Circadian Rhythms

Estimating ground-level concentrations of dry 24-hr PM_2.5 (micrograms per cubic meter) from satellite observations of total-column AOD (unitless) requires a conversion factor that accounts for their spatially and temporally varying relationship:
η is a function of the factors that relates 24-hr dry aerosol mass to satellite observations of ambient AOD: aerosol size, aerosol type, diurnal variation, relative humidity, and the vertical structure of aerosol extinction (van Donkelaar et al. 2006 (link)). Following the methods of Liu et al. (2004 (link), 2007) and van Donkelaar et al. (2006) (link), we used a global 3-D CTM [GEOS-Chem; geos-chem.org; see Supplemental Material (doi:10.1289/ehp.0901623)] to calculate the daily global distribution of η.
The GEOS-Chem model solves for the temporal and spatial evolution of aerosol (sulfate, nitrate, ammonium, carbonaceous, mineral dust, and sea salt) and gaseous compounds using meteorological data sets, emission inventories, and equations that represent the physics and chemistry of atmospheric constituents. The model calculates the global 3-D distribution of aerosol mass and AOD with a transport time step of 15 min. We applied the modeled relationship between aerosol mass and relative humidity for each aerosol type to calculate PM_2.5 for relative humidity values that correspond to surface measurement standards [European Committee for Standardization (CEN) 1998 ; U.S. Environmental Protection Agency 1997 ] (35% for theUnited States and Canada; 50% for Europe). We calculated daily values of η as the ratio of 24-hr ground-level PM_2.5 for a relative humidity of 35% (U.S. and Canadian surface measurement gravimetric analysis standard)and of 50% (European surface measurement standard) to total-column AOD at ambient relative humidity. We averaged the AOD between 1000 hours and 1200 hours local solar time, which corresponded to the Terra overpass period. We interpolated values of η from 2° × 2.5°, the resolution of the GEOS-Chem simulation, to 0.1° × 0.1° for application to satellite AOD values.
We compared the original MODIS and MISR total-column AOD with coincident ground-based measurements of daily mean PM_2.5. Canadian sites are part of the National Air Pollution Surveillance Network (NAPS) and are maintained by Environment Canada (http://www.etc.cte.ec.gc.ca/NAPS/index_e.html). The U.S. data were from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network (http://vista.cira.colostate.edu/improve/Data/data.htm) and from the U.S. Environmental Protection Agency Air Quality System Federal Reference Method sites (http://www.epa.gov/air/data/index.html). Validation of global satellite-derived PM_2.5 estimates was hindered by the lack of available surface-measurement networks in many parts of the world. To supplement this lack of available surface measurements, we collected 244 annually representative, ground-based PM_2.5 data from both published and unpublished field measurements outside the United States and Canada[see Supplemental Material (doi:10.1289/ehp.0901623)].

Actigraphy is a non-invasive method of monitoring participants’ rest-activity cycles, which is most commonly used in sleep and circadian rhythm research [2] (link), [8] (link). In many studies, actigraphy recordings are used to ensure that participants adhere to a prescribed sleep-wake rhythm (cf. e.g. [5] , [9] (link)). Moreover, actigraphy data can inform about disturbances of the sleep-wake cycle such as circadian rhythm disorders and sleep disorders (for an overview see [1] (link)). It is measured with a wrist-watch-like device, usually worn on the non-dominant hand, for several days before, during and after an experiment. In some cases ankle movements rather than wrist movements are expected to reflect the ‘true’ activity of the participant (e.g. when the upper limbs are spastic) and in these instances the device can be worn on the ankle. Movements the device undergoes are continuously recorded with a previously specified sampling rate (SR; e.g. 4/60 Hz).
In most studies, however, the analysis of actigraphy data is limited to a rather crude visual inspection of the general pattern of rest and activity or sleep and wakefulness, which is also reflected in the purposes other available R packages serve. The ‘PhysicalActivity’ package [3] for example only allows analysing device wear and non-wear time intervals and other tools such as the ‘accelerometry’ package [10] even focus solely on the analysis of periods of activity thus completely ignoring rhythmic changes between rest and activity. We think that sometimes, however, a more advanced analysis of rest-activity cycles could yield further information and certain parameters may even become variables of interest. While the ‘GGIR’ package computes the M5 and L5 descriptives (i.e. five hours with minimal and maximal activity) for data obtained with specific devices, it does not calculate other parameters that might be valuable for a comprehensive description of the rest-activity pattern. In particular, parameters quantifying how well the period length of a rhythm matches the earth’s 24 h light-dark cycle, how fragmented a rhythm is and what amplitude the rest-activity pattern has could be of special interest. We assume that the reluctance to further analyse actigraphy data regarding such parameters and thus the underestimation of their scientific value is, partly, due to the lack of adequate analysis tools. We thus developed the package ‘nparACT’ for R Core Team [6] , that computes several non-parametric measures from actigraphy data that allow for a quantification of the parameters mentioned above (cf. [11] (link), [12] (link), [13] (link)). The most recent version of the package can be downloaded from the Comprehensive R Archives Network (CRAN) and is, just as R itself, open source. As the package is updated from time to time, we deliberately do not provide a zip file of the package along with this publication.

Single nucleotide variation (SNV) data (n = 7130) were collected across 18 cancer types from Genomic Data Commons. We estimated the mutation frequency of each circadian rhythm gene in each tumor using the maftools (https://bioconductor.org/packages/release/bioc/html/maftools.html) and oncoplot waterfall plot [38 (link)]. We show genes with an overall mutation proportion of over 10%. SNV mutation frequency (percentage) of each gene's coding region was calculated using the formula: Number of Mutated Samples/Number of Cancer Samples. The SNVs were divided into missense mutations, nonsense mutations, multiple hits, frameshift insertions, frameshift deletions, splice sites, in-frame amplifications and in-frame deletions.
For copy number variation (CNV) data, we downloaded gene level copy number score data (GISTIC—focal score by gene) from UCSC Xena, which is the sequence interval focused on the gene and assessed whether the gene was amplified or deleted. Values between − 0.3 and 0.3 were scored as 0 for no changes, larger than 0.3 as 1 for amplifications, and less than 0.3 as − 1 for deletions. Heatmap plot of mutation frequency was generated by using ComplexHeatmap R package.

The score to represent the circadian rhythm disorder level was established based on the expression data of CRGs, including 13 clock control genes and 35 core clock genes. The single sample gene set enrichment analysis (ssGSEA) [39 (link)] in the R package "GSVA" was used to find equally enriched pathways and calculate the gene set enrichment scores (ES) for clock control genes and core clock genes. The control component minus the core component was defined as the circadian rhythm score (CRS) to quantify the expression levels of these genes for each cancer patient. We also estimated the CRS between tumor and normal samples in 18 cancers from the TCGA. We use the surv_cutpoint function of the "survminer" R package to determine the optimal cut-point for CRS and to divide patients into high and low subgroups.

A total of 48 CRGs, including 13 clock control genes and 35 core clock genes, were obtained when we used the keywords ((((circadian clock gene) OR (circadian rhythm gene) OR (clock control gene) OR (core clock gene)) AND (cancer)) in PubMed (https://pubmed.ncbi.nlm.nih.gov/) (Additional file 4: Table S1).
In the Cancer Genome Atlas data portal (TCGA, https://portal.gdc.cancer.gov/), 18 cancer types (Additional file 4: Table S2) were filtered based on the presence of at least 5 normal samples. We downloaded RNA-Seq data expressed as fragments per kilobase per million (FPKM) from the UCSC Xena browser (GDC Center, https://gdc.xenahubs.net). The gene-expression profiles of TCGA in the Fragments Per Kilobase per Million (FPKM) format were transformed into TPMs (transcripts per kilobase million) by using R version 4.2.0 software. Additionally downloaded for analysis were clinical data, copy number variation data, and single nucleotide variation data.
Microarray datasets including gene expression profiles and corresponding clinical information data of GSE42568, GSE14333, and GSE31908 were employed from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/). We downloaded the chemotherapy and immunotherapy response datasets from the GEO database (GSE25055, GSE20194, GSE42127, GSE14814, GSE146163, GSE78220, and GSE174570). Moreover, we acquired four sets of mice's expression profile data from the GEO database that had circadian rhythm genes knocked out (GSE134333, GSE188688, GSE134333, and GSE143524). For microarray data, we performed log2 conversion of the normalized expression profile data. The list of these datasets was displayed in Additional file 4: Table S3.