Mitochondrial DNA (mtDNA) has been used for decades as a predominant tool in population genetics and as a valuable addition to forensic genetic research, owing to its unique maternal inheritance pattern that enables the tracing of individuals along the maternal lineage across numerous generations. The dynamic interplay between evolutionary forces, primarily genetic drift, bottlenecks, and the founder effect, can exert significant influence on genetic profiles. Consequently, the Adriatic islands have accumulated a subset of lineages that exhibits remarkable absence or rarity within other European populations. This distinctive genetic composition underscores the islands’ potential as a significant resource in phylogenetic research, with implications reaching beyond regional boundaries to contribute to a global understanding. In the initial attempt to expand the mitochondrial forensic database of the Croatian population with haplotypes from small isolated communities, we sequenced mitogenomes of rare haplogroups from different Croatian island and mainland populations using next-generation sequencing (NGS). In the next step and based on the obtained results, we refined the global phylogeny of haplogroup N1a, HV2, and X by analyzing rare haplotypes, which are absent from the current phylogenetic tree. The trees were based on 16 novel and 52 previously published samples, revealing completely novel branches in the X and HV2 haplogroups and a new European cluster in the ancestral N1a variant, previously believed to be an exclusively African–Asian haplogroup. The research emphasizes the importance of investigating geographically isolated populations and their unique characteristics within a global context. Isolation, whether geographical or cultural, has always had an impact on the population genetic structure, and it is mirrored in the reduction of genetic diversity due to genetic drift and a higher rate of marriage in consanguinity due to the limited relation options . These fhttps://empop.online/, accessed on 28 June 2023). However, the number of complete mitogenomes in EMPOP (around 4000) is still insufficient and lacks contributions from Croatia [Mitochondrial DNA is also extensively used in forensic casework to characterize biological evidence based on its specific features, such as high copy numbers within cells and the small size of the mitochondrial genome, also very useful in analysis of ancient human remains. The currently available forensic mtDNA database is the EMPOP database, specifically developed for forensic applications, and comprising around 48,500 quality controlled mitotypes  ,23 by usThe initial complete sequencing of 9 mitochondrial genomes was performed at the Estonian Biocentre, Institute of Genomics, Tartu, Estonia using Sanger sequencing. Sequencing was performed on the Applied Biosystems 3730xl DNA Analyzer, Thermo Fischer Scientific, Waltham, MA, USA) with the BigDye Terminator sequencing kit , according to the previously published methodology ,25.n = 32) were performed at the Forensic Science Centre “Ivan Vučetić”, Zagreb, Croatia according to Illumina 2016 Protocol, with the modifications described previously [® GXL DNA polymerase in two amplicons for each sample. The conditions of the optimized protocol for both fragments were as follows: 9.1 kb fragment: 25× ; and 11.2 kb fragment: 25× . Both mtDNA amplicons were then quantified on a QubitTM 3.0 Fluorometer (Thermo Fisher Scientific) with the QubitTM dsDNA High Sensitivity kit. Both mtDNA amplicons were normalized to equal concentrations of 0.2 ng/μL, as described in the Illumina® protocol, and pooled for each sample, wherefrom the volume of 5 μL was taken for library preparation . Nextera® XT Library Prep Kit was used according to the Illumina® protocol, and negative controls (reagent blanks) were used in the target enrichment (NC-PCR) and library preparation (NC-LIB) procedures [® DNA High Sensitivity Assay on LabChip® GX Touch HT  and were diluted to concentrations of 0.2 ng/μL. The libraries normalized to 4 nM were pooled, denatured, and diluted to 12 pM, with 5% PhiX Sequencing Control v. 3  spike-in (Illumina® Denature and Dilute Libraries Guide for MiSeq System). The paired-end sequencing of all 32 samples was performed within a single run on an Illumina® MiSeq FGxTM instrument by using the MiSeq® Reagent Kit v.2, 300 cycles (2 × 151 bp). The run quality metrics were reviewed in Illumina® Sequencing Analysis Viewer (SAV) v.1.11.1 software: a cluster density of 1494 K/mm2, with 87.2% of clusters passing the filter was reported, and 78.8% of the sequenced bases exhibited a base call quality score (Q) above 30.Target enrichment, library preparation, and sequencing of the selected subsample  were mapped to the rCRS (NC_012920) [The raw fastq reads generated by the MiSeq_012920) ,22 using_012920)  was used_012920) . LeftAli_012920) , with a _012920) ,34. The _012920) . Computa_012920) .Coalescence time estimates were computed with the Bayesian MCMC approach implemented in the BEAST v1.7.5 suite of software . All anahttps://sites.google.com/site/mtphyl/home, accessed on 23 February 2023), and the tree topologies were subsequently manually verified, by using all publicly available sequences from the current literature and Mitomap up to March 2023. The trees of the complete mtDNA sequences (52 published and 16 novel ones—9 from our database + 7 sequences from the NGS dataset) were rooted to the Revised Cambridge Reference Sequence (rCRS), in order to enable comparison to the previously published data.The maximum parsimony phylogenetic tree was first constructed with the software mtPhyl 5.003 . The second cluster marked by a 4721 transition encompassed eight different complete sequences from various European regions—four samples of Russian, Greek, and Serbian origin ,11,49, j. The secIn the attempt to trace the geographical distribution of the contemporary N1a variants on a global scale and to revise the generally accepted postulates of their spatial predominance, we created world distribution map for both the N1a 16147A and 16147G variant (depicted in We also reconstructed the phylogenetic tree of the X1’3 using twelve published ,51,52,53The sequencing of two HV2 samples from the Cres Island was also performed and joined with a sample of Armenian origin from the Estonian database, sharing a similar HVSI motif. In the attempt to clarify their unusual connection, we constructed the maximum parsimony phylogenetic tree using eight complete mtDNA sequences from the literature and Mitomap ,56,57,58The Croatian Adriatic islands serve as genetic isolates characterized by the presence of atypical lineages when compared to the contemporary European population, which are more prevalent and frequent due to the influence of genetic drift and recurrent bottlenecks. Incorporating these rare mitogenomes into the EMPOP database would enhance its forensic power by facilitating the identification and matching of individuals possessing uncommon genetic profiles for a particular region. Furthermore, the identification of novel branches within certain haplogroups, such as the HV2, X1’3, and N1a1a ancestral variant, underlines the importance of exploring isolated populations and their distinctive characteristics in shaping the contemporary structure of the human population. Therefore, the objective of this study was to reconstruct the phylogenetic trees for these haplogroups by sequencing rare mitochondrial haplotypes obtained from our database. This research provides valuable insights into the genetic diversity and evolutionary history of these specific lineages, contributing to our understanding of the broader human population structure.The west-Eurasian mitochondrial haplogroup diversity encompasses haplogroups mainly derived from haplogroup R, the daughter branch of the macrohaplogroup N. The estimated origin of haplogroup N was most likely in the Arabian Peninsula around 55–65 kya, shortly after the Out-of-Africa migration, where the derivation into the R clade took place around 59 kya . West EuThe African/South Asian branch, characterized by the 16147G mutation, was believed to be more common in the Arabian Peninsula, northern Africa, and the Near Eastern region and very rare, or even absent in Europe ,67,68. IHaplogroup X was estimated to originate in the Near East ca. 30 kya. According to the current nomenclature it is divided into four subclades denoted as X1–X4. The X2 subclade encompasses by far the largest part of the X tree, marked by both the highest frequency and diversity of all X lineages scattered worldwide It is geographically distributed among West Eurasians, northern groups of Native Americans, as well as in northern Africa and the Near East, but with low frequency, around 1–2% ,16. SomeSimilar interesting trace of a long-distance migration from Near/Middle East in the Adriatic gene pool was found in the HV2 haplogroup. Haplogroup HV is predominantly present in the Near East, Middle East, and in the Caucasus, while in Europe it is spread unevenly—it is rare or absent in the north and west, but more common among southern and eastern Europeans . A revisThe identification of a previously unknown European cluster within the African/South Asian N1a 16147G branch suggests the emergence of a new founder lineage likely originating locally within European territories, potentially predating the Neolithic period. Additionally, the wider distribution of N1a 16147A than previously reported in the literature suggests a need for a revision of the complete N1a phylogeny. Furthermore, the detection of rare mitochondrial lineages, such as X* and HV2b, indicates the presence of signals with origins in the Near/Middle East within the Adriatic gene pool. These findings highlight that small human genetic isolates can serve as reservoirs of population variability, offering insights into prehistoric migratory events that have played a significant role in shaping the maternal genetic landscape on a larger scale.


All sequences of the nad1 gene obtained during the present study were assembled and aligned. The standard statistics for haplotype composition, i.e., the number of haplotypes, haplotype diversity, nucleotide diversity, and average number of nucleotide differences, were calculated. Haplotype analysis and visualization of haplotype frequency among populations were performed using a median-joining network.A total of 133 specimens of I. melis isolated from the American mink and of the striped field mouse do not differ significantly. The median-joining network showed that the three main haplotypes are in the centre of a star-like structure, with the remaining haplotypes as the satellites, reflecting the recent expansion of the populations.Based on the samples collected from different localities in Poland, our study revealed that the overall genetic diversity of I. melis isolated from the American mink and striped field mouse reveals a high level of homogeneity. Moreover, regional differences in the food composition of the definitive hosts play an important role in shaping the genetic structure of the trematode populations.The overall genetic diversity of  Isthmiophora Lühe, 1909 constitutes a group of 27- and 29-collar-spined echinostomes )examined . In the examined , 29–32);I. melis, combined with the various aspects concerning body size or physiological conditions of its hosts , may cause the parasites to display a clearly visible phenotypic plasticity [I. melis can be even higher than in a typical host such as American mink  or can attain a similar value . Of course, these studies also could have been performed with the application of other markers, such as single-nucleotide polymorphism (SNP) or microsatellites. However, since the use of the nad1 mtDNA marker showed intra- and inter-population differences, it can be concluded that the chosen methodology was sufficient. The material used in our study was also used in the research conducted by Chibowski et al. [I. melis. The results support the hypothesis that in trematodes with an allogenic life cycle, the biology of the host shapes the genetic structure of the parasites. When voles—i.e., not a potential source of I. melis infection—dominated the diet of American mink, the genetic diversity of the parasite was at a low level. In contrast, when the diet was dominated by amphibians and, to a lesser extent, fish, the intra-population genetic diversity of I. melis increased. Thus, regional differences in the host diet seem to affect the genetic structure of the parasite population. Similar results were observed in studies by Sharrard-Smith et al. [Lutra lutra) and mink. The authors showed that differences in the phylogenies between the two species Metorchis bilis and Pseudamphistomum truncatum may suggest divergent demographic histories, possibly reflecting contrasting host diets. It is also worth emphasizing that the correlation between the host's diet and the presence of parasites is an important element necessary to understand the functioning of the food web in the ecosystem.As we reported previously, the wide host range of asticity , or altei et al. , where th et al. , who evaI. melis isolated from the American mink and striped field mouse do not differ. The haplotype distribution analysis showed recent expansion of the populations, where the biology of the definitive host plays an important role in the genetic structure of the parasite. Finally, the results confirm that the genetic structure of the I. melis population is related to the diet composition of the definitive host.Our study showed that the overall genetic diversity of


Greenspace plays a crucial role in urban ecosystems and has been recognized as a key factor in promoting sustainable and healthy city development. Recent studies have revealed a growing concern about urban greenspace exposure inequality; however, the extent to which urbanization affects human exposure to greenspace and associated inequalities over time remains unclear. Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping and a population-weighted exposure framework to quantify the changes in human exposure to greenspace and associated equality (rather than equity) for 1028 global cities from 2000 to 2018. Results show a substantial increase in physical greenspace coverage and an improvement in human exposure to urban greenspace, leading to a reduction in greenspace exposure inequality over the past two decades. Nevertheless, we observe a contrast in the rate of reduction in greenspace exposure inequality between cities in the Global South and North, with a faster rate of reduction in the Global South, nearly four times that of the Global North. These findings provide valuable insights into the impact of urbanization on urban nature and environmental inequality change and can help inform future city greening efforts. A study of 1,028 global cities from 2000-2018 shows increased human exposure to greenspace, reducing greenspace inequality. Notably, cities in the Global South improved nearly four times faster than those in the Global North. These insights can guide city greening strategies. Currently, over half of the world’s population resides in urban areas and this number is expected to increase to 68% by 20502. While urbanization has brought about many social and economic benefits, such as improved access to public infrastructure, sanitation, and education services3, it has also resulted in severe environmental degradation, including deforestation4, habitat loss5, air and noise pollution6, and increased fossil fuel consumption and CO2 emissions7. This has led to environmental injustice, with some communities bearing a disproportionate share of the negative impact of urbanization9. Achieving equality in urban environmental sustainability is crucial, as it will help address the imbalance between social, environmental, and economic developments and reduce society’s vulnerability to risks10. This is the focus of the 11th Sustainable Development Goal of creating inclusive, resilient, and sustainable cities and human settlements11.Urban areas are the hub of human society, characterized by agglomeration, industrialization, and modernization12. Providing universal access to green and public space has been acknowledged as an important pathway in the pursuit of sustainable and healthy development goals. Using greenspace supply metrics such as greenspace total or per capita supply, previous studies have measured the greenspace supply at both the city and global scales14. Human exposure to greenspace, measured as the averaged amount of greenspace coverage within people’s nearby environment expressed as a percentage16, has also been quantified in sampled cities19. Recent research has used high-resolution satellite imagery and population data, combined with environmental exposure models, to reveal the disparities in greenspace exposure between cities in the Global North and Global South. Urban residents in Global South cities  have only one-third the exposure to greenspace compared to those in Global North cities , but experience twice the level of inequality17. However, existing studies on greenspace exposure and inequality are constrained to data from individual baseline years that cannot capture the impacts of long-term urban development21 and the gradual temporal evolution of human-nature interactions such as urban warming and greenspace management strategies23, limiting our understanding of the impact of urban development on greenspace supply, human exposure, and inequality over time.Greenspace, as a key component of urban nature, offers a wide range of ecosystem services and health benefits, making it a useful proxy for evaluating urban environmental sustainability25 the replacement of green land cover with built land cover, and the enrichment of built land cover with designed green land. This replacement often occurs during early-stage urban development via rapid urban expansion, transforming naturally vegetated areas into artificial impervious surfaces and ultimately resulting in a subsequent decrease in greenspace coverage. Historically, there is typically a process of greenspace destruction as agricultural and natural green land is replaced by impervious surfaces and then a later process of re-greening via environmental improvements. Urban expansion through comprehensive spatial planning , tends to ‘reprovision’ greenspace by design from the outset, but these spaces themselves tend to evolve  over time according to income, land value and environmental preferences27. In later periods, a highly urbanized city generates its own ecology, with factors such as urban warming and CO2 fertilization leading to an extension of the growing season and increased greenspace growth29. City management practices, such as planting street trees and creating vertical gardens, can also increase the overall greenspace supply in an urban environment developed over time under a legal environmental planning regime31. The growth of the urban population modifies the interactions between the population and the urban green environment, leading to changes in human exposure to greenspace32. Greenspace is a ‘superior good’, with demand increasing as the prosperity of citizens increases33. In contrast to a crowding effect of population growth, leading to reduced greenspace exposure, an income effect tends to increase greenspace supply, diversity, and aesthetics in wealthier cities. The two effects (destruction and construction of greenspace) can also occur periodically and are not always strictly chronological. Different parts of cities developed under different political and economic conditions often display different patterns of greenspace, for example, highly planned systems of public local parks in the post-World War II cities; intensive street-tree planting during the first half of the 20th century; private greenspaces in ancient city quarters, very little greenspace in a period of informal urban expansion37.The impacts of urbanization on greenspace exposure are of two kinds:39, limited studies have investigated the net change in greenspace supply and human exposure to greenspace across a global sample of cities for a comparable time, to identify general trends. Such trends are the net outcomes of the destructive and constructive processes often at work simultaneously in the evolution of city fabric. Inequality in greenspace exposure is an increasing concern as it can be translated into adverse effects in mental and physical health19. Individual studies show how greenspace provision and the joint effects of greenspace provision and spatial configuration control greenspace exposure inequality17. However, the drivers of changing greenspace exposure inequality over time remain unclear and limit our understanding of the relationship between greenspace and population distributions in determining and attempting to promote greenspace exposure equality in the future projection.While both destructive and constructive processes are well understood in the context of particular cities and professional urban management activities2) using 30-m-resolution Landsat satellite data from 2000 to 2018, and validate its accuracy using the 1-m-resolution National Agriculture Imagery Program (NAIP) aerial data and 10-m Sentinel-2 satellite observations. Specifically, we adopted a broad definition of greenspace to refer to land that is partly or completely covered with grass, trees, shrubs, and other vegetation32. That is, green-covered land within the curtilage of an urban boundary, since we are focusing on cities in this study. We further leveraged the 30-m-resolution satellite-derived greenspace coverage map  and 100-m population data, together with the population-weighted exposure model, to assess the spatiotemporal trends of urban greenspace exposure, inequality, and the associated drivers over the first two decades of 21st century. The equality term in this study is to quantify whether individual people share the even greenspace resource40. To this end, we included cities with different urban development stages, classified as Global North and Global South cities, by answering the following four questions. (1) What has been the fate of urban greenspace supply under global urbanization during the first two decades of the current century? (2) Has human exposure to greenspace and the inequality issue improved or worsened in this period? (3) What are the relative contributions of greenspace provision and population growth to changes in exposure and the associated inequality? (4) What can be learned from the start of the century to guide city greening in the decades ahead?Here we generate a global urban greenspace dataset for 1028 large cities  than Global South cities (−2.51 × 0.001 yr−1), resulting in an overall slightly positive change trend for all global cities (0.22 × 0.001 yr−1) (Table −1), which is twice that of Asian (−2.11 × 0.001 yr−1) and Australian and Oceanian (2.16 × 0.001 yr−1) cities. Cities in South America (−0.44 × 0.001 yr−1) and Africa (0.51 × 0.001 yr−1) experience the lowest temporal trend level, which is approximately half of that of the North American cities (0.95 × 0.001 yr−1). Interestingly, we find a turning point around the year 2011 in the temporal trajectory of annual greenspace coverage for Global North and Global South cities . Results reveal a contrasting pattern in the direction of temporal change in greenspace coverage between Global North and South Fig. . Global ies Fig. . Before ies Fig. . Our sen−1) (Table −1) than Global South cities (0.44 × 0.001 yr−1). In North American, European, African, Australian, and Oceanian cities, population distribution amplifies the share of greenspace coverage, with a greater temporal change magnitude in human greenspace exposure compared to physical greenspace coverage. An important finding is that despite a negative trend of greenspace coverage in Global South and Asian cities, people in these cities experienced a slightly positive change in greenspace exposure in the first two decades of the 21st century. Annual continental-level greenspace exposure has similar temporal trajectory patterns as greenspace coverage but shows larger variabilities across continents , almost four times that of Global North cities (−0.94 × 0.001 yr−1)  in the temporal change of greenspace exposure inequality over first two decades of the 21ies Fig. . We alsoies Fig.  and 14. 43. The unequal distribution of greenspace and its exposure is one of the pressing issues in urban sustainability and public health, especially in rapidly urbanizing Global South cities44. Greenspace, as a key component of urban nature and a direct pathway to achieving the 11th Sustainable Development Goal, is widely understood to be reduced by urbanization for individual and even global cities32. We note, however, this is not entirely true, due to greenspace being a superior economic good , the demand for which rises with income46. This gives rise to different dynamics between the Global South and North, and differences within cities of these two broad global regions.Global urbanization promotes increasing population growth and socio-economic prosperity but also enhances environmental degradation, which substantially challenges urban sustainabilityst century, the positive effects of urbanization on vegetation growth have been increasing, which may partially counteract the loss of vegetation caused by land transformation in earlier stages of urbanization, and lead to improved human exposure to greenspace25. By combining this data with global population mappings, we were able to quantify the temporal trends in greenspace exposure and inequality over the past two decades. Our results show that greenspace coverage has increased overall across global cities 55. Global South cities may need to increase green planting programs to make up for the reduction in physical greenspace coverage17. Interestingly, a turning point in the trend of greenspace provision and exposure was observed around the year 2011, when the world’s urban population exceeded the rural population56 and natural and anthropogenic activities plausibly related to a net global vegetation greening  have been enhanced47. Prior to this, cities globally, were experiencing a decline in greenspace coverage, but after 2011, the trend reversed turning to a positive increase, indicating a net greenspace provision.Prominent spatial differences in urban greenspace trends have been revealed across our sample of global cities, particularly in the contrast between cities in the Global North and Global South. Global North cities that are highly urbanized have become greener over the past two decades, the likely cause of which is more green real estate building and green urban planning and city management by both private and public developers and agenciesoss Fig. . This co57 and concurrently, greenspace coverage increases and population grows in new urban areas16. Our findings show a substantial difference in greenspace exposure change rates between cities in the Global North and Global South, with cities in the Global North experiencing approximately seven times for the increase rate of greenspace exposure compared to those in the Global South  with greenspace, and measure specific human-centric exposures. Clearly, another important factor in assessing the degree and distribution of access or exposure to greenspace is demographic structure such as gender, age, income, and race. In the present study, greenspace exposure differences across population groups are not considered, since their measurement and a technical consideration of between-group equity is beyond the scope of our methods in this study. We therefore talk in terms of equality, rather than equity of exposure and our measurements relate to human population counts abstracted from specific population sub-groups. A deeper understanding of greenspace exposure and demographic structure is becoming socially and politically urgent59, which will help identify ‘hotspot’ areas with less greenspace that disproportionately affect certain vulnerable populations, and potentially help quantify the variations in health outcomes.There are also some levels of limitations that should be acknowledged in this study. First, although the integration of 30-m-resolution Landsat satellite with a spectral unmixing approach can help resolve the sub-pixel greenspace mapping, our method is still unable to explicitly identify certain greenspace types in small and fragmented patches at the Landsat’s resolution, such as street plantation, lawns, and pocket gardens and parks, given the heterogeneous landscape of cities. Therefore, satellites with a higher spatial resolution such as 10-m Sentinel-2, 3-m PlanetScope, and sub-meter WorldView imageries can be incorporated to offer advanced observational opportunities for urban greenspace mapping, but one of the key challenges is how to develop robust spatiotemporal reconstruction algorithms for generating high-quality historical archives. Second, the spatial distributions of greenspace and population footprints within each year of this study are static without modeling spatiotemporal interactions between greenspace and humans dynamically. On the one hand, people living in cities are spatially moving in their daily routines, being exposed to different nearby green environments beyond those near to their place of residence. On the other hand, seasonal changes in urban greenspace that reflect different phenological phases will influence the greenspace availability over time. Therefore, a promising open topic for follow-up research is to integrate a human mobility dataset with dynamic greenspace observations to enable a spatially and temporally explicit human-greenspace interaction framework and move toward real-time greenspace exposure assessment. Third, a global sample of 1028 cities is adopted to investigate urban greening and greenspace exposure inequality across a large geographical spectrum, allowing us to make conclusions that transcend the local observations that limit many greenspace studies. We acknowledge, however, that the area criterion of 100 kmWe generated two-decade time-series greenspace maps from the 30-m-resolution satellite data for global large cities using the linear spectral unmixing approach. We further combined the long-term fine-scale greenspace and population mappings with a population-weighted exposure framework to explore temporal changes in greenspace coverage, human greenspace exposure, and the associated inequality. To decipher the drivers controlling the inequality in human exposure to greenspace over time, we proposed a Venn conceptual model to quantify the individual and joint contributions from greenspace and population for the past two decades. The full workflow of the research design is shown in Supplementary Fig. 60. As the high-quality year-by-year GUB datasets are lacking, we chose the GUB data in 2018 with a geographic area larger than 100 km2 as the urban boundary to allow for 1) the exploration of the urban expansion impacts on greenness change and 2) the collection of sufficient samples for measuring human greenspace exposure and inequality17. It is noted that we refer to these 1028 urban areas as ‘cities’ throughout the manuscript.We selected 1028 major urban areas globally as our study targets. The boundaries of these urban areas were extracted from the latest global urban boundaries (GUB) product61. We used the surface reflectance products of three visible  and one near-infrared band. To minimize the uncertainty caused by Landsat-7 scan line off failure62, we primarily focused on the use of Landsat-5 and Landsat-8 Collection 2/Tier 1 Level-2 products, with data availabilities of 12-year Landsat-5 (2000–2011), 2-year Landsat-7 (2012-2013), and 5-year Landsat-8 (2014–2018).We used 19-year (2000–2018) Landsat surface reflectance products from three satellite sensors  with a 30-m spatial solution to quantify the spatial distribution and temporal dynamics of greenspace. Landsat provides the longest high-quality temporal record of global surface reflectance data together with the pixel-level quality assurance (QA) layer indicating cloud, cloud shadow, snow, and ice conditions63. With the QA bitmask layer, we excluded the pixels that were contaminated by cloud cover, shadow, and snow. We further calculated the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) to quantify the spectral characteristics of green vegetation and water bodies, respectively. Finally, we adopted a maximum value composite approach to generate the annual greenest vegetation green metrics by selecting the pixel-based maximum NDVI values from the cloud-free time series within a one-year cycle. In addition to the maximum NDVI value, we also recorded the corresponding NDWI and spectral reflectance of blue, green, red, and near-infrared bands for the following fractional greenspace coverage mapping.Several data pre-processing steps are conducted to ensure high-quality inputs for greenspace mapping. We first harmonized Landsat-5 and Landsat-7 data using Landsat-8 data as a baseline to generate consistent time-series data that removes potential impacts due to the difference in spectral settings across satellite sensors64. We chose the WorldPop population density dataset for its advantages of high spatial resolution, annual update frequency, and global coverage over alternatives such as Gridded Population of the World (GPW)65, LandScan66, and High-Resolution Settlement Layer (HRSL)67.We used the WorldPop dataset from 2000 to 2018 to map the population’s spatially explicit distribution and temporal dynamics. WorldPop provides a global annual update of demographic datasets  at a 100-m spatial resolution using a random forest regression tree-based mapping approach32. The linear spectral unmixing model assumes that one pixel’s spectral signature (including reflectance and its derivative index) is a linearly weighted sum of a few spectrally pure endmembers and their fraction covers within pixel68.i, including spectral reflectance of three visible  and one near-infrared bands, NDVI, and NDWI, kth endmember, kth pixel, n is the total number of endmembers, kth endmember within pixel i, which is usually calculated from the least-squares method with the following physical constraints:We adopted the linear spectral unmixing model to map fractional greenspace coverage from the composited Landsat surface reflectance-NDVI-NDWI time series, which can capture subpixel greenspace signalsn = 3). To collect pure spectra of endmembers and minimize their annual variations, in addition to Eq.  or non-greenspace  components using a threshold approach70. We adopted the thresholds of 0.3, 0.4, and 0.5 for this classification. Then, we aggregated the greenspace binary map to 100 m for the calculation of city-level physical greenspace coverage.With the endmember spectra and the associated physical constraints, we first calculated the pixel-level fractional greenspace coverage from Eq. . To remo17. This task includes three steps. First, we generated the annual composite from Sentinel-2 and NAIP datasets. For Sentinel-2 data, we applied a maximum value composite approach to generate the yearly greenest vegetation green metric across 1028 urban cities like Landsat data. Since NAIP only collected aerial imagery during the agricultural growing season in the sampling regions of the continental United States, we first chose the summer NAIP data  of the sampled United States cities as candidates. We then excluded these NAIP candidate data whose observation dates are notably different from the peak growing season by visually comparing them with the corresponding Sentinel-2 and Landsat images. Consequently, a total of 639 United States cities with 19, 580 NAIP images were selected  aerial imagery for 2003–2015 and 10-m resolution Sentinel-2 satellite for 2016-2018 as benchmarks, following the approach in Chen et al. 32, which can model the spatial interactions between greenspace and population, to quantify the probability and level of human exposure to greenspace according to Eq. , M is the total pixel number within the city, and We adopted the population-weighted exposure framework17. The calculations of these three metrics are provided in the Supplementary materials. Three metrics range between 0 and 1, where 0 indicates absolute equality and 1 indicates absolute inequality, and increasing value means a larger level of inequality.We used three commonly used economic metrics  to measure the inequality in human exposure to greenspace for global cities following the framework and method of Chen et al. 71, to calculate the magnitude and direction of the city-level monotonic trends of physical greenspace coverage, human greenspace exposure, and greenspace exposure inequality. We adopted a significance level of 0.05 to assess the significance of time series trends.We used the non-parametric Mann–Kendall statistic and Theil–Sen slope estimator approaches, which are insensitive to data distribution and outliersi to i + 1, the individual contribution of greenspace provision , i + 1 and population i, i and population i.We proposed a Venn conceptual model to quantify the contributions of greenspace and population to the temporal changes of greenspace exposure inequality. This model originates from the widely used variation partitioning approach that attributes the variations of a response result  into different explanatory variables. As shown in Fig. i , i and population i + 1.Similarly, the individual contribution of population distribution to the overall greenspace exposure inequality , i + 1.The joint contribution of greenspace provision and population change  calculation of the monotonic trend of greenspace exposure inequality In addition to the Venn conceptual model, we adopted another empirical approachSupplementary InformationPeer Review File


Numerous studies have indicated that STAT3 plays a key role in promoting oncogenesis and it is considered a potential therapeutic target for cancer treatment; however, there are no reports on STAT3 using pan-cancer analysis. Therefore, it is important to investigate the role of STAT3 in different types of tumors using pan-cancer analysis. In the present study, we used multiple databases to comprehensively analyze the relationship between STAT3 expression and prognosis, different stages of patients with cancer, investigate the clinical value of STAT3 in predicting prognosis, and the relationship between STAT3 genetic alteration and prognosis, drug sensitivity, and STAT3 expression, to determine whether STAT3 participates in tumor immunity, to provide a rationale for STAT3 as a treatment target for a broad-spectrum malignancies. Our results indicate that STAT3 can serve as a prognostic, sensitivity prediction biomarker and a target for immunotherapy, which has been of great value for pan-cancer treatment. Overall, we found that STAT3 significantly predicted cancer prognosis, drug resistance, and immunotherapy, providing a rationale for further experimental studies. Given the current dire global cancer situation, immunotherapy is on the rise. On one hand, immunotherapy can increase the ability of the immune system to identify and eliminate cells with tumor antigens, enhancing immune-mediated anti-tumor immune responses; on the other hand, it can inhibit tumor-induced immune suppression2. Cancer immunotherapy has achieved dramatic results in patients with cancer; therefore, it is considered the third revolution in cancer treatment3. Thus, it is necessary to identify new biomarkers to prevent and treat cancer. However, statistical power and poor reproducibility limit the findings of many studies4. With the rapid development of tumor genomic technologies, cancer research has entered pan-cancer analysis. Pan-cancer analysis refers to the study of tumors of a variety of cancer types using genomics technology, finding common features across diverse cancer types, understanding tumor progression, and providing a new and promising therapeutic avenue for the treatment of a broad spectrum of cancers5.Currently, cancer is the first or second leading cause of death and is one of the leading disease-related causes of death worldwide. With the aging population, the morbidity and incidence rates of malignant tumors are increasing every year6. In recent years, studies have shown that aberrant STAT3 phosphorylation has been implicated in malignant transformation of cells, mediating tumor initiation and progression, and is therefore considered a proto-oncogene7. It has been shown that aberrant expression of STAT3 has tumor-promoting effects in many cancers, such as squamous cell carcinoma8, thyroid cancer9, and non-small cell lung cancer 10, which is considered a significant hub in tumorigenesis. Similarly, in the tumor immune response, STAT3 phosphorylation can promote evasion of immune surveillance11, and inhibition of STAT3 phosphorylation can suppress evasion of immune surveillance12. Numerous studies have indicated that STAT3 plays a key role in promoting oncogenesis, and it is considered a potential therapeutic target for cancer treatment; however, there are no reports on STAT3 using pan-cancer analysis. Therefore, it is important to investigate the role of STAT3 in different types of tumors using pan-cancer analysis.Signal transducer and activator of transcription 3 (STAT3) is a member of the STAT protein family that acts as a transcriptional activator. STAT3 can be activated by Janus kinases (JAKs), epidermal growth factor receptor (EGFR), and interleukin 6 (IL6), which are involved in a number of physiological and pathological processes, including cell survival, proliferation, transformation, apoptosis, as cellular immunityIn the present study, we comprehensively used multiple databases to analyze the relationship between STAT3 expression and prognosis, different stages of patients with pan-cancer, investigate the clinical value of STAT3 in predicting prognosis, and the relationship between STAT3 genetic alteration and prognosis, drug sensitivity, and STAT3 expression, to determine whether STAT3 participates in tumor immunity, to provide a rationale for STAT3 as a treatment for broad-spectrum malignancy.http://vip.sangerbox.com/). The UALCAN portal  was used to conduct the protein expression analysis of the CPTAC  dataset. Then, in the GEPIA2 database , STAT3 was added as input and cancers in pan-cancer were selected; STAT3 expression information in different stage of patients with cancer were obtained in “Expression DIY-StagePlot.”The Sangerbox databases contain the expression of STAT3 genes in human cancer and normal tissues in TCGA and GTEx (genotype-tissue expression). After removing instances with fewer than three samples, the expression of STAT3 in normal vs. cancer tissues was visualized using the Sangerbox database , STAT3 was added as input, cancers in pan-cancer were selected, and a cut-off value of 50% was selected as the dividing threshold. The relationship between STAT3 expression and “Overall Survival” and “Recurrence-free survival” was collected in “Survival Analysis.”In the GEPIA2 database . In the cBioPortal database , we entered “STAT3” in the “TCGA Pan-Cancer Atlas Studies” section for queries of the genetic alteration characteristics across all TCGA tumors. In the “Mutations” module, the mutated site information was displayed. Moreover, we also applied the “Comparison” to obtain data on the overall, disease-free, progression-free, and disease-free survival differences for cancer cases. The corresponding Kaplan–Meier plots with log-rank P values were generated for significantly different cancers with or without STAT3 genetic alterations.Information on http://ualcan.path.uab.edu/), we investigated the total degree of proteomic expression and phospho-protein expression of STAT3 in cancer and normal tissues. STAT3 was added as an input and “TCGA” was selected, the information of STAT3 methylation in normal vs. cancer tissues was collected in “Methylation.” Then, “CPTAP” was selected to collect STAT3 protein phosphorylation data in normal and cancer tissues.Using the UALCAN database (http://timer.cistrome.org/), we selected choosing "GENE,” STAT3 added as input in Gene Expression, and choosing “Cancer-associated fibroblast” in Immune Infiltrates, “Purity Adjustment” was used to obtain the relationship between STAT3 expression and cancer-associated fibroblasts. Relationship between STAT3 expression and cancer-associated immune infiltration score was obtained in Sangerbox database (http://vip.sangerbox.com/), and the results were considered to be significant when P-value was < 0.05, and the absolute value of R was > 0.2013.In the TIMER database (https://cn.string-db.org/), STAT3 was used as input in the “multiple proteins” box with “Homo sapiens” selected as the species. In addition, the following parameters were set: meaning of network edges: “evidence,” active interaction sources: “Experiments,” minimum required interaction score: “low confidence 0.15” and max number of interactors to show: “no more than 50 interactors” 14. Finally, the results were visualized using Cytoscape 3.9.0. After that, in the “Similar Gene Detection” module of GEPIA2, we obtained the top 100 CBX3-correlated target genes. We then acquired five corresponding dot plots marked with the P value and correlation coefficient (R). Moreover, based on the “Gene_Corr” module in the “Exploration” of TIMER2.0, we obtained heatmap data of the five genes in TCGA cancers. We combined these two sets of data to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG)15 in the DAVID database (https://david.ncifcrf.gov/). The DAVID database was entered into the gene list as a screening tool. “OFFICIAL-GENE-SYMBOL” was selected, “Homo sapiens” was set as the species selection, and P < 0.01 and FDR < 0.01 were used as the screening standards to investigate the signaling pathways and biological properties. The data were analyzed using visualization approaches available on the Weishengxin online platform (http://www.bioinformatics.com.cn/).In STRING database (http://bioinfo.life.hust.edu.cn/GSCA/#/drug), STAT3 was used as keyword input, selected “GDSC drug sensitivity and expression correlation”, then, the relationship between drug sensitivity and expression of STAT3 was obtained.The relationship between drug sensitivity and expression of STAT3 was obtained from GSCA database (P = 1.1 e−71), glioma (GBMLGG P = 2.9 e−195), brain lower-grade glioma (LGG P = 2.1 e−158), esophageal carcinoma (ESCA P = 3.2 e−9), stomach and esophageal carcinoma (STES P = 1.3 e−8), stomach adenocarcinoma(STAD P = 2.5 e−22), pancreatic adenocarcinoma (PAAD P = 1.1 e−29),acute myeloid leukemia (LAML P = 1.2 e−13), and cholangiocarcinoma (CHOL P = 0.03). We found significantly decreased mRNA expression of STAT3 in nine tumor tissues, including breast invasive carcinoma (BRCA P = 4.2 e−8), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC P = 0.04), lung adenocarcinoma (LUAD P = 1.8 e−56), kidney renal papillary cell carcinoma (KIRP P = 9.3 e−14), pan-kidney (KIPAN P = 1.3 e−10), and colon adenocarcinoma (COAD P = 1.2 e−12). The ULACAN database results , uterine corpus endometrial carcinoma (UCEC P = 2.89E−02), lung adenocarcinoma (LUAD P = 3.42E−03), head and neck squamous carcinoma (HNSC P = 4.56E−16), pancreatic adenocarcinoma (PAAD P = 2.77E−03), and glioblastoma multiforme (GBM P = 1.19E−22). Ovarian cancer (OV P = 4.95E−04) and liver hepatocellular carcinoma (LIHC P = 5.67E−06) had lower protein expression levels than normal tissues. The GEPIA2 database showed that the expression levels of STAT3 were different among the different breast invasive carcinoma  and ovarian serous cystadenocarcinoma  stages , brain lower grade glioma (LGG P = 0.00096 HR = 1.9), testicular germ cell tumors , and uveal melanoma (UVM P = 0.038 HR = 2.6) with high levels of STAT3 expression had a significantly worse median survival time than those with low expression. However, in the vast majority of cases, patients with skin cutaneous melanoma  with high levels of STAT3 expression had a significantly better median survival time than those with low expression. In DFS , brain lower grade glioma , and uveal melanoma  with high levels of STAT3 expression had a significantly worse median survival time than those with low expression in the vast majority of cases. However, in the vast majority of cases, patients with esophageal carcinoma (ESCA P = 0.0036 HR = 0.61) with high levels of STAT3 expression had a significantly better median survival time than those with low expression.In OS, the GEIPA2 database showed  . In liver hepatocellular carcinoma (LIHC), testicular germ cell tumors (TGCT), and thyroid carcinoma (THCA), we observed a significantly lower DNA methylation level than in cancer tissues (P < 0.05). Subsequently, we compared the different phosphorylation features of STAT3 in normal and tumor tissues using the CPTAC dataset. STAT3 phosphorylation data for various tumor types are shown in Fig. P -values were less than 0.05.We obtained results from the UALCAN database Fig. . Owing tP < 0.05). However, in HPV-negative HNSCC (HNSC-HPV-), brain lower grade glioma (LGG), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), and the expression of STAT3 and cancer-associated fibroblasts showed a positive correlation (P < 0.05). Sangerbox database results in Fig. P = 1.7 e−26), brain lower grade glioma ,, pan-kidney cohort , colon adenocarcinoma , colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma , stomach adenocarcinoma , kidney renal clear cell carcinoma , bladder urothelial carcinoma , neuroblastoma , rectum adenocarcinoma , ovarian serous cystadenocarcinoma , uveal melanoma , pancreatic adenocarcinoma , acute myeloid leukemia , pheochromocytoma and paraganglioma , acute lymphoblastic leukemia, recurrent blood-derived cancer, bone marrow , lymphoid neoplasm diffuse large B-cell lymphoma , kidney chromophobe , and a negative significant correlation such as TARGET-acute myeloid leukemia .In the TIMER database, the same results across the algorithms Fig. A showed The top 50 proteins interacting with STAT3 are shown in Supplementary Table The GSCA database results Fig.  showed a16. According to the World Health Organization (WHO), cancer was the first or second leading cause of death in 201917. To enhance treatment for patients with cancer and predict prognosis in various cancer patients, immunotherapy and biomarkers have appeared, and have been fruitful in various cancer treatments18. Therefore, it is necessary to identify new biomarkers to prevent and treat cancer. Pan-cancer analysis is widely used to identify prospective biomarkers and reliable therapeutic targets19. In this study, multiple databases were used in a comprehensive pan-cancer analysis to provide a rationale for STAT3 as a broad-spectrum immunotherapy target in oncology.In recent years, cancer has become a prominent public health concern. In recent decades, cancer incidence has grown by nearly 3.9% each year, with a mortality rate of nearly 2.5%20, and STAT3 has a lower gene copy number, which might be one of the reasons for these findings. It is interesting to note that in the ULACAN database, the levels of STAT3 protein expression were higher than those in normal tissues, showing opposite results to those in Sangerbox. As a geometrical explanation of this phenomenon, research has shown that the levels of mRNA expression do not always correlate with the levels of protein expression, the translation rate of a protein is not identical, and the translational rate determines protein production rates, which are important factors that determine protein production rates21. In addition, reports have shown using immunohistochemical analysis that the expression level of STAT3 is significantly higher in patients with glioblastoma multiforme (GBM)22, esophageal carcinoma (ESCA)23, pancreatic adenocarcinoma (PAAD)24 and lung adenocarcinoma (LUAD)25 than in controls , which is consistent with our results. In ovarian cancer (OV) and hepatocellular carcinoma (LIHC), STAT3 protein expression was lower than that in normal tissues, but a study has shown that compared with normal tissues, patients with ovarian cancer (OV) had higher STAT3 protein26. We speculate that this difference may be due to the small sample size in ULACAN. The GEPIA2 database demonstrated that the expression levels of STAT3 were different among the different breast invasive carcinoma cancer (BRCA P = 0.0359) and ovarian serous cystadenocarcinoma (OV P = 0.0213) stages. STAT3 expression was found to be closely related to clinical stage (P < 0.001), and lymph node metastasis (P < 0.001) in breast invasive carcinoma cancer (BRCA)27. Studies on ovarian serous cystadenocarcinomas have since confirmed this finding28, which consistent with our results. Taken together, these results indicate that the expression of STAT3 differs between normal and cancer tissues in the vast majority of cancers; in particular, STAT3 protein expression is significantly higher in cancer than that in normal tissues.Multiple studies have implicated STAT3 in differential tissue expression in multiple cancers. The few studies on STAT3 at the gene level have noted that STAT3 activation is mainly achieved through phosphorylationP = 0.016; HR = 8.6), brain lower grade glioma (LGG P = 0.00096 HR = 1.9), testicular germ cell tumors (TGCT P = 0.045 HR = 6e + 08), and uveal melanoma (UVM P = 0.038 HR = 2.6) with high levels of STAT3 expression had a significantly worse median survival time than those with low expression. In kidney chromophobe (KICH), the relationship between overall survival of patients and STAT3 expression has not been reported; however, high STAT3 expression has been associated with poor prognosis in patients with Wilms tumors29. Similarly, high STAT3 expression promotes glioma cell survival and angiogenesis in brain lower-grade glioma (LGG), which could be another important factor that affects prognosis30. Increased STAT3 expression is also relevant in testicular germ cell tumors (TGCT). High STAT3 expression promotes testicular germ cell survival and angiogenesis, which could be another important factor that affect prognosis31. STAT3 is highly expressed in uveal melanoma (UVM), which promotes tumorigenesis and cancer progression and plays an important role in tumor prognosis32, consistent with our findings. However, in the vast majority of cases, patients with DFS and esophageal carcinoma (ESCA) with high levels of STAT3 expression had a significantly better median survival time than those with low expression. Studies have reported that different expression patterns exist in esophageal adenocarcinoma (ESAD) and esophageal squamous cell carcinoma (ESCA). In esophageal adenocarcinoma, patients with higher STAT3 expression had a superior survival than those with a lower expression33. Interestingly, we found that intersections exist in SKCM, which means a potential influence of confounders such as age and gender. In general, the above results showed that STAT3 could be potential biomarkers for multiple cancer diagnoses. However, we drew the conclusion above only from database. Therefore, some confounding factors such as gender, age, and the cancer stages were not taken into account. Hence, these influencing factors should be considered in future studies to validate these findings and draw a more definitive conclusion.For OS, the GEIPA2 database showed 35. In addition, the relationship between STAT3 gene alteration and cancer occurrence has also been reported in glioblastoma (GBM) and head and neck squamous cell carcinoma (HNSC)36. In summary, alterations in STAT3 are involved in carcinogenesis and cancer progression, especially in hematologic malignancies. Therefore, STAT3 may serve as therapeutic targets.As an oncogene, STAT3 alteration may lead to cancer initiation and progression; therefore, we analyzed STAT3 alterations in different cancers using the cBioPortal database. In diffuse large B-cell lymphoma (DLBC), a study has shown that the mutation of STAT3 induces the occurrence of this cancer37. Our results showed that the level of STAT3 DNA methylation was increased in the vast majority of cancers. Recent studies have suggested that DNA methylation plays a role in fine-tuning or reinforcing gene silencing38, which may be the reason that STAT3 has declined in most cancer types. Few studies have examined STAT3 DNA methylation in cancers. Nevertheless, in pituitary tumors39 and leukemia40, there have been reports that STAT3 DNA methylation may be early harbinger in cancer, and can promote vascularization41. Protein phosphorylation is an important regulatory mechanism that controls protein structure and function, and is thought to play an important role in cancer progression42. As an oncogene, STAT3 multiple phosphorylation sites are linked to different cancers. There are reports that STAT3 phosphorylation at Y705 is responsible for activating STAT3 to promote cell proliferation, enhance cell survival and proliferation, promote immune escape, and contribute to lung cancer malignant transformation43. Phosphorylation at S727 can activate transcriptional activity44, playing an important role in biological activities. The levels of phosphorylated STAT3 have been evaluated as a prognostic biomarker, and the levels of phosphorylated STAT3 at Y705 have been shown to increase the radiosensitization of glioblastoma multiforme cells45. In recent years, inhibition of phosphorylation and methylation of STAT3 has been studied as an anti-cancer strategy, indicating that phosphorylation and methylation of STAT3 are promising targets.DNA methylation has been shown to be related to carcinogenesis46. Our results showed that STAT3 levels were negatively associated with cancer-associated fibroblasts (CAFs) in patients with esophageal carcinoma (ESCA). STAT3 levels have been reported to be inversely correlated with the degree of tumor differentiation in esophageal carcinoma47. However, cancer-associated fibroblasts promote tumor growth and progression48 which could be one reason for the above results. Similarly, miR-210 transferred by lung cancer cell-derived exosomes acts as a proangiogenic factor in cancer-associated fibroblasts by modulating the JAK2/STAT3 pathway49. IL-22 produced by cancer-associated fibroblasts has been reported to promote gastric cancer cell invasion via STAT3 and ERK signaling50. In pancreatic adenocarcinoma (PAAD)51, STAT3 levels are positively associated with cancer-associated fibroblasts in Pancreatic adenocarcinoma. This is consistent with our findings. The Sangerbox database results showed that STAT3 expression status was significantly correlated with immune infiltration score in 27 cancers, illustrating that STAT3 plays a role in immune responses. Correspondingly, studies have indicated that STAT3 plays a role in the immune responses52. This is consistent with our findings. In addition, it has been reported that in glioblastoma, the STAT3 pathway is a key molecular hub in tumor-mediated immunosuppression53. STAT3 expression has also been reported to be associated with tumor-associated macrophages differentiation which has also been found54. These results show that STAT3 expression is closely related to immune infiltration and may be a novel target for cancer immunotherapy.The tumor microenvironment has been the subject of intensive investigation in recent years. During tumor initiation and progression, the immune status of the tumor microenvironment is an essential factor affecting tumor progression. Therefore, the tumor microenvironment is considered to be a “the seventh hallmark” of cancer55, and tumor-derived EGFRvIII can drive STAT3/5 and progression in glioblastoma56. In addition, studies have shown that PGAP3, GRB7, STARD3, and PSMD3 are significantly positively correlated with STAT3 in TCGA cancers; however, reports on these are lacking. Based on our results, it has diverse biological functions and participates in diverse signaling pathways, such as Th17 cell differentiation. Th17 cells are immune cells that secrete IL-17 and play important roles in inflammation mediation57. Several studies have indicated that STAT3 plays an important role in regulating Th17 differentiation58. The JAK-STAT signaling pathway is closely related to the development and progression of various diseases, including autoimmune diseases59, autoimmunity60 and cancers61. We found that it has diverse biological functions and participates in diverse signaling pathways, especially in cancer immunotherapy. Taken together, our data showed that STAT3 is critical in cancer immunotherapy, further illustrating that STAT3 could be a promising target for cancer therapy.To reveal the biological roles of STAT3-related genes and proteins, we performed enrichment analysis. After further visualization, an analysis was performed. STAT3 combined with EGFR showed the highest score. Other investigators have reported similar findings, showing that STAT3 has been linked to EGFR, where EGFR can activate STAT3, thus contributing to cancer progression. For example, EGFR-STAT3 signaling can promote the formation of malignant peripheral nerve sheath tumors62. However, the relationship between STAT3 expression and its drug sensitivity has not been explored. Studies have shown that the Stat3-DNA binding activity and the expressive intensity of phospho-Stat3 protein were lower in 5-fluorouracil drug-resistant cell than in parental cell63. Notably, a significant negative correlation was found between STAT3 expression and sensitivity to drugs, such as 17-AAG and afatinib. 17-AAG dampened M1-CM-induced inflammation and catabolism in NPCs by decreasing HSP70 and inhibiting the Janus kinase 2 (JAK2)-signal transducer and activator of transcription 3 (STAT3) pathway64. Thus, an increase in STAT3 expression predicted 17-AGG drug resistance. In afatinib-resistant lung cancer cells, inhibition of IL-6R/JAK1 significantly increased the sensitivity to afatinib65, and STAT3 expression increased the predicted drug resistance. Collectively, a close relationship exists between aberrant STAT3 expression and drug sensitivity, which may be a cause of drug resistance.Finally, we analyzed STAT3 function in clinical applications. A significant positive correlation was observed between STAT3 expression and drug sensitivity. AR-42  has been shown to have potent HDAC-inhibitory activity, which inhibit the GP130/STAT3 pathway to inhibit multiple myeloma cell growthOverall, we found that STAT3 has a good predictive value for cancer prognosis, drug resistance, and immunotherapy. However, even though we systematically investigated pan-cancer information from multiple databases, there were some limitations to this study, such as experimental error and missing data. Additional validation experiments are needed in clinical samples or appropriate models in vitro or in vivo to avoid such bias, and this study provides a rationale for further experimental studies.These results indicate that STAT3 can serve as a prognostic, sensitivity prediction biomarker and target of immunotherapy, which is of great value in pan-cancer treatment. However, in-depth studies focusing on STAT3 expression and the tumor immune microenvironment should be conducted to provide a therapeutic strategy based on the immune system.Supplementary Tables.


The National Health and Family Planning Commission of China (NHFPCC) issued the “Measures for the Management of Human Sperm Banks,” which was revised in 2003 and is still in effect today. One of the standard guidelines is that potential donors undergo laboratory testing to exclude infectious and genetic diseases and karyotype analysis. However, patient demands for donor genetic testing have also increased, and only karyotype analysis to exclude genetic diseases is not sufficient to meet these demands.To examine donor genetic screening practices at sperm banks in China and to evaluate the qualifications and skills of genetic counselors at the banks.An electronic survey was distributed to twenty-seven sperm banks to examine donor genetic screening practices at sperm banks in China and to evaluate the qualifications and skills of genetic counselors at the banks. Twenty-six human sperm banks responded to a 32-question survey about their current practices related to genetic testing of sperm donors.The 26 sperm banks reported that all qualified sperm donors undergo karyotype analysis; 22 banks (84.6%) collected three generations of family history from each qualified sperm donor; 10 (38.5%) reported that they attempted to accommodate special requests from donor semen recipients for particular genetic tests. Only 2 of the 26 (7.7%) sperm banks reported that they performed whole-exome sequencing. At all the sperm banks, consent for genetic testing was obtained as part of the overall contract for sperm donors. Nineteen (73.1%) sperm banks had genetic counselors on their staff, while six (23.1%) had no genetic counselors on their staff but had access to genetic counselors at the hospital. Only one (3.8%) sperm bank had no genetic counselors on their staff or at the hospital.The need for larger scale genetic testing of donors and recipients and an extensive panel of genetic tests specific to the Chinese population. Additionally, professionally trained geneticists must be employed as genetic counsellors so that the results of genetic tests and their implications can be explained to donors. In early 1981, Lu Hui-Lin and Lu Guang-Xiu established the first human sperm bank in the People’s Republic of China. Despite its unsteady start, human sperm banking has now become a routine part of China’s pervasive and restrictive reproductive complex within a period of 40 years. Today, there are 27 sperm banks in China, and Beijing, Shanghai, and Henan province have two sperm banks each Figure 1In 1997, there was a big uproar among professionals and in the media when a donor with 18 donor offspring was found to be suffering from autosomal dominant cerebellar ataxia, which is a severe, untreatable, hereditary neurodegenerative disorder . These iIn 2001, The National Health and Family Planning Commission of China (NHFPCC) issued the “Measures for the Management of Human Sperm Banks,” which was revised in 2003 and is still in effect today. One of the standard guidelines is that potential donors undergo laboratory testing to exclude infectious and genetic diseases and karyotype analysis . AdditioCounseling regarding the residual risk and reproductive implications of genetic testing is best provided by a certified genetic counselor . HoweverIn order to provide a comprehensive overview of the genetic testing practices across sperm banks in China, in the present study, we have conducted a survey of human sperm banks in China to examine which genetic tests are performed on sperm donors and have evaluated the professionals involved in family history evaluation and genetic screening of donors.This study used a questionnaire to survey the genetic testing of sperm donors in China. The survey consisted of 32 questions that aimed to evaluate the current practices in genetic testing of sperm donors in human sperm banks, including basic information about the sperm banks, the methods used for genetic testing of sperm donors, information about genetic counselors at the sperm bank, and the procedures for managing test results. The completion and submission of the questionnaires by participants indicated their consent for participation in the study.Study Population and ProcedureHuman sperm banks in the People’s Republic of China were identified through an internet search and information on the NHFPCC website. Twenty-seven human sperm banks were identified and invited to participate in this study. Each sperm bank was contacted by telephone or WeChat  and asked to nominate an individual who would be most suited to answering the online questionnaire on genetic screening practices at the sperm bank. The questionnaire was distributed through WeChat. The individuals who were contacted were asked to forward the survey to an appropriate staff member if they felt that they could not adequately address the survey questions.All responses were anonymized. One week after the surveys were distributed, the participants were contacted by telephone to remind them of the opportunity to participate if they had not yet had the chance to do so. The study protocol was approved by the Ethics Committee of Central South University approved of this study .Responses were received from 26 of the 27 human sperm banks (96.3%) that were contacted. The individuals who responded were the heads of the sperm banks, as shown in The participating sperm banks reported that all qualified sperm donors undergo karyotype analysis. In addition, 22 of the 26 sperm banks (84.6%) collect data on three generations of family history from each qualified sperm donor Table 1.The data in As shown in As shown in The data from the present survey show that the prevalence of genetic testing and the protocol for the tests are not standardized and differ across human sperm banks in different regions of China.In recent years, the cost of molecular genetic tests has greatly reduced due to the emergence of next-generation sequencing technologies, which have allowed for the analysis of a large number of genes and the concurrent evaluation of hundreds of mutations . Hence, Present study showed there are 10 banks in China are using NGS (Next-Generation Sequencing) to test specific genetic now. However, as we know, it cannot be detected by NGS in some specific genes mutations, for example: HBA1, HBA2, SMA etc. Therefore, the gap-PCR method is used for detecting thalassemia-related genes. Also, to our knowledge, the hemoglobin electrophoresis was used for detect thalassemia-related genes in sperm bank 14, 19 and 21, these banks test all donors thalassemia. However, the thing is, different methods would show different detection rates and interpretations. Also, they all have advantages and limitations  sperm banks had a genetic counselor on their staff . In 2013Genetic testing of sperm donors was introduced in China much after it was initiated in other countries. In the United States, sperm banks frequently cite the guidelines of organizations such as the American Society of Reproductive Medicine  and the The present study has certain limitations such as survey recipients were asked to forward the survey to an appropriate staff member who would be most suited to answering the online questionnaire, which might have led to self-selection bias. In addition, the sperm bank in some provinces was relatively small, the number of qualified sperm donors was few, hence, and the findings in these sperm banks might have led to bias.This is the first study to examine donor genetic screening practices at sperm banks and to evaluate the qualifications and skills of genetic counselors at the sperm banks in China. The results of this study are significant for the development and standardization of sperm banks in China. They imply that genetic tests for donors and recipients, apart from karyotype and three generation family history analysis, must be performed on a wider scale. Additionally, based on data available about birth defects in donor offspring and whole-exome sequencing of donors, an extensive panel of genetic tests specific to the Chinese population must be developed. In addition, a professional geneticist trained in genetic counseling must be employed by sperm banks so that the results of the tests and their implications can be clearly explained to the donors and their corresponding needs can be addressed. These strategies will benefit donors as well as the sperm banks.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by Ethics Committee of Central South University. The patients/participants provided their written informed consent to participate in this study.Study conceptualization and design, patient recruitment, and data collection: CH, Q-LW, X-ZZ, and W-BZ. Data analysis and manuscript drafting: CH, Z-HH and X-ZZ. Patient recruitment and data collection: H-LW. All authors approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.