleviate oxidative damage and maintain crop growth. Our study is the first to report the mechanism of MC regulation in soybean under osmotic stress, providing new insights for the rational application of MC in soybean.

Emergence of a new designated clade 16 with significant antigenic drift in hemagglutinin gene of H9N2 subtype avian influenza virus in eastern China.

H9N2 avian influenza viruses (AIVs) pose an increasing threat to the poultry industry worldwide and have pandemic potential. Vaccination has been principal prevention strategy to control H9N2 in China since 1998, but vaccine effectiveness is persistently challenged by the emergence of the genetic and/or antigenic variants. Here, we analysed the genetic and antigenic characteristics of H9N2 viruses in China, including 70 HA sequences of H9N2 isolates from poultry, 7358 from online databases during 2010-2020, and 15 from the early reference strains. Bayesian analyses based on hemagglutinin (HA) gene revealed that a new designated clade16 emerged in April 2012, and was prevalent and co-circulated with clade 15 since 2013 in China. Clade 16 viruses exhibited decreased cross-reactivity with those from clade 15. Antigenic Cartography analyses showed represent strains were classified into three antigenic groups named as Group1, Group2 and Group3, and most of the strains in Group 3 (15/17, 88.2%) were from Clade 16 while most of the strains in Group2 (26/29, 89.7%) were from Clade 15. The mean distance between Group 3 and Group 2 was 4.079 (95%CI 3.605-4.554), revealing that major switches to antigenic properties were observed over the emergence of clade 16. Genetic analysis indicated that 11 coevolving amino acid substitutions primarily at antigenic sites were associated with the antigenic differences between clade 15 and clade 16. These data highlight complexities of the genetic evolution and provide a framework for the genetic basis and antigenic characterization of emerging clade 16 of H9N2 subtype avian influenza virus.

SNP application improves drought tolerance in soybean.

As an important bioactive molecule, nitric oxide (NO) can effectively alleviate the effects of drought stress on crops. However, it is still unclear whether it can increase the stress resistance of soybean. Therefore, in this study, our objective was to explore the effect of exogenous NO application on the physiological characteristics of soybean seedlings under drought stress. As test material, two soybean varieties, HN65 and HN44, were used, while sodium nitroprusside (SNP) of 100 µmol L-1, 200 µmol L-1, 500 µmol L-1, 1000 µmol L-1 served as an exogenous NO donor, and PEG-6000 as an osmotic regulator to simulate drought stress. The effects of irrigation with different SNP concentrations for different days on the physiological characteristics of the soybean seedlings under drought conditions were then investigated. The results obtained showed that the activities of antioxidant enzymes, osmotic regulator contents, as well as the abscisic acid and salicylic acid contents of the plant leaves increased with increasing SNP concentration and treatment time. However, we observed that excessively high SNP concentrations decreased the activities of key nitrogen metabolism enzymes significantly. This study provides a theoretical basis for determining a suitable exogenous NO concentration and application duration. It also highlights strategies for exploring the mechanism by which exogenous NO regulates crop drought resistance.

PAIP 2020: Microsatellite instability prediction in colorectal cancer.

Microsatellite instability (MSI) refers to alterations in the length of simple repetitive genomic sequences. MSI status serves as a prognostic and predictive factor in colorectal cancer. The MSI-high status is a good prognostic factor in stage II/III cancer, and predicts a lack of benefit to adjuvant fluorouracil chemotherapy in stage II cancer but a good response to immunotherapy in stage IV cancer. Therefore, determining MSI status in patients with colorectal cancer is important for identifying the appropriate treatment protocol. In the Pathology Artificial Intelligence Platform (PAIP) 2020 challenge, artificial intelligence researchers were invited to predict MSI status based on colorectal cancer slide images. Participants were required to perform two tasks. The primary task was to classify a given slide image as belonging to either the MSI-high or the microsatellite-stable group. The second task was tumor area segmentation to avoid ties with the main task. A total of 210 of the 495 participants enrolled in the challenge downloaded the images, and 23 teams submitted their final results. Seven teams from the top 10 participants agreed to disclose their algorithms, most of which were convolutional neural network-based deep learning models, such as EfficientNet and UNet. The top-ranked system achieved the highest F1 score (0.9231). This paper summarizes the various methods used in the PAIP 2020 challenge. This paper supports the effectiveness of digital pathology for identifying the relationship between colorectal cancer and the MSI characteristics.

Lysine Deprivation Suppresses Adipogenesis in 3T3-L1 Cells: A Transcriptome Analysis.

Growing evidence proves that amino acid restriction can reverse obesity by reducing adipose tissue mass. Amino acids are not only the building blocks of proteins but also serve as signaling molecules in multiple biological pathways. The study of adipocytes' response to amino acid level changes is crucial. It has been reported that a low concentration of lysine suppresses lipid accumulation and transcription of several adipogenic genes in 3T3-L1 preadipocytes. However, the detailed lysine-deprivation-induced cellular transcriptomic changes and the altered pathways have yet to be fully studied. Here, using 3T3-L1 cells, we performed RNA sequencing on undifferentiated and differentiated cells, and differentiated cells under a lysine-free environment, and the data were subjected to KEGG enrichment. We found that the differentiation process of 3T3-L1 cells to adipocytes required the large-scale upregulation of metabolic pathways, mainly on the mitochondrial TCA cycle, oxidative phosphorylation, and downregulation of the lysosomal pathway. Single amino acid lysine depletion suppressed differentiation dose dependently. It disrupted the metabolism of cellular amino acids, which could be partially reflected in the changes in amino acid levels in the culture medium. It inhibited the mitochondria respiratory chain and upregulated the lysosomal pathway, which are essential for adipocyte differentiation. We also noticed that cellular interleukin 6 (IL6) expression and medium IL6 level were dramatically increased, which was one of the targets for suppressing adipogenesis induced by lysine depletion. Moreover, we showed that the depletion of some essential amino acids such as methionine and cystine could induce similar phenomena. This suggests that individual amino acid deprivation may share some common pathways. This descriptive study dissects the pathways for adipogenesis and how the cellular transcriptome was altered under lysine depletion.

Isoxazole/Isoxazoline Skeleton in the Structural Modification of Natural Products: A Review.

Isoxazoles and isoxazolines are five-membered heterocyclic molecules containing nitrogen and oxygen. Isoxazole and isoxazoline are the most popular heterocyclic compounds for developing novel drug candidates. Over 80 molecules with a broad range of bioactivities, including antitumor, antibacterial, anti-inflammatory, antidiabetic, cardiovascular, and other activities, were reviewed. A review of recent studies on the use of isoxazoles and isoxazolines moiety derivative activities for natural products is presented here, focusing on the parameters that affect the bioactivity of these compounds.

Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types.

BACKGROUND: Identifying lymph node metastasis (LNM) relies mainly on indirect radiology. Current studies omitted the quantified associations with traits beyond cancer types, failing to provide generalisation performance across various tumour types. METHODS: 4400 whole slide images across 11 cancer types were collected for training, cross-verification, and external validation of the pan-cancer lymph node metastasis (PC-LNM) model. We proposed an attention-based weakly supervised neural network based on self-supervised cancer-invariant features for the prediction task. RESULTS: PC-LNM achieved a test area under the curve (AUC) of 0.732 (95% confidence interval: 0.717-0.746, P < 0.0001) in fivefold cross-validation of multiple cancer types, which also demonstrated good generalisation in the external validation cohort with AUC of 0.699 (95% confidence interval: 0.658-0.737, P < 0.0001). The interpretability results derived from PC-LNM revealed that the regions with the highest attention scores identified by the model generally correspond to tumours with poorly differentiated morphologies. PC-LNM achieved superior performance over previously reported methods and could also act as an independent prognostic factor for patients across multiple tumour types. DISCUSSION: We presented an automated pan-cancer model for predicting the LNM status from primary tumour histology, which could act as a novel prognostic marker across multiple cancer types.