Chronic obstructive pulmonary disease (COPD), a preventable lung disease, has the highest prevalence in the elderly and deserves special consideration regarding earlier warnings in this fragile population. The impact of age on COPD is well known, but the COPD risk of the aging process in the lungs remains unclear. Therefore, it is necessary to understand the COPD risk of the aging process in the lungs, providing an early COPD risk decision for adults.COPD risk is evaluated for adults to make an early COPD risk decision from the perspective of lung radiomics features. First, the subjects are divided into four groups according to the COPD stages. Their ages are divided into eight equal age intervals in each group. Second, four survival Cox models are established based on the lung radiomics features to evaluate the risk probability from COPD stage 0 to suffering COPD and COPD stages. Finally, four risk ranks are defined by equally dividing the COPD risk probability from 0 to 1. Subsequently, the COPD risk at different stages is evaluated with varying age intervals to provide an early COPD risk decision.The evaluation metrics area under the curve (AUC)/C index of four survival Cox models are 0.87/0.94, 0.84/0.83, 0.94/0.89, and 0.97/0.86, respectively, showing the effectiveness of the models. The risk rank levels up every 5 years for the subjects who had suffered COPD after 60. For the subjects with COPD stage 0, the risk rank of suffering COPD stage I levels up every 5 years after the age of 65 years, and the risk rank of suffering COPD stages II and III & IV levels up every 5 years after the age of 70 years.Once the age is above 60 years, the patients with COPD need to take action to prevent the progress and deterioration of COPD. Once the age is above 65 years, the patients with COPD stage 0 need to take precautions against COPD. The gold standard for the diagnosis and evaluation of COPD is the forced expiratory volume in the first second FEV and FEV1st (PFT) , COPD trst (PFT) , 4, COPDst (PFT) , 6, and st (PFT) , 8. Manyst (PFT) . Age relst (PFT) . Age hasst (PFT) . COPD hast (PFT) . However1 and FVC in the PFT increase with age from 6 to 18 years, which linearly change until the adolescent growth spurt at about 10 years in girls and 12 years in boys  has been regarded as the most effective modality for characterizing and quantifying COPD , for exaIn summary, our contributions in this study are briefly described as follows:Four survival Cox models are established to evaluate the COPD risk at different COPD stages based on lung radiomics features;Earlier COPD risk decisions are made. The start age of the COPD risk rank, which levels up every 5 years, is given for the subjects who had suffered COPD or may suffer COPD at different stages.This section mainly introduces the cohort  and research methods used in this study.The ethics committee had approved this study of the National Clinical Research Center of Respiratory Diseases in Guangzhou Medical University, China. Chinese subjects were enrolled by the China National Clinical Research Center of Respiratory Diseases from May 25, 2009, to January 11, 2011.A trained deep learning model ResU-Net automatically segments the lung parenchyma images from the chest HRCT images. Then, PyRadiomics automatically calculates the lung radiomics features based on the lung parenchyma images. The 468 subjects are divided into four groups A–D according to the COPD stages, and the ages of 40–79 years are divided into eight equal age intervals in each group. Each group includes the subjects suffering from COPD stage 0 as the 1st subgroup. The 2nd subgroup of the four groups includes the subjects suffering from COPD, stages I, II, and III & IV, respectively. The Lasso model is individually applied to select the lung radiomics features of the four groups. To predict the COPD risk probability of the four groups, the four survival Cox models are constructed based on the selected lung radiomics features by the Lasso model and their age intervals, generating intuitive radiomics nomograms. The four COPD risk ranks are defined by equally dividing the COPD risk probability from 0 to 1. Subsequently, the COPD risk at different stages is evaluated with varying age intervals.https://github.com/JoHof/lungmask.Considering the overall change of lung status with aging, the lung parenchyma, including left and right lungs, is taken as the ROI in this study. The ResU-Net model trained by human chest CT images  automatihttps://pyradiomics.readthedocs.io/en/latest/index.html. Before calculating the lung radiomics features from the ROI (lung parenchyma) images, the ROI images with Hounsfield unit (HU) should be extracted from the chest HRCT images by our previous method  model have been confirmed . It is an = 130), and the 2nd subgroup in the four groups A–D are the subjects who suffer from COPD , I (n = 109), II (n = 122), and III & IV (n = 107), respectively. The reference , stages I, II, and III & IV using survival Cox models, respectively. eference  has giveK = 10) is used to ensure the effectiveness of the Lasso model.The standard R package lars (Lasso model) is applied separately to select the lung radiomics features affecting the COPD stages from the normalized lung radiomics features of the four groups. A ten-fold cross-validation .i = 1~468 (468 subjects), j = 1~1316 (1316 lung radiomics features of each subject), xij is the ith row and jth column of the 468 × 1,316 lung radiomics features, xjmax, xjmin are the mean, the maximum, the minimum of each radiomics featurexj, respectively.where, Formula (2) is the Lasso model to select the normalized lung radiomics features of the four groups, respectively.Ak denotes the selected lung radiomics features, k = 1, 2, 3, 4 respectively denotes group A-D. yi denotes each group's COPD stage (the independent variable). Especially, yi in group A denotes the COPD stage 0 and suffering COPD. λ denotes the penalty parameter (λ ≥ 0). βj denotes the regression coefficient, i∈, and j∈.where matrix x of the four groups from the selected lung radiomics features again, by statistically significant hazard ratio (HR). Then, the subjects with the final selected lung radiomics features x are divided into 70 and 30%. Notably, 70% of the subjects train the survival Cox models A–D by computing estimates of the survival functions, drawing the nomograms A–D, respectively. Then, four survival Cox models adopt the standard R rms package cph with the significance level α = 0.1. Finally, 30% of the subjects in each group validate the performance of the four trained survival Cox models, respectively. It is noted that the failure event time is the age interval. The event indicator represents the COPD stage 0, and the suffering COPD, stage I, stage II, and stage III & IV of the four groups.The survival Cox model , 46, theEquation (3) gives the survival probability formula of the survival Cox model.0(t) denotes the baseline hazard function, βTx denotes the log-risk function, which is the product of the probability at each event time the event has occurred to the individual. β denotes the weights for optimizing the Cox partial likelihood.where λEquation (4) converts the COPD survival probability to the COPD risk probability.k = 1, 2, 3, 4 respectively denotes the group A-D, Riski|k denotes the ith COPD risk probability in the kth group, and λi|k is the ith COPD survival probability in the kth group as Equation (3).where Specifically, the COPD risk probability in group A is the COPD stage 0 probability and suffering COPD probability. The COPD risk probability in groups B–D is the COPD stage 0 probability and suffering COPD stage I probability, stage II probability, and stage III & IV probability, respectively. After calculating the COPD risk probability, each group's COPD risk probability is separated according to the COPD stages. Then, the four COPD risk ranks  are defined by equally dividing the COPD risk probability from 0 to 1. Subsequently, the COPD risk is evaluated in the four groups at different risk ranks and age intervals.This section shows the final selected lung radiomics features, radiomics nomograms, performance evaluation of the four survival Cox models, scatter plots, and curves of COPD risk probability of the four groups, respectively.p-value) are also reported in “***” denotes p-value <0.001, “**” denotes p-value <0.01, “*” denotes p-value <0.05, and “.” denotes p-value <0.1. For facilitating expression, the final selected lung radiomics features are defined as Radiomics 1–16, respectively. Radiomics 1–16 are used to construct the survival Cox models.Specifically, Radiomics 1–3 are the factors that affect the COPD risk of the patients who have suffered from COPD with aging. Radiomics 4–5, Radiomics 6–11, and Radiomics 12–16 are the factors that affect the COPD risk of the patients who had suffered the COPD stages I, II, III, and IV with aging, respectively.Radiomics nomograms are pictorial representations depicting the association between radiomics variables and the probability of suffering COPD or different COPD stage events, providing an intuitive way to interpret the survival Cox model .X  show the importance of its group. For example, The four receiver operating characteristic (ROC) curves are drawn to evaluate the models A–D's performances. x. In this study, four survival Cox models of the four groups A–D are developed based on the lung radiomics features to evaluate COPD risk at different stages for adults aged from 40 to 79 years, providing a COPD risk decision for adults. All four survival Cox models' effectiveness has been evaluated. They are not only effective in evaluating the risk of suffering from COPD  but also effective in more detailed differentiation of COPD stages .The lung radiomics features, as an imaging biomarker reflecting the lung structure (SHAPE radiomics features) and lung tissue (the other radiomics features except for SHAPE), are first used to evaluate COPD risk at different stages with aging. Finally, independent features Radiomics 1–16, affecting the COPD evolution from stage 0 to suffering COPD, COPD stages I, II, and III & IV, are determined. Radiomics 1, 5, 10, and 13 are the most direct risk features for COPD evolution from stage 0 to suffering COPD, COPD stages I, II, and III & IV, respectively. Our study discovers that different lung radiomics features affect the COPD risk at different stages with aging. The trends of COPD risk probability, which increase with aging, are in line with the change law of the aging process in the lung , 13.For the subjects who have suffered the COPD, COPD stages I, II, and III & IV, our study discovers that although the COPD risk increases with aging, each COPD risk rank of the patients who had suffered from the COPD, COPD stages I, II, and III & IV, basically has the same age intervals. Therefore, age is not only a point to distinguish the “young COPD” (young patients with COPD aged <50 years) , 48 fromFor the subjects at the COPD stage 0, the COPD risk which may suffer the COPD, COPD stages I, II, and III & IV also increases with aging. Our study discovers that the age intervals of each COPD risk rank of the subjects who may suffer the COPD, COPD stages I, II, and III & IV, from the COPD stage 0 are inconsistent. Age also can distinguish different COPD risk ranks. It is a relatively safe COPD risk rank (the mild risk) of the subjects who may suffer the COPD and the COPD stage I before the age of 65 years. It is also a relatively safe COPD risk rank of subjects who may suffer the COPD stages II and III & IV before the age of 70 years. For the subjects at COPD stage 0, the risk rank of suffering COPD stage I levels up every 5 years after the age of 65 years, and the risk rank of suffering COPD stages II and III & IV levels up every 5 years after the age of 70 years. Therefore, the age of 65 years is the start age of the increased risk of the subjects who may suffer the COPD stage I, and the age of 70 years is the start age of the increased risk of the subjects who may suffer the COPD stages II and III & IV. Although the COPD risk cannot reach the extreme risk rank, once the subjects with COPD stage 0 are above 65 or 70 years, they also need to take precautions against COPD.There are some limitations of the methods and materials in this study. First, the survival Cox model considers both events and time, but it can only analyze two opposite events. At the same time, the model itself has high requirements for the collinearity of input data. Although we used the Lasso model to remove the collinearity of the lung radiomics features, some valuable features may be omitted. Then, there is a lack of subjects aged 40–50 years in the COPD stage I and subjects aged 40–55 years in the COPD stages III & IV. That is because relatively few people aged 40–50 years who suffer from COPD stage I will go to the hospital for treatment and undergo the CT scan, and few people suffer the COPD stages III & IV at the age of 40–55 years. Finally, although lung radiomics features have met the needs of COPD risk evaluation, an improved deep learning survival Cox model , with a Four effective models are established to evaluate COPD risk from COPD stage 0 to suffered COPD, COPD stages I, II, and III & IV, respectively. The early COPD risk decision is made based on the COPD risk results. The start age of the COPD risk rank, which levels up every 5 years, is given for the subjects who had suffered COPD or may suffer COPD at different stages. It concludes that once the age is above 60 years, the patients with COPD need to be paid close attention to prevent the progress and deterioration of COPD, and once the age is above 65 years, the patients with COPD stage 0 need to take precautions against COPD.The lung radiomics features supporting the conclusions of this article will be available with a reasonable request.The studies involving human participants were reviewed and approved by National Clinical Research Centre of Respiratory Diseases in Guangzhou Medical University, China. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.WL, HC, RC, and YK: conceptualization. YY, WL, YG, YL, and QL: methodology. YY, KY, SW, NZ, and WD: software. WL, YG, YL, and ZC: validation. YY, WL, HC, and XL: formal analysis. YY, WZ, and XL: investigation. KY, HC, and XL: resources. YY, WL, YG, YL, and ZC: data curation. YY: writing—original draft. WL, YL, and YK: writing—review and editing. YY, YG, QL, and SW: visualization. HC, RC, and YK: supervision. WL and YK: project administration. WL, HC, RC, and YK: funding acquisition. All authors contributed to the article and approved the submitted version.This work was supported by the Natural Science Foundation of Guangdong Province, China (2019A1515011382), the Stable Support Plan for Colleges and Universities in Shenzhen, China (SZWD2021010), the Scientific Research Fund of Liaoning Province, China (JL201919), and the National Natural Science Foundation of China (62071311).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Erk1, p38 Mapk, Jnk) were up-regulated by qPCR during the non-breeding season. These findings suggested that adiponectin may be involved in the regulation of seasonal changes in the ovarian function of wild ground squirrels, possibly by acting on the MAPK signaling pathway to regulate sex steroidogenesis in the ovaries.The goal of this study is to explore the relationship between altered circulating adiponectin concentration, ovarian tissue morphology, ovarian steroidogenesis, and sex hormone production in ovaries of wild ground squirrels. The ovarian mass differed significantly during the breeding and non-breeding seasons, and the circulating estradiol and progesterone concentrations were significantly higher in the breeding season, while the circulating adiponectin level was significantly lower. The expression levels of gonadotropin receptors (FSHR and LHR) and steroidogenic enzymes  were significantly higher during the breeding season. Comparing the ovarian transcriptome data of wild ground squirrels between the two periods, we found that some differentially expressed genes were enriched for ovarian steroidogenesis and the adipocytokine signaling pathway, which correlated with our present results. Notably, the MAPK signaling pathway was also enriched and its related genes ( Adiponectin, a 30 kDa peptide hormone, is the most abundant protein secreted by white adipose tissue. Adiponectin monomers undergo processes, such as post-translational modification, to form multiple multimers and are highly expressed in the circulation of rodents and humans . The isoFollicle development and sex steroidogenesis are two fundamental roles of the ovary, responsible for oogenesis and endocrine functions, respectively . Sex steCitellus dauricus Brandt) are typical seasonal breeding mammals whose reproductive activity exhibits cyclic variations from year to year [Wild ground squirrels  in ovaries showed that AdipoR1 and AdipoR2 were localized in the cytoplasm of theca cells, interstitial cells, luteal cells, and granulosa cells, with strong staining in ovaries during the breeding season a,b,d,e, Fshr and Lhr were significantly higher in the ovaries during the breeding season than the non-breeding season  in ovaries was examined, which showed that FSHR a–c and Lg season j,k.Star, Cyp11a1, Cyp19a1, and Hsd3b1 in ovaries were significantly higher in the breeding season than in the non-breeding season  were expressed in ovaries of wild ground squirrels in the breeding and non-breeding seasons, localized to the cytoplasm a–o. Therg season a–e.Differences in gene expression in ovarian tissue during the breeding and non-breeding seasons are shown in Erk1, Erk2, p38 Mapk, Jnk) in the ovarian tissues during the breeding and non-breeding seasons are shown in Erk1, p38 Mapk, Jnk)  a–d showeThis study investigated, for the first time, the relationship between adiponectin and the changes in ovarian function in seasonal breeding animals. Our results showed that ovaries of wild ground squirrels are larger and more active during the breeding season compared to the non-breeding season, with more steroidogenesis, reduced adiponectin secretion, and some altered pathways.The reproductive organs of seasonal breeding mammals show enlargement and physiological activity during the breeding season ,30,31,32Previous studies have demonstrated that, in addition to adipocytes, adiponectin can be produced and secreted by several other tissues, including skeletal muscle and cardiac muscle, which can play important autocrine and paracrine roles . In our We observed, via qPCR, that the expressions of steroidogenic enzymes  and gonadotropin receptors (FSHR and LHR) in the ovarian cells were significantly increased during the breeding season. The increased expression of StAR, P450scc, and 3β-HSD promoted the conversion of cholesterol to progesterone, whereas P450arom converted androgen to estradiol in response to FSH stimulation. This agrees with the ELISA results, showing significantly higher circulating progesterone and estradiol concentrations during the breeding season. At the same time, the plasma level of adiponectin was significantly lower in the breeding season than in the non-breeding season. However, the expression levels of adiponectin and AdipoR1 in the ovary were significantly higher in the breeding season, according to qPCR, which may be a compensatory mechanism and/or a negative feedback regulation of the receptors by hormones ,39. For Alterations in adiponectin and steroidogenesis are supported by transcriptomic data in the ovaries of wild ground squirrels. After comparing the ovarian transcriptome data between the breeding and non-breeding seasons, we found that many differentially expressed genes were related to the regulation of reproductive development, steroidogenesis, and adiponectin. In addition, we found that the MAPK pathway, the key downstream signal of adiponectin, was enriched by DEGs. The expression of the three major MAPK signaling pathways was examined by qPCR and that of extracellular-signal-regulated kinase 1/2 (ERK1/2), p38 MAPK, and c-Jun N-terminal kinase (JNK) was found to be significantly different between two periods. This suggests that the effect of adiponectin on steroidogenesis may be mediated through the MAPK signaling pathway. It has been reported that MAPK is a major downstream component of adiponectin signaling and adiponectin promotes p38 MAPK, ERK 1/2, and JNK phosphorylation through the adipoRs and APPL1 complex, thereby activating the MAPK signaling pathway and participating in the regulation of physiological activities ,44. NumeThe results of this study demonstrate that seasonal variations in adiponectin may regulate ovarian steroidogenesis under the mediation of the MAPK signaling pathway, thereby affecting seasonal ovarian function in female ground squirrels. The data obtained open up new prospects for studying the role of adiponectin in reproduction and may indicate its role in the control of reproduction in different seasons  and under the influence of various factors . In the future, we plan to explore the specific mechanism of the potential pathway in adiponectin effects on ovarian function in vitro.n = 10) and the non-breeding season  used in this study were captured in Zhangjiakou City, Hebei Province, China. Animals were anesthetized with 4% isoflurane, weighed, and phlebotomized in hind limb saphenous vein. Blood samples were collected into heparinized tubes and centrifuged at low speed . We collected the supernatant (plasma) after centrifugation and cryopreserved (−20 °C). The ground squirrels were euthanized using an overdose of pentobarbital , whose ovaries were dissected. The cross-sectional diameter of the ovaries (n = 10) was measured using a straightedge and the ovaries (n = 10) were weighed using an electronic balance. Ovaries from one side of wild ground squirrels (n = 10) were fixed in 4% paraformaldehyde for histological and immunohistochemical analysis, while ovaries from the other side (n = 10) were immediately frozen in liquid nitrogen and stored at −80 °C for gene expression analysis. All animal test procedures were in accordance with the Policy on the Care and Use of Animals of the Ethics Committee of Beijing Forestry University (EAWC_BJFU_202008) and approved by the Hebei Provincial Department of Agriculture (JNZF11/2007).The wild adult female ground squirrels (weighing 230–419 g) during the breeding season , and counterstained with hematoxylin. We scored the positive signals of DAB staining of ovarian tissue with ImageJ   [Deparaffinized ovarian sections were incubated in citric-acid-buffered solution and subsequently blocked with 10% goat serum. Tissues were incubated overnight (4 °C) with primary antibodies including AdipoR1 , AdipoR2 , FSHR , LHR , StAR , P450scc , P450c17 , P450arom , HSD3B1 , and control group with normal rabbit IgG instead of the primary antibody. The specificity of the AdipoR1, AdipoR2, FSHR, LHR, StAR, P450scc, P450c17, P450arom, and 3β-HSD antibodies have been described in previous studies ,53,54. Tositive) ,55.The concentrations of estradiol and progesterone in the plasma of wild ground squirrels were measured using ELISA kits  according to the protocol. The data at 450 nm were subsequently read with a microplate reader . The intra-assay and inter-assay coefficient of variation for estradiol were 5.1% and 8.9%, respectively. In the hormone determination of progesterone, the intra- and inter-assay co-efficient of variation were 3.9% and 6.7%, respectively. The intra-assay co-efficient of variation was 4.5% and the inter-assay co-efficient of variation was 7.3% in the hormone determination of adiponectin.β-actin.Total RNA from the ovaries of wild ground squirrels was extracted using the Trizol Kit  and the concentration was adjusted to 250 ng/µL. cDNA was synthesized with StarScript II RT MasterMix, RNA (1000 ng), and random primer . The 10 µL system , 5 μL 2× Power SYBR Green PCR master mix, and 1.4 μL ddH2O) was configured according to the FastStart DNA MasterPlast SYBR Green Kit  protocol and the primer design is shown in n = 3) was extracted following the protocol of Trizol kit. The mRNAs in the total RNA were enriched by magnetic beads with oligo (DT) and subsequently randomly interrupted. The fragmented mRNA was synthesized as cDNA single stranded using the M-MuLV reverse transcriptase system, followed by the addition of DNA polymerase I, dNTPs, and buffer to synthesize stable cDNA double strands. cDNA was purified by the AMPure XP system  and 200–250 bp cDNA was selected for PCR amplification. The amplification products were purified again using AMPure XP Beads, resulting in a cDNA library. Transcriptome libraries were sequenced by the Illumina HiSeq platform .Total RNA (1000 ng) of ovarian tissue  were selected. GO functional classification of differentially expressed genes was performed using the blast2GO algorithm and the KEGG mapping analysis of genes was performed by the KEGG pathway alignment analysis tool to obtain functional annotation as well as pathway enrichment analysis results.The clean reads were obtained by quality control of raw reads. Quantification of gene expression levels was calculated according to the method in Xie’s article . The datt test. p < 0.05 was set as the indicator of statistical significance.GraphPad Prism 6 software was used to analyze the significance of differences by Student’s


Air pollution has been identified as related to the diseases of susceptible population, but the spatial heterogeneity of its economic burden and its determinants are rarely investigated. The issue is of great policy significance, especially after the epidemic of COVID-19, when human are facing the joint crisis of health and environment, and some areas is prone to falling into poverty.The geographical detector was adopted to study the spatial distribution characteristics of the incidence of catastrophic health expenditure (ICHE) for older adults in 100 rural areas in China at the prefecture-city level. The health factors, sociological factors, policy factors and environmental factors and their interactions are identified.First, most health service factors had strong explanatory power for ICHE whether it interacts with air pollution. Second, 50 single-factor high-risk areas of ICHE were found in the study, but at the same time, there were 21 areas dominated by multiple factors.The different contributions and synergy among the factors constitute the complex mechanism of factors and catastrophic health expenditure. Moreover, during this process, air pollution aggravates the contribution of health service factors toward ICHE. In addition, the leading factors of ICHE are different among regions. At the end, this paper also puts forward some policy suggestions from the perspective of health and environment crisis in the post-COVID-19 world: environmental protection policies should be combined with the prevention of infectious diseases; advanced health investment is the most cost-effective policy for the inverse health sequences of air pollution and infectious diseases such as coronavirus disease 2019 (COVID-19); integrating environmental protection policy into healthy development policy, different regions take targeted measures to cope with the intertwined crisis.The online version contains supplementary material available at 10.1186/s12939-022-01774-6. Most countries took measures to block or restrict travel and worked to curb the spread of the disease. Although this is a great crisis in human history, it has unintentionally helped people in some respects. The concentration of some air pollutants, such as NOSeveral studies have confirmed the relationship between air pollution and respiratory –7, cardiWhile tackling air-pollution-related diseases, more attention should be paid to individuals vulnerable to CHE. First, compared with urban residents, rural residents are more likely to suffer from diseases caused by air pollution due to more outdoor work, less awareness of self-protection measures, and limited access to medical services. Comparing the cause-specific mortality between urban and rural areas in China from 2014 to 2016, the relative risks of all-cause, cardiovascular, and respiratory mortality in rural areas were 1.01, 1.03, and 1.04 times higher than those in urban areas, respectively . A TurkiIn addition, despite the spatial spill over effect of air pollution, few relevant studies use spatial methods to analyse the correlation between various related factors –31. To tHealth service factors are closely related to CHE. According to their characteristics, we divided the various health service factors into three categories: demand, supply, and utilisation of health services. The demand for health services is mainly reflected by health status, which the prevalence of non-communicable diseases (NCDs) and prevalence of disability (DA) in this study belong to. Li et al.  found thThe synergy among other factors constitutes the complex mechanism of air pollution and catastrophic health expenditure, as shown in Fig. 2.5, PM10, SO2, NO2, O3, and CO, and comprehensively evaluates China’s air quality.The Ministry of Environmental Protection (MEP) of the People’s Republic of China (PRC) has employed the new index “air quality index (AQI)” since January 2013. Our study used this composite index to describe regional air quality. AQI considers six major air pollutants in China, namely, PMThe data source of AQI in this study is China National Environmental Monitoring Centre (CNEMC). CNEMC covers four levels: national, provincial, municipal and county, including 1436 monitoring stations in 338 cities above prefecture level, 96 regional stations, and 15 background stations [China Health and Retirement Longitudinal Study (CHARLS) is a longitudinal survey designed to represent mainland China residents aged 45 and over. There is no upper age limit. It attempts to establish a high-quality public micro database, which can provide a wide range of information from socio-economic status to health status to meet the needs of scientific research of the elderly. CHARLS coordinates with leading international research in the Health and Retirement Research model to ensure the adoption of best practices and international comparability of results. The national baseline survey was conducted from 2011 to 2012: the second wave in 2013, the third wave in 2015, and the fourth wave in 2018. To ensure the representativeness of the sample, the CHARLS baseline survey covered 150 countries/regions and 450 villages/urban communities and involved 17,708 individuals from 10,257 families, reflecting the overall situation of China’s middle-aged and elderly population. At present, the first four waves of the CHARLS data and life history have been publicly released. In this study, the latest 2018 CHARLS database was used as the primary data source, but finally, due to the small sample size of individual research areas, only 100 sample cities , and Var is the variance.The significance of the data was analysed according to the classification method mentioned above:China is composed of 333 prefectures. The distribution of cities with high ICHE is dotted in the rural areas of China investigated in this study. As shown in Fig. In addition, Lu’an City’s ICHE (38.78%) was the highest, and Chaozhou City’s ICHE (1.82%) was the lowest, with a difference of 21.3 times.The geographical detector analysed the previous raster data and obtained the following results:As shown in Table The results in Fig. A risk detector is used to explore which category of a risk factor corresponds to the high or low value of the dependent variable ICHE. By calculating the mean value of ICHE in each stratum, we observed that ICHE changed with the increasing risk factors. In Fig. P < 0.01), the explanatory power of OOP, IOEXP, UROS, URH, NCDs, DA, NHB, NH, and ND for the spatial distribution of ICHE reached 30.82%, 26.4%, 8.15%, 13.44%, 14.7%, 15.45%, 8.62%, 12.54%, and 10.99%, respectively. Most health service factors have strong explanatory power for ICHE. Furthermore, when interacting with air pollution, these explanatory powers increased to 52.86%, 56.15%, 56.48%, 57.96%, 63.3%, 45.58%, 43.61%, 40.78%, and 43.63%, respectively. It shows that the health service factor contributes more to ICHE in the context of air pollution and always plays an especially important role. Furthermore, we have tentatively introduced annual average PM2.5 data from CNEMC to test the robustness of AQI related results. The results showed that there was little difference between the results of PM2.5 and that of AQI , E-H risk area (4), H-A risk area (4), S-I risk area (1), S-H risk area (1), E-I-H risk area (2), S-I-H risk area (1) and E-H-A risk area (1). We will focus on Chaozhou (A risk area) and Fuzhou (E-H-A risk area). Chaozhou is a coastal city in the eastern Guangdong Province. Its terrain is high in the northwest and low in the southeast. It is surrounded by mountains on three sides and the sea on one side. It is quite easy to form a stable atmospheric environment in Chaozhou, but also prone to serious air pollution problems , which aIn the previous section, we described the impact of air pollution on the spatial distribution of ICHE, and extensive studies have focused on the health consequences of air pollution and proposed environmental and health policies for air pollution. However, it is far from sufficient. Human activities have brought about an increasing number of climate problems and public health emergencies. In the face of continuous environmental deterioration and catastrophic public emergencies such as COVID-19, the spill over and dissemination of the two create a linked destiny—having to face the common problems of economic stagnation and damage to human health.As we all know, both COVID-19 and air pollution are caused by human activities that lead to environmental degradation, resulting in avoidable loss of life and economic losses, mainly due to delays, inadequacies, and mistakes in action . Health 2 dropped by 12·9 μg/m3 and PM2.5 dropped by 18·9 μg/m3 in most cities in China due to the quarantine period during the COVID-19 pandemic. The short-term improvement of air quality during the quarantine period avoided a total of 8911 NO2-related deaths and 3214 PM2.5-related deaths [The pandemic prompted countries to suspend economic activities and establish temporary isolation policies, further leading to the drop in air pollution before and after the pandemic. NOd deaths  and limid deaths . However2.5, PM10, and other atmospheric particles in air pollution; that is, viruses such as the SARS-CoV-2 can be transmitted to the human body through biological aerosols [In past management, most people did not realise a connection between air pollution and virus transmission. Relevant research on the outbreak of COVID-19 has paid attention to this matter. It has been found that a large number of bacteria and viruses are suspended in PMaerosols . First, aerosols  and otheaerosols , 12 diseaerosols , eruptinBoth the pandemic itself and the butterfly effect brought about by the pandemic may cause significant changes across some factors in our model. Lockdowns during the COVID-19 pandemic may make it difficult to get nutritious food and physical activity , furtherFor a long time, compared with other aspects, politicians have regarded health as a secondary aspect of the government’s action plan, thus redistributing the budget and paying more attention to those aspects that drive economic development . An impoWhether it is an industrial policy or the follow-up recovery strategy of COVID-19, ‘people-oriented’ is the theme we emphasise. However, when people’s health and economic burdens are linked to air pollution, we must pay attention to environmental protection in some policies. COVID-19 has seriously damaged the global economy . How to Additional file 1: Appendix Fig. 1. The robustness test results of AQI related results.


The majority of the 27 SNP marking genes constituting the Allograft rejection pathway were located on chromosome 6 (19 SNPs) and the remainder were mapped to chromosomes 2, 3, 10, 12, 20, and X. This pathway comprises several immune system components crucial for the self versus non-self recognition, but also the components of antiviral immunity. Our study demonstrated that not only single variants are important for resistance to COVID-19, but also the cumulative impact of several SNPs within the same pathway matters.COVID-19 infections pose a serious global health concern so it is crucial to identify the biomarkers for the susceptibility to and resistance against this disease that could help in a rapid risk assessment and reliable decisions being made on patients’ treatment and their potential hospitalisation. Several studies investigated the factors associated with severe COVID-19 outcomes that can be either environmental, population based, or genetic. It was demonstrated that the genetics of the host plays an important role in the various immune responses and, therefore, there are different clinical presentations of COVID-19 infection. In this study, we aimed to use variant descriptive statistics from GWAS (Genome-Wide Association Study) and variant genomic annotations to identify metabolic pathways that are associated with a severe COVID-19 infection as well as pathways related to resistance to COVID-19. For this purpose, we applied a custom-designed mixed linear model implemented into custom-written software. Our analysis of more than 12.5 million SNPs did not indicate any pathway that was significant for a severe COVID-19 infection. However, the Allograft rejection pathway (hsa05330) was significant ( COVID-19 infections pose a serious global health concern, so it remains crucial to identify the biomarkers for the susceptibility to and resistance against this disease that could help in a rapid risk assessment and reliable decisions being made on patients’ treatment and their potential hospitalisation. The knowledge of genomic variants that are associated with the susceptibility to and resistance against COVID-19 infections is essential for understanding the metabolic pathways and regulatory factors related to different presentations of the disease and, in consequence, for developing new drug targets [Several studies investigated factors associated with severe COVID-19 outcomes that can be either environmental, population based, or genetic. For instance, a geographic and population variation in the disease course and outcome has demonstrated that individuals representing Black, Hispanic, or Asian ethnicities have a higher risk of death compared to Caucasians ,3. The iOAS1, OAS2, and OAS3 [CD74, which is a part of the MHC-II that blocks the endosomal entry pathway of the virus [ACE2 located on chromosome X and human leukocyte antigens (HLA).It was also demonstrated that the genetics of the host plays an important role in the various immune responses and, therefore, in the different clinical presentations of COVID-19 infection. Risk and protective variants were associated with multiple loci on chromosomes 3, 9, 6, 12, 19, and 21 ,12 incluand OAS3 , and an he virus . FurtherThe main aim of our study was to use variant descriptive statistics from GWAS and variant genomic annotations to identify metabolic pathways that are associated with a severe COVID-19 infection as well as pathways related to resistance to COVID-19. For this purpose, we applied a custom-designed mixed linear model implemented in custom-written software.p = 7.10547 × 10−9) within the intron of RAPGEF5 gene (ENSG00000136237); however, this gene is not assigned to any KEGG pathway. In total, 12,736 SNPs were included in the pathway model. Out of the 288 KEGG pathway effects predicted, none were significant in the SEVERE analysis, while the Allograft rejection pathway (hsa05330) was significant (p = 0.01087) in the RESISTANT analysis. All 27 SNPs, each representing one gene constituting hsa05330, were significant in GWAS  and the subset used in the estimation of KEGG pathway effects, demonstrated that the majority of most significant nic SNPs , which cOur analysis did not indicate any significant pathway in the case of a severe COVID-19 phenotype. However, in the case of a resistant phenotype, the Allograft rejection pathway (hsa05330) was identified as significant, with 27 SNPs marking genes constituting this pathway. The Allograft rejection pathway , associaCTLA-4 expression increases upon activation [CD86 is also associated with myocarditis and gallbladder squamous cell carcinoma [Considering the genomic location of the 27 SNPs from the Allograft rejection pathway , five SNtivation ,27,29. Ctivation ,29,31. Itivation . Malfuncarcinoma .One SNP on chromosome 12 pointed out interferon IFN-gamma, which is a cytokine produced mostly by activated T cells and NK cells and that can induce MHC expression, which makes it an important factor in transplantation. IFN plays a dual role in COVID-19 infections, on one side aggravating the symptoms, and on the other alleviating the disease course, depending on the disease stage and types of the interferon involved and on the personal predispositions . This ocUnsurprisingly, the outcome of COVID-19 has been reported to be more severe in patients with co-existing pathologies, especially those associated with an impaired immune system ,40. The Despite the promising results, we must bear in mind the fact that severe COVID-19 might also be triggered by immune system aberrations, such as inborn errors of the interferon pathways . The humThe immunological synapse is an important element in the Allograft rejection pathway. The structure of the immunological synapse, i.e., the gap between the immune cell and another cell, such as antigen-presenting cells, APC, together with its complex molecular interactions and canonical, means the step-manner organisation has been known as “the bull’s eye structure”, with the central complex made of MHC-TCR (major histocompatibility complex–T cell receptor), leading to it providing the first signal ,47,48,49Furthermore, two SNPs were found on chromosome 10 . The firRAPGEF5 gene, with an outstanding p-value of 7.10547 × 10−9, was not assigned to any KEGG pathway. RAPGEF5 encodes Rap guanine nucleotide exchange factor 5 protein and belongs to the Ras family of GTPases that plays an important role in cell growth, differentiation, and malignant transformation [The most significant SNP from GWAS, marking the ormation . The genBlood samples were collected from 1235 individuals across Poland between April 2020 and April 2021. In our analysis, a subset of 1076 samples from unrelated individuals was used. For all individuals, basic clinical data including age, gender, BMI, and comorbidities  were collected. For some participants, additional data on genetic disorders, flu, tuberculosis, and measles vaccination status, smoking habits, as well as hepatitis C infection were ascertained. Only individuals without diagnosed severe health disorders (till the moment of sample collection), such as cancer, were qualified for this study. Within this cohort, the SEVERE group (N = 235) was composed of patients with severe, life-threatening outcomes of COVID-19 infection including respiratory insufficiency, requiring intensive medical care and artificial ventilation, and NEWS  less than 5 . The RESDetailed information about the cohort, including demographic and clinical features, can be found in our previous paper by Kaja et al. .All participants of this study provided informed consent before the collection of blood samples and clinical data. The ethical approval was granted by the Ethics Committee of the Central Clinical Hospital of the Ministry of Interior and Administration in Warsaw (decisions 41/2020 from 3 April 2020 and 125/2020 from 1 July 2020). The study complied with the 1964 Helsinki declaration and its later amendments and adhered to the highest data security standards 140 of ISO 27001 and the General Data Protection Regulation (GDPR) act.The project was carried out following the Total Quality Management (TQM) methodology, which ensures the quality of results and analyzes the risk and possible difficulties of the planned methodology. TQM involves defining all critical points of the procedures: reference ranges for collected biological material, material preparation, DNA isolation, DNA concentration and quality, genome sequencing, and quality control of the data. The legal and ethical transparency of the entire project was ensured, including confidentiality, integrity, and impartiality of the data.Whole genomes of 1076 unrelated participants were sequenced in this study. Four mL of K-EDTA peripheral blood from participants were collected according to the standardised Quality Management System protocol. Genomic DNA was isolated from the peripheral blood leukocytes using a QIAamp DNA Blood Mini Kit, Blood/Cell DNA Mini Kit  and Xpure Blood Kit  according to the manufacturers’ protocols. The concentration and purity of isolated DNA were measured using the NanoDropTM spectrophotometer and the quality of the DNA was evaluated using gel electrophoresis. The sequencing library was prepared by Macrogen Europe  using TruSeq DNA PCR-free kit  and 550 bp inserts. The quality of DNA libraries was measured using 2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA, USA. The Whole Genome Sequencing (WGS) was performed on the Illumina NovaSeq 6000 platform using 150 bp paired-end reads, yielding a mean depth of coverage of 35.26X in the cohort.p-value for HWE test > 0.0001, Minor Allele Frequency > 0.001, and being biallelic. Filtering was performed using PLINK 1.9 [The quality of sequenced reads was assessed using FastQC v0.11.7 (github.com/s-andrews/FastQC) and reads were subsequently mapped to the GRCh38 human reference genome using Speedseq framework v.0.1.2 , encompaLINK 1.9 .The original classification of the infection outcome was divided into five categories: control, resistant, benign, mild, and severe. However, in GWAS, binary phenotypes were defined. For the analysis of the resistance to COVID-19 infection  the resistant group was coded as “1”, the control group was removed from the analysis, and all the other groups were coded as “0”. For the analysis of the susceptibility to severe COVID-19 infection  the severe group was coded as “1”, the control group was removed from the analysis, and all the other groups were coded as “0”.ith SNP of jth individual, ith SNP, and The mixed linear model, simultaneously fitting effects of all 9,767,423 SNPs for the 1076 individuals, was applied for GWAS. In this model the genetic similarity between individuals was partitioned into a component that is due to the variation in SNP genotypes among patients and a “rest” component, that was not assessed by SNPs and thus represents a pure polygenic part of the total phenotypic variation. The model is given byith SNP v=1−ksoftware , while tsoftware  and PythSNPs were annotated to genes and KEGG pathways using David software . The dowK represents the number of genes shared between KEGG i and j, while N represents the total number of genes involved in KEGG i and j. Variance components were assumed as known, amounting to The estimation of KEGG pathway effects was based on the following mixed linear modelTo maximise the computational performance of the estimation the Allograft rejection pathway (hsa05330) was significant for the resistance to the COVID-19 infection;(2)27 SNPs marking genes constituting the Allograft rejection pathway, and the majority of these were located on chromosome 6 (19 SNPs), while the remainder were mapped to chromosomes 2, 3, 10, 12, 20, and X.(3)the Allograft rejection pathway comprises several immune system components crucial for the self versus non-self recognition, and also the components of antiviral immunity;(4)no significant metabolic pathway was indicated in the case of susceptibility to COVID-19 and its severe course.Here, we used GWAS analysis to identify metabolic pathways that are associated with a severe COVID-19 infection as well as pathways related to resistance to COVID-19. The main finding of this study were:Our study showed that not only are single variants important, but also the cumulative impact of several SNPs within the same pathway seems to matter in the case of severe COVID-19 disease. This evidence supports the notion of a multifactorial background of a disease course. Moreover, as previously indicated by us and others, accumulating evidence suggests inborn errors of the immune system components, especially IFN and TNF superfamily members, are crucial when it comes to the COVID-19 severe course.


Bladder cancer with similar diagnosis based on traditional classification exhibits different behaviors and therapeutic outcomes. Thus, circulating tumor cells (CTCs) represent a more accurate approach to investigate bladder cancer features. Our results demonstrate that risk score based on EGFR, TRPM4, TWIST1, and ZEB1 four-gene signature in CTCs is markedly and undoubtedly associated with recurrence, suggesting an innovative and non-invasive strategy to manage both non muscle invasive and muscle invasive bladder cancer progression without the necessity of repetitive and onerous cystoscopies.p < 0.050). Overall, we identified a new prognostic signature that directly impacted the prediction of recurrence, improving the choice of the best treatment for BC patients.Bladder cancer (BC) is one of the most expensive lifetime cancers to treat because of the high recurrence rate, repeated surgeries, and long-term cystoscopy monitoring and treatment. The lack of an accurate classification system predicting the risk of recurrence or progression leads to the search for new biomarkers and strategies. Our pilot study aimed to identify a prognostic gene signature in circulating tumor cells (CTCs) isolated by ScreenCell devices from muscle invasive and non-muscle invasive BC patients. Through the PubMed database and Cancer Genome Atlas dataset, a panel of 15 genes modulated in BC with respect to normal tissues was selected. Their expression was evaluated in CTCs and thanks to the univariate and multivariate Cox regression analysis, EGFR, TRPM4, TWIST1, and ZEB1 were recognized as prognostic biomarkers. Thereafter, by using the risk score model, we demonstrated that this 4-gene signature significantly grouped patients into high- and low-risk in terms of recurrence free survival (HR = 2.704, 95% CI = 1.010–7.313, Log-rank  Bladder cancer (BC) is the tenth most common cancer worldwide and its incidence is gradually increasing, particularly in developed countries . The AmeSeveral efforts are being made to improve the understanding of these types of tumors with the aim to better stratifying patients to reduce or eliminate the risk of recurrence and progression. Primary BCs are molecularly and clinically heterogeneous ,9,10,11.Scientific discoveries have demonstrated the relevance of circulating tumor cells (CTCs). CTCs are cancer cells that have been detached from the primary tumor mass and are wiped out by the circulatory or lymphatic system . In seveDetaching from the primary tumors, CTCs lose the biochemical and biophysical signaling from the extracellular matrix and, consequently, start to be subjected to several changes. The shape changes that occur in CTCs are driven by cytoskeletal remodeling and the resulting stretching of chromatin may influence gene expression ,32. In aTherefore, the aim of this paper was to identify a specific gene signature based on CTC analysis that is able to molecular subtype patients to better guide therapeutic decision by using a non-invasive sampling. Starting from the knowledge of the literature, we focused the analysis on a selected panel of genes involved in BC growth and progression that also included members of the transient receptor potential channels (TRPs). These are calcium permeable channels, localized both in the plasma membrane and in the intracellular compartment known to be responsible for tumorigenesis and progression through the regulation of tumor invasion and metastasis in several cancers . http://www.pubmed.com/, accessed on 10 September 2018) search conducted in 2018 using the keywords ‘human bladder cancer’ combined with ‘mRNA biomarkers’ and ‘overall survival’ resulted in 141 hits and in 86 hits for ‘genitourinary cancer’ with ‘transient receptor potential channel’. All research articles were written in English, dating back to the maximum year of publication of 2010, and the evaluated biomarkers involved in bladder cancer growth and progression were included. In vitro and in vivo studies concerning human samples were also included. Studies with limited information or with an inaccurate number of samples were excluded. The validity of the articles, after applying the inclusion and exclusion criteria, were assessed by three researchers .A NCBI PubMed  with OS available for 404 patients. The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets were screened and the accessible transcriptomic data of patients diagnosed with BC were obtained , at the Urologic and Andrologic Clinics, University of Perugia, Perugia, Italy. Moreover, to improve the clarity/soundness of the results, the exclusion criteria were: another histological type of cancer, had received neoadjuvant or adjuvant chemotherapy, had a medical history of the same or another type of cancer, and had received a T0 stage classification. Enrolled patients had a clinical and radiological evidence of BC and did not undergo systemic/locally therapy before and after the TURB and the blood sample collection. The diagnosis was confirmed by histological analysis. The patient data regarding the age, tumor stage, histological type, regional lymph node involvement, and number of metastatic sites were also tabulated and statistically analyzed . Local wn = 5) were age-matched men without BC who were recruited for normal tissue specimens according to the Ethics Committee of the University of Perugia-approved protocol. Moreover, fresh samples (n = 8) of primary tumors were recovered for comparative analyses.Blood samples from 53 BC patients were collected and analyzed between March 2018 and March 2020. Healthy donors . The use of patient data and CTCs for research purposes at the University of Perugia were executed pursuant to the Italian legislation and international standards. Anonymous written informed consent was obtained from each participant or their guardians prior to the study enrolment.® Blood Collection Tubes EDTA  on the day of surgery. Blood was obtained at the middle of vein puncture after the first 2 mL of blood was discarded to avoid contamination by normal epithelial cells. Peripheral blood was immediately processed within 3 h by means of ScreenCell devices , as previously described [For CTC detection, 7 mL of peripheral blood from each patient was collected into Vacutainerescribed , to isolescribed  as well escribed . Seven blood samples from healthy donors were obtained from the blood bank to establish the normal expression of each marker in the white blood cells as previously described . In addition, fresh BC tissue samples (biopsies), obtained during TURB from eight patients, were used in the study.A Single Shot Cell Lysis Kit  was employed to extract the total RNA according to the protocol. Messenger RNA from biopsies was extracted by the RNeasy Mini Kit . Messenger RNAs from five human normal bladder samples (NHB) were purchased from AMS Biotechnology .The iScript Advanced cDNA Synthesis Kit (BioRad) was employed to retrotranscribe RNA and the SSOADvanced PreAmp Kit and PrimePCR PreAMP Assays (BioRad) were used to preamplify the cDNA including all of the primers used in the gene expression analysis.The ddPCR was performed by using the ddPCRSupermix for Probes (No dUTP) (BioRad) and the specific PrimePCR™ ddPCR™ Expression Probe Assays conjugated with the FAM or HEX fluorescent dyes (the same pool used in the preamplification step) (BioRad). The following target genes were analyzed: Baculoviral IAP Repeat Containing 5 (BIRC5), cadherin 11 (CDH11), epidermal growth factor receptor (EGFR), epithelial cell adhesion molecule (EPCAM), keratin 18 (KRT18), secreted phosphoprotein 1 (SPP1), transient receptor potential cation channel subfamily A member 1 (TRPA1), subfamily C Member 1 (TRPC1), member 3 (TRPC3), member 6 (TRPC6), subfamily V member 6 (TRPV6), subfamily M member 4 (TRPM4), tenascin (TNC), Twist family BHLH transcription factor 1 (TWIST1), vascular endothelial growth factor A (VEGFA), vimentin (VIM), and zinc finger E-box binding homebox 1 (ZEB1). QuantaSoft Software (BioRad) was used to analyze the data, expressed as copies/µL and normalized to a ß-actin concentration. ddPCR analysis was also performed to analyze the CD3D, CD19, CD45, ICAM1, CD41, CD235a, and Beta2-microglobulin gene expression in BC samples, given that some of the aforementioned transcripts could also be expressed, although at low levels, in normal blood cells. The results obtained from BC patients were compared with those obtained from the peripheral blood mononuclear cells from healthy donors and used them as the negative threshold. Gene expression was shown as fold respect to a calibrator (=1). ® 594, 1:100, Abcam, Cambridge, UK) were used to stain the CTCs. PureBluTM DAPI  labeled the nuclei. C2 Plus confocal laser scanning microscope  and NIS Element Imaging Software (Nikon Instruments) were used for the acquisition and processing of data. Epithelial CTCs required having a DAPI-positive nucleus with a diameter of 4 μm, cytokeratin staining surrounding 50% of the nucleus, and no CD45 expression.CTCs were fixed in 4% paraformaldehyde, permeabilized with phosphate buffered saline (PBS) plus 0.2% Triton X-100 and blocked for 60 min with 1% bovine serum albumin in PBS. Mouse anti-human pan-cytokeratin (C11) Ab , anti-human CD45 Ab , anti-human TWIST1 Ab , anti-human EGFR , anti-human TRPM4 , and anti-human ZEB1 , followed by corresponding IgG Abs  and grown at 37 °C in a 5% CO2 incubator, was used as the positive control.The InCTC assay was performed starting from blood diluted with PBS plus 2% fetal bovine serum (FBS), processed with Ficoll-Paque premium gradient medium . The monolayer containing the CTCs and leukocytes was then collected and washed with RPMI medium without serum. A micro-Boyden transwell chamber  with 8-μm-diameter pore inserts coated with Matrigel was exploited to assess the migration ability of the CTCs. The CTCs were seeded onto the upper chamber in the medium without serum, while 10% FBS was added to the lower chamber. The chambers were then incubated in a humidified incubator with 5% COhttps://string-db.org, accessed on 22 October 2018) database, based on known and predicted PPIs, was employed to seek potential interactions between the markers [Homo sapiens”, and an interaction score >0.7 were considered as active interaction sources and applied to construct the PPI networks. The PPI network was visualized by Cytoscape software version 3.6.1.  In the networks, proteins are schematized as nodes and interactions as edges. The search tool for retrieval of interacting genes (STRING)  and low-risk (0) groups, and Kaplan–Meier and receiver operating characteristic (ROC) analyses were performed.The univariate Cox proportional hazard regression model was used to assess the association of gene expression in CTC with the recurrence free survival (RFS) of patients included in the cohort. Genes with The quantitative variables are expressed as the mean values ± standard deviation (SD) or as the median values (interquartile range). Qualitative variables are expressed as absolute and relative frequencies. For the comparisons of the proportions, categorical data were tested by the Fisher’s exact test. t-test. The correlation matrix was analyzed by the Spearman correlation test.The differential expression gene (DEG) analysis was performed by applying the Mann–Whitney U The Cox proportional hazard regression model was used to perform the univariate and multivariate survival analyses. Only statistically significant variables in the univariate analysis were incorporated into the risk score equation. To assess whether the risk score was independent of other clinical characteristics, multivariable Cox regression analysis and stratification analysis were used.The differences in the recurrence time of BC patients stratified by low- and high-risk according to gene expression in CTCs was assessed by the Kaplan–Meier method. The RFS was considered as the time from the TURB surgery to the date of the first recurrence or, if the recurrence of tumor was not present, the date of the last follow-up. The statistical significance of the observed differences between groups was determined by the log-rank test. The grading variable was dichotomized by the time-dependent ROC curve analysis. The cutoff point estimated by the ROC analysis indicates the distinguishing characteristic of the grading variable used to classify participants into the recurrence group. The accuracy of the dichotomous results was calculated through the area under the ROC curve (AUC). p-values were two-sided, and p ≤ 0.05 was considered to indicate statistical significant.All testing was carried out using GraphPad Prism 9.1 software  or SPSS  or R . All Analysis of the PubMed search highlighted several bladder-specific transcripts implicated in BC growth and metastasis. Among them, we selected BIRC5, CDH11, EGFR, EPCAM, KRT18, SPP1, TRPA1, TRPC1, TRPC3, TRPC6, TRPV6, TRPM4, TWIST1, TNC, UPK2, VEGFA, VIM, and ZEB1 as potential biomarker candidates in our investigation to develop a prognostic signature model. First, through the gene expression profiling interactive analysis (GEPIA2), we analyzed the different expression of these candidates between the normal and tumor bladder tissue A. ResultIt is well-known that CTCs acquire different features with respect to the primary tumor cells by changing gene expression . Thus, wAs shown, the BC clinical stage at the time of diagnosis ranged from the Tx to T4 stage. However, the majority of the patients had Ta stage disease. None of the patients had a distal metastasis, nor were positive nodes found at diagnosis. Recurrence was recorded in 41.4% patients. The mean follow-up period was 17.6 months, with a median of 16.8 months. The blood samples were processed by ScreenCell devices and the CTCs were detected in all patients with the exception of three. The isolated CTCs were characterized for the expression of pan-CK and for the absence of CD45, as previously described . InteresThen, through the ddPCR, we analyzed the expression of the selected markers in isolated CTCs. BIRC5, CDH11, EGFR, EPCAM, KRT18, SPP1, TRPA1, TRPC1, TRPC3, TRPC6, TRPV6, TRPM4, TWIST1, TNC, VEGFA, VIM, and ZEB1 genes were heterogeneously expressed. Furthermore, we compared the gene expression levels obtained in the CTCs, evaluated as fold changes with respect to the normal human bladder RNAs (NHB), with biopsies. A significantly increased expression of BIRC5, CDH11, EGFR, EPCAM, KRT18, SPP1, TNC, TRPC1, TRPC6, TRPM4, TRPV6, TWIST1, VEGFA, and ZEB1 was found in the CTCs with respect to NHB and, even more interestingly, compared with the biopsies . Neither TRPA1 nor TRPC3 expression levels differed statistically from NHB or the biopsies (data not shown), so we excluded them from further analysis. No cells with CTC-like features were found in the peripheral blood of healthy donors, whose WBCs were similarly analyzed, demonstrating the negligible expression of these genes (data not shown). In addition, the positive detection of BIRC5, CDH11, EGFR, SPP1, TNC, TRPC6, TRPM4, TRPV6, TWIST1, VEGFA, and ZEB1 was more present in the CTC group compared with the biopsy group . Overall, these data indicate that the increase in the expression levels of several genes involved in the processes of angiogenesis, epithelial–mesenchymal transition (EMT), and cation flux–regulation pathways is associated with the acquisition of the CTC phenotype.Then, to investigate the relationship between the marker gene expression levels in CTCs and tumor stage, we analyzed the mRNA expression levels of BIRC5, CDH11, EGFR, EPCAM, KRT18, SPP1, TRPC1, TRPC6, TRPV6, TRPM4, TWIST1, TNC, VEGFA, VIM, and ZEB1 in the CTCs from the NMIBC and MIBC patients . Our datThrough the univariate Cox analysis, we analyzed whether the biomarker expression levels of the two clusters were associated with the prognosis of BC patients. Our results showed that the RFS was significantly associated with the expression levels of EGFR, TRPM4, TWIST1, and ZEB1 belonging to the same cluster. These data outline a four-mRNA-based model to predict the RFS time in BC patients .Furthermore, Kaplan–Meyer analysis was used to validate the association between the expression of four gene candidates and the prognosis of patients. For this purpose, first, we stratified patients into high- and low-expressing, according to the ROC analysis, for the EGFR, TRPM4, TWIST1, and ZEB1 markers. As shown in To facilitate the application of identified RFS-related mRNAs in the clinical management of BC patients, the risk score for each patient was constructed with the regression coefficient from the univariate Cox analysis by using: Risk score = (3.002 × Expression EGFR + 4.514 × Expression TRPM4 + 5.360 × Expression TWIST1 + 3.590 × Expression ZEB1). Then, the patients were stratified into high- and low-risk groups according to the median risk score, which was used as the cutoff point. The Kaplan–Meier analysis showed a shorter RFS in the high-risk group with respect to the low-risk group B as alsoThe lack of an accurate classification system predicting the risk of recurrence or the progression of BC patients hinders the search of new biomarkers. Indeed, even though TURBT is the standard of care for the management of BC , its maiIn the last few years, most of the studies have focused their attention on liquid biopsy and the enrichment of CTCs, also in BC ,52,53. IThe spread of CTCs in the bloodstream represents a fundamental moment for the spread of cancer, even if it is now accepted that only a small percentage of these cells can survive in the circulation and therefore give rise to metastases . In lineThus, our aim was to identify a specific CTC gene expression profile correlated to the ability of recurrence. First, we analyzed several potential markers such as BIRC5, CDH11, EPCAM, KRT18, SPP1, TIMP2, UPK2, TRPA1, TRPC1, TRPC3, TRPC6, TRPV6, TRPM4, TWIST1, TNC, VEGFA, VIM, and ZEB1 disclosed as related to the BC primary tumor in studies published back to 2010. The expression levels of these biomarker candidates were then analyzed in the CTCs from BC patients. No differences were found in the expression by comparing the CTCs from the NMIBC with MIBC patients whereas several markers were expressed at different levels in HG with respect to the LG samples. For this reason, we investigated them in order to evaluate the relation with RFS. The univariate analysis, also supported by the Kaplan–Meyer, showed that in our study including the NMIBC and MIBC patients, four biomarkers—EGFR, TRPM4, TWIST1, and ZEB1—are strongly associated with RFS.Our data were in line with recent findings that describe the association between high EGFR expression and shorter RFS, not only in the MIBC, but also in stage T1 NMIBC patients, suggesting early stratification is essential in order to select the most appropriate targeted treatment . EncouraThe TRPM4 channel is a non-selective monovalent cationic channel expressed in several tissues including smooth muscle and urothelial cells of bladder. The TRPM4 activation leads to sodium ion influx into the cell with consequent plasma membrane depolarization and calcium signaling . It has TWIST1 overexpression is frequently detected in several cancers also including BC . Its expFinally, ZEB1, the fourth biomarkers identified in our study, was significantly overexpressed in BC in comparison with the healthy adjacent tissues. Moreover, it is associated with vasculogenic mimicry and with EMT promotion in BC . In factIdentifying the features of primary tumors is insufficient to manage the disease’s progression, as single-site tumor biopsies cannot accurately portray the extremely variable cell population in the tumor mass. In this regard, CTCs are found to be genetically different and subjected to a distinct evolutionary process than the primary tumor mass. For this reason, the non-invasive liquid biopsy and the examination of CTCs could represent a good choice to replace the tissue biopsy in order to better evaluate the optimal therapy and predict the prognosis of the patients .The results from this study demonstrated that the expression of the four-gene signature in CTCs was markedly associated with recurrence, suggesting an innovative strategy to manage disease progression without repetitive invasive procedures.In the near future, the precise stratification of patients based on the EGFR, TRPM4, TWIST1, and ZEB1 gene signature risk score obtained in CTCs could represent, immediately after the diagnosis, a strategy to better plan the clinical evolution of the patient and therefore select the best therapeutic management. Indeed, faster follow-up, early radical surgery, or systemic therapy may be approved in the very high-risk group of patients, leading to the possibility of reducing the number of cystoscopies performed that are onerous for patients and expensive for health care providers. However, we believe that it is urgent to conduct studies with larger cohorts of patients to validate this risk score in the clinical practice.


Around 40–50% of all triple-negative breast cancer (TNBC) patients achieve a pathological complete response (pCR) after treatment with neoadjuvant chemotherapy (NAC). The identification of biomarkers predicting the response to NAC could be helpful for personalized treatment. This systematic review provides an overview of putative biomarkers at baseline that are predictive for a pCR following NAC. Embase, Medline and Web of Science were searched for articles published between January 2010 and August 2022. The articles had to meet the following criteria: patients with primary invasive TNBC without distant metastases and patients must have received NAC. In total, 2045 articles were screened by two reviewers resulting in the inclusion of 92 articles. Overall, the most frequently reported biomarkers associated with a pCR were a high expression of Ki-67, an expression of PD-L1 and the abundance of tumor-infiltrating lymphocytes, particularly CD8+ T cells, and corresponding immune gene signatures. In addition, our review reveals proteomic, genomic and transcriptomic markers that relate to cancer cells, the tumor microenvironment and the peripheral blood, which also affect chemo-sensitivity. We conclude that a prediction model based on a combination of tumor and immune markers is likely to better stratify TNBC patients with respect to NAC response. BRCA1 gene mutation. Over the last decades, several gene-expression-based classifications have emerged for TNBC [Breast cancer is one of the most common malignancies among women worldwide . Up to 1for TNBC ,6,7. ThePatients with TNBC are not eligible for endocrine or HER2-targeted therapies, currently rendering chemotherapy as the most-used therapeutic option . AnthracMany researchers have already investigated whether standard clinicopathologic characteristics at baseline, including tumor size, histologic grade, histologic subtype and lymph node involvement, can predict the response to NAC, albeit with contradictory results. For example, some articles found an association between the smaller, lower-grade, non-metastasized tumors (N0) and pCR rate, whereas other articles reported no association, indicating that these characteristics cannot be solely used for the prediction of a pCR ,24,25,26Currently, no universally approved biomarker is available to predict the response to NAC. Novel biomarkers at baseline that enable the identification of good and poor responders would be critical for therapeutic decision making for TNBC patients . NowadayThe goal of this review was to provide a systematic overview of the existing literature regarding baseline biomarkers present in cancer cells, the tumor microenvironment or peripheral blood of TNBC patients and their potential for predicting the pCR rate.The databases Embase, MEDLINE and Web of Science were searched from 2010 to August 2022 for articles with the use of the controlled terms TNBC and NAC . Only paTo be included in this review, the articles had to meet the following criteria: patients had to have primary invasive breast cancer without distant metastases and patients must have received NAC. A total of 2045 papers were screened and the papers that focused on the prediction of a pCR were of interest. The final exclusion of papers was based on the following criteria: no results for the outcome of interest, post-NAC focus only, lack of TNBC-specific results and focus on prognosis only. One duplicate article was discovered. In total, 92 papers were selected, revised and summarized for this article. Following the Preferred Reporting Items for the Systematic Reviews and Meta-analyses (PRISMA) 2020 statement, this review was written and subdivided into 4 parts: proteomic (divided into 2 sections: tumor cells and tumor microenvironment), genomic and transcriptomic features . The PRIProtein expression determined by immunohistochemistry is the method that is used mostly in daily diagnostics for tumor subtyping, because it is quick and relatively cheap. Many protein markers at baseline have been analyzed for their ability to predict the response rate to NAC.TNBC is a highly proliferative breast cancer subtype, often associated with high Ki-67 expression . The Ki-Another protein marker often investigated for its association with a pCR is the androgen receptor (AR). Approximately 65 to 85% of all TNBCs lack AR expression . MultiplERBB ontology family [p = 0.03) [The HER2 protein is a member of the y family . Signali = 0.03) . However = 0.03) . Recentl = 0.03) ,50. More = 0.03) ,49.TP53 gene, with a frequency of 84% [TP53 mutation status and the response to NAC [The most common genetic mutation in TNBC is in the y of 84% ,52. The y of 84% . Severale to NAC ,26,53,54e to NAC ,43.ErbB family of receptor tyrosine kinases [EGFR is a transmembrane glycoprotein, which is a member of the  kinases . It play kinases . Tang et kinases . Another kinases . Further kinases . These cFor VEGFR2, vimentin and HAGE, a higher expression of these individual markers was associated with a higher likelihood of achieving a pCR ,57,58. VFinally, for a number of other tumor-related biomarkers, such as GCS, CYP1A1, SOX10, GATA3, NF-κB, p63, CK5, CK5/6, CK14 and CK17, no association was found across several studies between expression levels and pCR rate ,40,41,56The tumor microenvironment is of great importance for the survival, growth and metastasis of breast cancer cells . SeveralTumor-infiltrating lymphocytes (TILs) are lymphocytes that are present in and around the invasive tumor and they play a role in the tumor microenvironment where they can mediate adaptive immune responses against the tumor ,70. OverZooming in on TILs, specifically high levels of CD8+ cells and a high CD8/CD4 ratio were reported to be associated with achieving a pCR ,93,94,95Recent reports have indicated that programmed cell death protein ligand 1 (PD-L1) expression can be used as a predictive marker of pCR rate ,99,100. CD73 plays a role in the catabolism of extracellular ATP to adenosine and is upregulated in regulatory T-cells in response to adenosine signaling . It is hTumor-associated macrophages (TAMs) play an important role in tumor growth, angiogenesis, metastasis and treatment resistance. A high infiltration of CD163+ TAMs has been correlated with lower pCR rates after NAC ,93. In aFurthermore, recent research suggests that Ki-67 expressed on immune cells could also be important for immune oncology benefits .Besides lymphocytes and macrophages, other immune cell subpopulations show aberrant abundance in TNBC. Neutrophils are known to promote tumor cell proliferation, angiogenesis and distant metastasis . NormallFrequencies of circulating immune cells are also linked to the response to NAC. The examination of circulating immune cells is simple, less invasive and less expensive compared to obtaining tissue-based immune parameters. A significant association was reported between a low NLR in circulating blood and the ability to achieve a pCR in several studies ,107,108.Genetic mutations in the DNA of the tumor cell have extensively been analyzed for their role in the response rate.TP53 mutation, other genes are also often dysregulated in breast cancer. For example, the PIK3CA gene is mutated in 16% of primary TNBCs and mutations in PIK3CA reduce the dependency of tumor cells on growth factors, promoting cell growth and transformation [PIK3CA-enhancing mutation are less likely to achieve a pCR compared to PIK3CA wild-type tumors, indicating that PI3K inhibitors could possibly enhance chemotherapeutic sensitivity [PIK3CA mutation and NAC regimen.Besides the common ormation . Researcsitivity ,111,112.BRCA1 or BRCA2 germline mutation and BRCA1 mutation carriers are more likely to develop TNBC [BRCA mutation carriers or not, are treated with NAC. The pCR rate has been reported to be between 35% and 70% for TNBC patients with a BRCA mutation, receiving the standard NAC regimen [BRCA1 mutation achieved a higher pCR rate than patients without the BRCA mutation [BRCA1 mutation carriers and non-carriers. However, several other studies did not find a statistically significant difference between the pCR rates of good versus poor responders based on the germline or somatic BRCA1/2 mutation status [On average, 10% to 15% of all TNBC patients carry a lop TNBC . BRCA1/B regimen ,115. Duemutation . Howevern status ,117,118.BRCA1 methylation levels are higher in patients with a pCR than those without a pCR [ATM are involved in breast cancer susceptibility. It was found that there were lower levels of DNA methylation in the ATM gene in cases with a pCR than in cases with a poor response. Another study by Meyer et al. reported nine differentially methylated regions to be associated with the response to NAC [Watanabe et al. found that on the epigenetic DNA level, ut a pCR . Germline to NAC .BRCA mutation status, homologous recombination deficiency itself has also been associated with pCR rates. This group of tumors is also called BRCAness or BRCA-like. These sporadic cancers share phenotypic overlap with BRCA1/2 germline-mutated tumors. However, these patients lack a detectable germline mutation but do have a homologous recombination error. In contrast to the positive predictive value of the BRCA mutation status, Akashi-Tanaka et al. reported that those with non-BRCAness tumors achieved a pCR more often than those with BRCAness tumors [BRCA patients, homologous recombination deficiency was correlated with a higher pCR rate. A study by Huang et al. found that the mutation status of 10 DNA repair genes involved in homologous recombination could predict the response to NAC [Besides the s tumors . On the s tumors . Moreovee to NAC . Tumors TOP2A) is a nuclear DNA-binding enzyme which is critical for obtaining a relaxed DNA structure, important for replication [TOP2A are inconsistent. No significant correlation between TOP2A overexpression and response rate to NAC was reported by Sakuma et al. [TOP2A showed a decreased pCR rate compared with tumors without amplification of TOP2A [TOP2A expression were more likely to achieve a pCR [Topoisomerase IIa  and two coding genes (WDR72 and GADD45A) could significantly distinguish between patients achieving a pCR and non-pCR [GADD45A is involved in growth arrest and DNA damage pathways [TCF3, CREB1 and CEP44) and two lncRNAs (NR 023392.1 and NR 048561.1) could predict the pCR to NAC [TCF3 is part of the Wnt pathway-associated TCF/LEF transcription factor family and TCF3 is upregulated in cancers where it promotes proliferation and metastasis. CREB1 is a DNA-binding protein which stimulates transcription and it is also involved in tumor proliferation and metastasis [Some studies developed a gene signature consisting of coding genes and/or long non-coding RNAs (lncRNA), to predict the response to NAC. It is noteworthy that half of the genome is transcribed through lncRNAs, which are RNA transcripts of more than 200 nucleotides without coding capacity. Normally, lncRNAs are regulatory molecules that have been associated with the pathology of breast cancer. A study by Wang et al. investigated whether a specific RNA signature, based on lncRNA and coding gene expression, could predict the pCR rate and demonstrated that a response score signature consisting of one lncRNA  models investigated in different studies have also been reported to be predictive for pCR rate ,95,127. TMBS15A, SIRT5, SPARC, LAG-3, SFRP1, CCND1, SCD5, ILF2, IDO1, CTLA4, NFKB1, MAPK1, TRAF1, CXCL13, CXCL16, GZMK, IL7R, CR2, CD19, MS4A1, IL33 and MELK are associated with a higher likelihood of achieving a pCR [LAG-3, IDO1, CTLA4, CXCL13, CXCL16, GZMK, IL7R, CD19 and IL33 have reported immune functions, and their putative relation with a pCR is in line with findings on the immune profile, as discussed above. Patients with low expression levels of ITPKC and HO-1 and a lack of PCDH17 methylation showed higher pCR rates [DEFB1, DEFB103A, DEFB4A and FCAR was seen in patients who failed to achieve a pCR [IDO1, LAG3, STAT1 and GZMB did not show an association with pCR rate [Furthermore, higher expression levels of ng a pCR ,131,132.CR rates ,27,32. Fve a pCR . These fve a pCR ,107,108.ve a pCR . Echavarve a pCR ). The bapCR rate .MicroRNAs (miRNAs) are considered to influence the development and progression of breast cancer and are small non-coding RNAs of 25 nucleotides in length. Gene expression can be negatively regulated by miRNAs by inhibiting the translation or degradation of the target messenger RNA (mRNA). Lately, the role of several microRNAs in the chemo-resistance of TNBC tumors and their predictive value for the rate of response to NAC has been studied extensively. The miR-143-5p, miR-30a, miR-9-3p, miR-770 and miR-18a showed differential expressions between good and poor responders to NAC ,135,136.Exosomes are small membranous vesicles of around 30 to 100 nanometers that can contain lipids, proteins or miRNAs secreted by cells into the blood . ExosomaThis review focused on comprehensively charting and describing biomarkers at baseline that could predict a pCR following NAC treatment for TNBC, on proteomic, genomic and transcriptomic levels. Various biomarkers have been investigated for their correlation with pCR rate . DespiteBased on this review, it has become clear that the immune microenvironment is critical for the response to NAC. The higher density of TILs, particularly CD8+ T cells, has been shown to be associated with achieving a pCR, indicating the predictive value of TILs as reported in a total number of 18 articles. The favorable involvement of CD4+ T regulatory cells and the unfavorable involvement of CD163+ TAMs and CD73+ tumor cells were also presented to correlate with the pCR rate. Thus, the pre-treatment activation of the CD8+ T cells against TNBC or their control via adaptive feedback mechanisms (such as evidenced by Tregs and TAMs) likely positively influences the response to NAC. The presence of CD8+ T cells is known to induce apoptosis of cancer cells, and CD4+ T cells are known for their helping function in maintaining the anti-tumor role of CD8 . ChemothPIK3CA seems to be the only biomarker that has shown an association with a pCR in multiple articles. Furthermore, various biomarkers have been found to be associated with a pCR but have only been reported infrequently. For these biomarkers, additional research is needed to validate the association with a pCR.On the tumor cell level, Ki-67 and AR seem to be fairly robust predictive markers of a pCR. Nevertheless, for Ki-67, the results reported are somewhat contradictory, but there were more studies (n = 7) that found an association between a high Ki-67 expression and pCR rate compared to the studies that did not find a significant association (n = 5). In addition, the studies reporting an association were based on larger cohorts compared to the studies without an association. On the genomic level, To our knowledge, this is the first systematic review giving an exhaustive overview of all reported biomarkers of a pCR in NAC-treated TNBC at the proteomic, genomic and transcriptomic levels. This review was written as a narrative  approach aiming to achieve a better understanding of the potential biological effects of individual markers on the pCR rate. A quantitative approach meta-analysis was not possible because of the heterogeneity of the articles in this search. The studies varied in biomarkers, patient populations, sample size, treatment regimen and other study settings, which makes it difficult to generalize the results found in these studies. Moreover, a lack of standardized threshold settings for immunohistochemical stains and scoring methods, and peripheral blood cell counts, increases the variety between the studies.Most of the studies included in this review followed the ASCO/CAP guidelines for ER and PR scoring, using a cut-off of <1%. However, nine studies used a cut-off of <10% for an ER negative status ,122,130.Currently, treatment decisions are based on baseline prognostic clinicopathologic features, so the additional value of predictive biomarkers should be studied on top of this. Given the rapid developments in the field of neoadjuvant treatment of TNBC patients, including the addition of neoadjuvant pembrolizumab, it becomes even more important to select those patients that could be treated with chemotherapy alone, to keep the health care system affordable. In addition, TIL-high TNBC patients have been reported to have a good prognosis without any systemic treatment . TherefoA clinical useful predictive biomarker is analytically valid, reproducible and able to select patients with a high or low chance of a pCR after NAC (for example >90% or <10%) ,147,148.In conclusion, several markers have been associated with the pCR rate, with immune markers being the most promising. However, several other markers that relate to cancer cells, the tumor microenvironment and the peripheral blood also affect chemo-sensitivity. The combination of tumor cell and immune markers, such as Ki-67 expression and TIL density, in a prediction model is likely to yield the best stratification of TNBC patients with respect to response to NAC.