SERPINE1, FABP4, SCG2, CALB2, and HOXC6) were closely associated with tumorigenesis, immune cells, and ICB response indices. Finally, TMEIG scores could accurately predict the prognosis and ICB response of patients with CRC. SERPINE1, FABP4, SCG2, CALB2, and HOXC6 might be potential targets related to ICB treatment. Furthermore, our study provided new insights into precision ICB therapy in CRC.Despite immune checkpoint blockade (ICB) therapy contributed to significant advances in cancer therapy, only a small percentage of patients with colorectal cancer (CRC) respond to it. Identification of these patients will facilitate ICB application in CRC. In this study, we integrated multiple CRC cohorts  to construct tumor microenvironment (TME) subtypes using TME indices calculated by CIBERSORT and ESTIMATE algorithms. Furthermore, a surrogate quantitative indicator, a tumor microenvironment immune gene (TMEIG) score system, was established using the key immune genes between TME clusters 1 and 2. The subsequent analysis demonstrated that TME subtypes and the TMEIG score system correlated with clinical outcomes of patients in multiple CRC cohorts and exhibited distinct immune statuses. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that patients with low TMEIG scores were more likely to benefit from ICB therapy. A study on two ICB cohorts (GSE78220 and IMvigor210) also validated that patients with low TMEIG scores exhibited higher ICB response rates and better prognoses after ICB treatment. The biomarker evaluation module on the TIDE website revealed that the TMEIG score was a robust predictive biomarker. Moreover, differential expression analysis, immunohistochemistry, qPCR experiments, and gene set prioritization module on the TIDE website demonstrated that the five genes that constitute the TMEIG score system ( Colorectal cancer (CRC) is one of the most common malignant tumors globally, with high morbidity and mortality. Tumor immunotherapies involving immune checkpoint blockade (ICB) have contributed to significant advancements in the treatment of many tumors , such asThe tumor microenvironment (TME) mainly contains tumor, immune and stromal cells, and small molecules . TME of In the present study, we integrated transcriptome data of 1,175 patients with colorectal cancer from the GPL570 platform and then employed CIBERSORT, ESTIMATE, and ssGSEA algorithms to assess the characteristics of the TME. Based on TME heterogeneity, two TME subtypes (clusters 1 and 2) with different survival statuses were identified using a consensus clustering algorithm. Weighted gene co-expression network analysis (WGCNA), linear models for microarray data (LIMMA), and other bioinformatics analyses were used to identify the hub TME immune genes between subtypes. Then, patients were also divided into two tumor microenvironment immune gene (TMEIG) subtypes (clusters A and B) with different survival statuses according to the hub TME immune genes. Moreover, a robust prognostic scoring system (TMEIG score) was developed using these TME immune genes, which could effectively predict overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) of patients with CRC. The prognostic TMEIG score system was also verified in multiple cohorts, such as TCGA-COAD. Notably, we validated that the TMEIG score could predict ICB response in multiple immunotherapy cohorts and is expected to guide the application of ICB in CRC.Ten data sets were downloaded from the public database, including clinical data and transcriptome data of 2,078 patients with CRC. GSE39582, GSE14333, GSE17536, GSE17537, and GSE72968 were all microarray data of the GPL570 platform and integrated as a training set (the combined GEO cohort). The CEL format data of the microarray was downloaded using the “GEOquery” package . ReadAffCIBERSORT  and ESTI“ConsensusClusterPlus” is a re-sampling unsupervised clustering method to verify the rationality of clustering . In the WGCNA analysis was used to identify gene modules most associated with traits . Stromalp-value of less than 0.05 was regarded as statistically significant. In addition, Gene Set Enrichment Analysis (GSEA) was conducted (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)  analysesonducted . The genonducted . The logp-value less than 0.05 were regarded as the candidates, input to least absolute shrinkage and selection operator (LASSO) regression  in the LASSO regression model. The combined GEO cohort was used as a training set, whereas GSE39582, GSE17536, and GSE17537 were used as the internal validation sets. In addition, the TCGA COAD cohort served as the external validation set.Only GSE39582, GSE17536, GSE17537, and GSE72968 had overall survival data in the combined GEO cohort . The surThe mutation data of TCGA COAD were downloaded from the TCGA website and analyzed using the “maftools” package . The tumTumor Immune Dysfunction and Exclusion (TIDE) algorithm was employed to predict ICB response based on the gene expression related to T cell dysfunction (Dysfunction) and T cell exclusion (Exclusion). The lower the TIDE score is reportedly associated with a better immunotherapy response . Further2 atmosphere.The human CRC cell lines SW620, RKO, HCT116, HT29, and NCM460 (ATCC) were cultured in RPMI-1640 medium  supplemented with 10% fetal bovine serum  at 37°C in a humidified 5% COvia PrimeScript™ RT Master Mix . Quantitive real-time PCR was performed using PowerUp™ SYBR™ Green Master Mix  in the StepOne™ Real-Time PCR System (Applied Biosystems). Each reaction was tested in triplicate. ACTB was used as the internal reference, and the 2(−ΔΔCT) method was used for calculating the relative mRNA expression. The following primer sets were used:Total RNAs of cell lines were extracted by TRIzol reagent  and then was reversely transcribed as cDNA FABP4: Forward: 5ʹ-GGGCCAGGAATTTGACGAAG-3ʹ, Reverse: 5ʹ-TCGTGGAAGTGACGCCTTTC-3ʹ; Human SCG2: Forward: 5ʹ-GTGAAGCGAGTTCCTGGTCA-3ʹ, Reverse: 5ʹ-ATGCTCTTTGATGGCCTGCT-3ʹ; Human CALB2: Forward: 5ʹ-GAAGGCAAGGAAAGGCTCTGG-3ʹ, Reverse: 5ʹ-GCCATCTCGATTTTCCCATCTG-3ʹ; Human SERPINE1: Forward: 5ʹ-CCTGGTTCTGCCCAAGTTCT-3ʹ, Reverse: 5ʹ-CCATGCGGGCTGAGACTATG-3ʹ; Human HOXC6: Forward: 5ʹ-CACTAACCCTTCCTTATCCTGCC-3ʹ, Reverse: 5ʹ-TCATAGGCGGTGGAATTGAGG-3ʹ; Human ACTB: Forward: 5ʹ-GATTCCTATGTGGGCGACGA-3ʹ, Reverse: 5ʹ-AGGTCTCAAACATGATCTGGGT-3ʹ.Human For the IHC experiment, we collected 16 pairs of CRC tissue  from patients who received surgery at the Department of Colorectal Cancer Surgery, the Second Affiliated Hospital of Harbin Medical University  between January 2014 and December 2020. Ethics approval was also granted by the Ethics Committee of Harbin Medical University (No. KY 2022-063). The primary antibodies used in IHC were as follows: anti-FABP4 , anti-SCG2 , anti-CALB2 , anti-HOXC6 , and anti-PAI1 (SERPINE1) . Paraffin sections were incubated with primary antibodies at 4°C overnight, followed by treatment with HRP-conjugated secondary antibodies at 37°C temperature for 60 min following PBS rinse. Then, tissues were counter-stained with hematoxylin and further treated with DAB for 2 min. The IHC results were independently analyzed by two experienced pathologists. A staining scoring system was evaluated by both staining intensity  and staining area . The staining intensity score was computed, and the score of the staining area was the final staining score. A total score of ≤3 was considered a weak expression. A total score of >3 was considered a strong expression. The details of IHC performance and scoring system are described in p-value less than 0.05, ** represented a p-value less than 0.01, *** represented a p-value less than 0.001, and **** represented a p-value less than 0.0001. All analyses were performed in R 4.0.3.Heat maps were visualized with the “ComplexHeatmap” package . The “ggp-value < 0.05; p-value < 0.05) and positively related to M1 macrophages .The flow diagram describes the construction of TME subtypes and the TMEIG score in CRC . We inteK = 2. The PCA plot also demonstrated significant differences between the two clusters (p = 0.047). Furthermore, we explored 11 critical biological gene signatures between the two TME subtypes using a heat map , and green  modules displayed the high correlations with adaptive immunity . Since there were very few downregulated genes, we mainly used the upregulated genes to compare with the candidate genes of WGCNA. A total of 202 TMEIGs were eventually identified after comparing candidate genes with upregulated genes . Similarly, the heat map of tumor-related pathways showed that cell cycle and DDR signatures were significantly decreased in TMEIG cluster B. Angio, transforming growth factor-beta (TGFβ), antigen processing machinery (APM), TGF-beta response signatures (TBRS) of fibroblasts (F-TBRS), and immune checkpoint signatures were increased in TMEIG cluster B as compared to that in cluster A  and their corresponding lambda coefficients were obtained when lambda = 0.0713387182. The TMEIG score was constructed based on the expression and coefficient of the five genes as described in the methods. According to the optimal cutoff value, patients were divided based on whether their TMEIG scores were high or low (p < 0.0001).Gene signature is a simple and effective model widely used in clinical practice . To furtO cohort . Detailsh or low . OS analp < 0.0001). In addition, the PFS and DSS in the low TMEIG score group were superior to those of the high TMEIG score group (p < 0.0001). Then, the TCGA COAD cohort also revealed that the overall survival of the high TMEIG score group was poorer  was significantly higher in the high score group (p = 6.9e−06). Next, we explored the proportion of patients with MSI-H and MSI-L/MSS in high and low TMEIG score groups. We observed more MSI-H CRC patients in the high TMEIG score group , and T-cell factor-beta (TCFβ) signatures were significantly more activated in the high TMEIG score group. In contrast, cell cycle and DDR signatures were highly expressed in the low TMEIG score group (p < 2.2e−16), Dysfunction , and Exclusion , and negatively correlated with M2 macrophages  and MDSC . In addition, there was a strong positive correlation between the TMEIG and TIDE scores (p < 0.003).To evaluate whether the TMEIG score could predict the efficacy of ICB treatment, we analyzed the expression of crucial immune checkpoint molecules between high and low TMEIG score groups. The results showed that the expression of immune checkpoint molecules . The insignificant result in GSE78220 (28 patients) can be attributed to the small sample size. Furthermore, the biomarker evaluation module on the TIDE website was used to assess the accuracy of the TMEIG score using multiple ICB cohorts as compared to other published biomarkers. The TMEIG score demonstrated an AUC of more than 0.5 in nine out of 16 ICB cohorts , with the other 14 cases displaying low expression (14/16). For FABP4, seven and two cases in tumor and normal samples, respectively, exhibited stronger staining, whereas nine and 14 cases in tumor and normal samples, respectively, were with low staining intensity. For HOXC6, seven and nine samples out of 16 exhibited strong staining in normal and tumor samples, respectively. The protein level of SCG2 was high in 12 cases of CRC samples (12/16) and 11 cases of normal samples (11/16). SEP1NG1 was found strongly stained in 11 cases of tumor samples (11/16) and nine cases of normal samples (9/16). Representative immunohistochemical images are shown in HOXC6 and SERPINE1 was significantly upregulated in RKO and HT29 cell lines, and FABP4 expression was downregulated in nearly all CRC cell lines analyzed. Although SCG2 and CALB2 were downregulated in patients with CRC in multiple cohorts, qPCR experiments showed that their expression was upregulated in several CRC cell lines (such as HCT116 and HT29). This discrepancy may be due to false positives in RNA sequencing or the heterogeneity between clinical tissues and tumor cells. Studies involving more clinical samples or cell lines may be needed to confirm the expression of the two genes at the RNA and protein levels in the future. KM plots of these genes are displayed in SERPINE1, FABP4, SCG2, and HOXC6)  and cytotoxic genes , which revealed that these genes might play an important role in tumor immunity. We then explored the correlation between the five genes and immune cells infiltrating the TME. As shown in the correlation heatmap, the five genes were positively related to macrophages , inversely correlated with resting NK cells and resting memory CD4+ T cells, CD8+ T cells, and activated memory CD4+ T cells  . Next, wd HOXC6) . Results T cells , which me to ICB . The expUnderstanding the heterogeneity of the tumor microenvironment is required to elucidate the biological properties of CRC and guide the treatment strategies. Moreover, the TME heterogeneity is closely related to the efficacy of ICB therapy . Thus, uIn this study, we constructed TME subtypes based on the TME landscape of patients with CRC, which can accurately distinguish the heterogeneity of the TME and predict the clinical prognosis. The patients were then re-clustered by TMEIGs identified by WGCNA and differential expression analysis. Two TMEIG subtypes were obtained, reflecting heterogeneity in TME and clinical prognosis. Gene signature is a simple and effective model widely used in clinical practice . TherefoVIM, TWIST1, ZEB1, and ZEB2), and TCFβ signatures were significantly activated in the high TMEIG score group (p = 0.003), indicating that patients in the high TMEIG score group exhibited poorer ICB response. The prediction of ICB response by MSI, TMB, or PD-L1 is based on the presence of CTL cells in the TME. Hence, we speculated that patients with high TMEIG scores mainly tend to exhibit poor ICB therapy response due to fewer CTL cells that are primarily dysfunctional. Since there was no suitable public ICB treatment CRC cohorts at the time of publication, we only used transcriptome data from other tumor types to verify the predictive power of the TMEIG score. Nevertheless, validation in melanoma and urothelial cancer datasets may indirectly suggest that the TMEIG score predicts the efficacy of immune checkpoint therapy in CRC. In accordance with TIDE results, a higher TMEIG score was associated with poorer ICB response and prognosis in two ICB treatment cohorts. In conclusion, the evidence demonstrated that the TMEIG score might serve as a reliable ICB biomarker in CRC. We will further validate our results once transcriptome data of CRC patients undergoing immune checkpoint therapy becomes publicly available or establish our own cohort regarding this point.Tumor mutation burden (TMB) , microsare group . Furtherre group . A highere group . In the re group . It indiSERPINE1, FABP4, SCG2, CALB2, and HOXC6 expression. HOXC6 is a member of the homeobox family, which encode transcription factors that play a critical role in morphogenesis in all multicellular organisms. HOXC6 expression was higher and negatively associated with prognosis in right-sided colon cancer than in left-sided colon cancer. This finding was further validated by tissue microarray analysis. HOXC6 facilitated proliferation and metastasis through the dickkopf-1 (DKK1)/Wnt/β-catenin axis in right-sided colon cancer (FABP4, which encodes the fatty acid–binding protein found in adipocytes, is unclear in CRC. A study demonstrated that FABP4 was downregulated in CRC (FABP4 and plasma FABP4 concentrations were higher in CRC tissues than in normal tissues (FABP4 in CRC must be investigated further. In addition, mRNA and protein levels of SCG2, a member of the chromogranin family of acidic secretory proteins, were significantly downregulated in CRC tissues (SCG2 inhibits tumor growth and angiogenesis by disrupting the activities of HIF-1α/VEGF in malignant CRC tissues (In vitro and in vivo studies have shown that CALB2 promotes hepatocellular carcinoma metastasis via the TRPV2-Ca2+-ERK1/2 signaling pathway (CALB2 in CRC, their expression levels were not quantified and compared in tumor and normal tissues (SERPINE1 expression is reportedly upregulated in CRC tissues and is associated with tumor invasiveness and aggressiveness (HOXC6, SERPINE1, FABP4, SCG2, and CALB2 in tumorigenesis, cancer immunity, and ICB treatment are poorly understood. In the present study, IHC and qPCR results preliminarily elucidated the expression levels of these five molecules in CRC and normal tissues. Larger clinical sample sizes are required to verify mRNA and protein expression levels reported in this study and whether protein levels can be used to predict the prognosis of patients suffering from CRC and their response to ICB therapy. In addition, we observed that the five genes were significantly associated with immune cells of TME, immune checkpoint genes, and cytotoxic genes. Immune checkpoint genes and cytotoxic genes were collected from our previous study (HOXC6, were closely associated with four immunosuppressive indices, including T cell dysfunction score, T cell exclusion score, association with ICB survival outcome, and logFC in CRISPR screens. Collectively, the roles of the five genes in tumor immunity are worthy of investigation, which will be the focus of our future research.In our study, the TMEIG score was determined by n cancer . The rold in CRC . IHC and tissues . Thus, t tissues . Mechani tissues . In vitr pathway . Althoug tissues . SERPINEsiveness . Our stusiveness . Neverthus study . Moreovein vivo and in vitro studies from our group will focus on these aspects.Our study has numerous advantages. First, datasets of the combined GEO cohort were downloaded from the GPL570 platform, which reduced the batch effect caused by different platform processes. Second, a large cohort with more than 1,000 samples was used for clustering, guaranteeing stable clustering results. Third, the prognostic power and predictive ICB response of the TMEIG score have been validated in multiple cohorts. However, the study design does have a few drawbacks. First, the predictive ICB response power of the TMEIG score was assessed in melanomas and metastatic urothelial cancer. These data must be verified using patients with CRC. Second, the relationship between the five molecules of the TMEIG score system and tumorigenesis, immune system, and ICB response were not investigated in this study. Future HOXC6 may be the most significant.In conclusion, we identified the TME subtypes that comprehensively depicted the TME, revealed multiple aspects of CRC biology, and assessed variation in the prognosis of patients with CRC. TMEIG score is a robust marker to predict patients’ prognosis and may serve as a predictor of ICB response in CRC. Moreover, we identified several potential targets that may play a critical role in ICB treatment, of which


Thisrepresents the first step toward the computation of highly accuraterotational constants for both the ground and first excited fundamentalvibrational levels, which reproduce the available experimental datawell within 0.1%. Centrifugal distortion parameters and vibrational–rotationalcoupling terms are computed as well and used to solve some conflictingexperimental results. Simulation of the vibrational transition frequenciesand intensities beyond the double harmonic approximation and up tothree quanta of vibrational excitation provides insights into thecouplings ruling the vibrational dynamics and guides the characterizationof the gas-phase infrared spectrum experimentally recorded in therange of 200–5000 cm–1. The full characterizationof the IR features is completed with the experimental determinationof the absorption cross sections over the 400–5000 cm–1 region from which the radiative forcing and global warming potentialof HFO-1123 are derived.Very short-lived substances have recently been proposedas replacementsfor hydrofluorocarbons (HFCs), in turn being used in place of ozone-depletingsubstances, in refrigerant applications. In this respect, hydro-fluoro-olefins(HFOs) are attracting particular interest because, due to their reducedglobal warming potential, they are supposed to be environmentallyfriendlier. Notwithstanding this feature, they represent a new classof compounds whose spectroscopic properties and reactivity need tobe characterized to allow their atmospheric monitoring and to understandtheir environmental fate. In the present work, the structural, vibrational,and ro-vibrational properties of trifluorothene (HFO-1123, F In fact, the Montreal protocol that was enforcedin 1987 has been phased out with a few exceptions: the use of CFCssince 1996 due to their capacity to catalytically destroy stratosphericozone molecules. This feature comes from the chlorine atom that, oncein the stratosphere, acts as a catalyst in the ozone depleting reactioncycles. In addition, CFCs are also greenhouse gases because they stronglyabsorb the electromagnetic radiation within the atmospheric windowlocated in the 8–12 μm wavelength spectral region. Indeed,these molecules generally show very strong infrared (IR) absorptionbands due to C–F and C–Cl stretching vibrations. Tomake things worse, CFCs generally have long atmospheric lifetimesthat on the one side favor their accumulation in the atmosphere andon the other side contribute even more to their global warming activityin the long period. As replacement gases, HCFCs and HFCs with analogouschemical and physical properties have been proposed. Between the two,HFCs have appeared to be the more convenient option because, giventhe absence of chlorine atoms, they cannot contribute to ozone loss.Furthermore, these molecules present shorter atmospheric lifetimesthan CFCs, because the C–H bond undergoes a reaction towardhydroxyl radicals in the troposphere.4 Nevertheless,these gases still contribute to global climate changes as the presenceof the C–F chromophore makes them greenhouse gases that absorbIR radiation around 9 μm.Halogenated organic molecules,when released into the environment,influence atmospheric chemistry and, hence, Earth’s climateby contributing to either ozone depletion or global warming. Due tothis role as trace gas pollutants, over the years, halocarbons havebeen the subject of many experimental and theoretical investigationsaimed on the one side at allowing their monitoring and on the otherat understanding their atmospheric reactivity.2C=CHF), known as HFO-1123,as the main component of refrigerant blends characterized by extremelylow GWPs. As such, HFO-1123 is emerging as a new molecule in the refrigerationmarket, and it is expected to be a next-generation refrigerant forair conditioners.5 From a chemical pointof view, presenting unsaturated C=C bonds, HFOs have low chemicalstabilities with a consequent shortening of the atmospheric lifetimethat is, in turn, at the basis of the low GWP. This increased chemicalreactivity however may cause self-decomposition reactions, and infact, it has been reported that HFO-1123 undergoes a disproportionationreaction under high-temperature and high-pressure conditions.7 As such, the thermodynamic properties of this molecule as well asits binary mixtures with other fluorinated refrigerants have beenstudied in recent years . Low-GWP molecules are currently being developedin order to meet the new regulation, and hydro-fluoro-olefins (HFOs)have appeared to be a good solution for applications in the fieldof refrigeration and air conditioning. In 2014, Asahi Glass Co., Ltd.employed trifluoroethene  and with purposelytailored data-analysis software,28 aids and speeds up thehuge and time-consuming task of interpreting high-resolution spectra.Among the different techniques available for environmentalandprocess monitoring, one of the most effective and easy-to-use is IRspectroscopy whose successful application, however, depends on theavailability of accurate spectroscopic data, above all the transitionfrequencies and intensities, for the species of interest.–1 for fundamental frequencies and a few km mol–1 for IR intensities.30 Concerninghalocarbons, it has been demonstrated that such accuracy can be reachedusing the coupled cluster theory with singles, doubles, and a perturbativeestimate of the triples, CCSD(T),32 coupled tolarge basis sets.35 An alternative and more cost-effective approach isrepresented by QM/QM' computational protocols involving CCSD(T)-basedcomposite schemes for geometry and harmonic properties,36 coupled with anharmonic effects described throughdensity functional theory (DFT).38On their own, vibrational investigations are ever increasinglysupported by quantum chemical (QC) calculations that work as a guideand a support for the assignment of the experimental signals. In fact,reliable predictions of vibrational transition frequencies and IRintensities can be obtained beyond the rigid-rotor-harmonic-oscillatormodel without the need of any empirical scaling factor and with afull account of anharmonic resonances with accuracies within 10 cm39 which was later refined using microwave andelectron diffraction data.40 The rotationalspectra were reanalyzed by obtaining centrifugal distortion termssome years later,41 while more recently,improved rotational parameters have been determined for the groundand some low lying vibrational excited states together with the analysisof the ro-vibrational spectrum of the ν6 band.42 The vibrational spectrum of trifluoroethenewas measured a long time ago,43 while inthe middle of the 70s, it was considered in a study devoted to explorethe C–H stretching frequencies in a series of halogenated ethenes.44 Some high-resolution investigations were carriedout in more recent times with the aim of determining the molecularparameters in some excited vibrational levels, even though the observedirregularities in the spectra patterns, due to Coriolis and anharmonicinteractions, were only recognized without any formal treatment ofthe couplings.47 The vibrational properties of HFO-1123 were the subjectof theoretical computations within the harmonic approximation,49 and more recently, Jiang et al. compared the available experimentaldata on fundamental frequencies with the corresponding values computedby scaled ab initio calculations using the B3PW91 functional.50Themicrowave spectra of HFO-1123 were investigated about 50 yearsago, leading to the determination of the ground and some excited vibrationallevel rotational constants and to an effective structure,–1 and the determination of integrated absorption cross sections isperformed over the 400–5000 cm–1 range. Thevibrational analysis is guided by state-of-the-art quantum chemicalcalculations involving hybrid CCSD(T)-based composite schemes andDFT anharmonic calculations. These are combined into hybrid forcefields from which vibrational energies, transition strengths, rotationalparameters, and coupling constants are determined.As the characterization of the vibrationalspectrum of trifluoroethenedates back to almost 70 years ago and an accurate modeling of itsro-vibrational spectroscopic properties has never been undertaken,the present work deals with a combined experimental and theoreticalstudy of the vibrational and ro-vibrational properties of HFO-1123in order to fill these gaps in view of the renewed interest in thisspecies for applications as a refrigerant of the next generation.Experimentally, the vibrational quantum assignment of the gas-phaseIR spectrum is carried out between 200 and 5000 cm2–1, the spectrawere recorded at a resolution of 1.0 and 0.5 cm–1 using a Spectrum One (PerkinElmer) interferometer and a glass cell equipped with KRS-5 windows. In the rangeof 400–5000 cm–1, the spectra were measuredby a Bruker Vertex 70 spectrometer with a resolution ranging from1.0 to 0.2 cm–1 and using a double-walled, stainless-steelcell  equipped with KBr windows. Cross sectionmeasurements were performed at a resolution of 0.5 cm–1, keeping the cell temperature constant at 293.0 K (±0.5 K).The pressure of the sample was varied in the range of 1.1–222.5hPa, and for each experimental run, 128 interferograms were collectedto maximize the signal-to-noise ratio. Following the methodologiesestablished in previous works,51 distortions due tofinite-resolution effects52 were minimizedby mixing the sample with N2  uptoa total pressure of 101 kPa. Adsorption of the sample on the cellwalls, checked by monitoring the pressure inside the cell and inspectingthe absorption spectra, was found to be negligible over a period longerthan that needed to obtain a spectrum. The gas sample of F2C=CHF (99% pure), supplied by Peninsular Chemical Research,Inc., was used without further purification.To record thegas-phase IR spectra, two different Fourier-TransformIR (FTIR) spectrometers were used according to the spectral region.In the range between 200 and 400 cm354Cn) extrapolation to the completebasis set limit (CBS) at the Hartree–Fock self-consistent fieldlevel55 and to the n–3 CBS extrapolation for the correlation contribution,56 respectively. In these expressions, n represents the cardinal number of the Dunning’sfamily of correlation consistent basis sets, and specifically, theHF-SCF extrapolation was carried out using the cc-pVnZ with n = T, Q, and 5,58 while n = T and Q were employed for the correlation contribution.The third term, 59 The second compositemethod relies on the cheap scheme (ChS), where the additivity relationis applied directly on the value of the geometrical parameter.61 Briefly, it starts from the geometry optimized at the CCSD(T)/cc-pVTZlevel and, on top of it, adds contributions accounting for the extrapolationto the CBS and core–valence correlations, evaluated accordingto the second-order Møller–Plesset (MP2) perturbationtheory62 in order to limit the computationalcost. The best estimates of the harmonic vibrational frequencies werealso obtained by applying the ChS, while for IR intensities and centrifugaldistortion constants, the following expression was employed:63ICCSD(T)/VTZ is the harmonic IR intensity obtained at the CCSD(T)/cc-pVTZ level; ΔIT–Q represents the correction due to the enlargementof the basis set obtained as the difference between the MP2 valuesobtained with the cc-pVQZ and cc-pVTZ basis; ΔI(aug) takes into account the effects of diffuse functions, whichare particularly relevant for intensity calculations ; ΔI(CV) has its usualmeaning. All the required harmonic force fields were obtained throughanalytic evaluation of the Hessian matrix.The equilibriumstructure of HFO-1123 was determined accordingto two different composite schemes relying on the CCSD(T) method.The first one exploits the additivity relation on the energy gradientused in the geometry optimization:65 full anharmonic calculationswere carried out at the DFT level byadopting the double-hybrid B2PLYP66 andrev-DSDPBEP8667 functionals coupled tothe aug-cc-pVTZ and jun-cc-pVTZ basis sets68 as well as the PW6B95 functional69 inconjunction with the jul-cc-pVDZ basis set.68 All the functionals were corrected for dispersion correlation effectsby means of the Grimme’s DFT-D3 method70 with the use of the Beck-Johnson damping.71 Cubic, and semidiagonal quartic force constants, and up to the third-derivativesof the dipole moment were obtained by numerical differentiation ofdisplaced analytic Hessians and electric dipole moment first derivativesalong the normal coordinates, respectively. Vibrational correctionsto rotational constants, vibrational wavenumbers, and IR intensitiesbeyond the double-harmonic approximation were obtained by applyingvibrational perturbation theory to second-order (VPT2)74 on the anharmonic force field. To improve the accuracy of the computedvibrational frequencies, a hybrid force field65 was devised by mixing the harmonic vibrational wavenumbers evaluatedin the frame of the ChS with the cubic- and quartic-force constantscomputed by means of double-hybrid functionals. In order to tacklethe problem of resonances plaguing the VPT2 approach, generalizedsecond-order vibrational perturbation theory (GVPT2) was adopted inwhich (near-) singular terms are removed from the perturbative summations and the energy levelscoupled by the resonances are treated in a second step by a propervariational calculation of reduced dimensionality.76 All DFT and MP2 calculations were performed with the Gaussian 16quantum chemical package,77 while for CCSD(T)computations, the CFOUR program78 was employed.Spectroscopic parameters were evaluated according to the general VPT2engine, implemented within the Gaussian software, and IR intensitiesevaluated up to three quanta of vibrational excitation.79On the basis of therecent literature,42C=CHF molecule is a planar near-prolateasymmetric rotor belonging to the Cs symmetrypoint group. It has 12 normal modes of vibration classified in termsof symmetry species, as 9A′ ⊕ 3A″, with A′ vibrations producingA/B hybrid bands with different contributions of the components and A″ vibrations giving rise C-type band envelopes.The F4.154 An inspection of the table reveals that the CCSD(T)/cc-pVTZ andthe double-hybrid functionals yield predictions of the equilibriumgeometry of similar quality with differences, measured with respectto the CCSD(T)/CBS+CV structure, up to 0.007 Å and about 0.3°for distances and angles, respectively, while at the PW6B95, deviationsincrease slightly. The ΔCBS and ΔCV contributions aresignificant for the improvement of the CCSD(T)/cc-pVTZ accuracy. Thisis particularly true for bond lengths, systematically overestimatedat the CCSD(T)/cc-pVTZ level (between 0.002 and 0.007 Å), forwhich both contributions are negative and range from −1 to−4 mÅ. Less systematic are the corrections for the bondangles for which the ΔCBS term ranges between −0.3°(∠ C1C2F6) and 0.4° (C1C2H3), while the ΔCV contributionis almost negligible. A comment is deserved concerning the comparisonto the experimental structure derived by Mom et al.40 When this geometry is compared against theoretical predictions,huge differences as large as 0.025 Å (C2—H3) and −1.1° (∠ C2C1F5) are noticed. In part, this is due to the fact thatref /CBS+CV composite schemethrough rev-DSDPBEP86-D3/jun-cc-pVTZ vibrational corrections. Theresulting parameters are in very good agreement with the correspondingexperimental counterparts with discrepancies of 0.06%, thus confirmingthe accuracy of the CCSD(T)/CBS+CV equilibrium geometry as well asthe reliability of the rev-DSDPBEP86 vibrational contributions. Analogousresults (not reported) have been obtained using both the ChS and theNano-LEGO augmented rev-DSDPBEP86 equilibrium rotational constants.The accuracy of the ground state rotational constants evaluated atthe CCSD(T)/cc-pVTZ, B2LYP-D3/aug-cc-pVTZ, and rev-DSDPBEP86-D3/jun-cc-pVTZlevels of theory are similar with rev-DSDPBEP86 providing slightlybetter results and mirror that of the geometry: the three methodsunderestimate the experimental values and show deviations around −0.9%,−0.7%, and −0.6%, respectively. Upon inspection of 82 The ChS results reproduce quarticcentrifugal distortion parameters very well, the absolute percentagedeviations being in the range of 0.3–2.6%, thus sensibly improvingthe CCSD(T)/cc-pVTZ outcomes in all cases but the ΔJK term.The spectroscopic parameters relevant for rotationalspectroscopy,specifically equilibrium and ground state rotational constants aswell as quartic centrifugal distortion terms, are summarized in 42 within the Ir representation of the Watson’s A reducedHamiltonian.81 The inspection of this tablereveals huge discrepancies between the two sets of experimental values,even though sextic centrifugal distortion parameters determined inref  and−19.7 cm–1 (ω3) and usuallyare of a larger magnitude than the ΔCBS term. Conversely, thecore–valence correlation yields positive corrections, generallyof smaller magnitude, being 6 cm–1 at most in thecase of the ω2 normal mode. Summed up, the differentcontributions cause a blue-shift of the CCSD(T)/cc-pVTZ harmonic wavenumbersfor all the vibrational normal modes, which reaches −36.6 and−30.1 cm–1 for the ω3 andω4 vibrations, respectively. Concerning the intensities,all three corrections can be either positive or negative and, in absoluteterms, they are generally small. In particular, core–valencecontributions are always lower than 0.7 km mol–1, and in general, they are almost negligible. Likewise, the ΔI(aug) term is always lower than 0.8 km mol–1 with exceptions being given by ω4 and ω5 for which it amounts to 8.1 and 5.1 km mol–1, respectively, and ω1 for which it is 1.6 km mol–1. The latter is small in absolute value but, in relativeterms, it represents 14.1% of the overall ChS harmonic IR intensityfor this normal mode. The largest CBS correction, for the ω4 vibration, is around 9 km mol–1, whichcorresponds to only 5% of the overall intensity. Conversely, the CBScorrection for ω1 amounts to 1.9 km mol–1, which accounts for almost 17% of the ChS IR intensity.Harmonic wavenumbers and IR intensities calculated within the double-harmonicapproximation at different levels of theory are summarized in –1 at the B2PLYP-D3/aug-cc-pVTZand rev-DSDPBEP86-D3/jun-cc-pVTZ levels of theory, respectively. Forthe former, the maximum deviations, around 14 cm–1 but in opposite directions, are observed for ω4 and ω11, which is also responsible for the largestdeviation (18 cm–1) observed among the rev-DSDPBEP86-D3harmonic frequencies. This functional shows a better agreement tothe ChS harmonic intensities than B2PLYP-D3. Indeed, the former presentsa MAD of c.a. 2 km mol–1 and maximum discrepanciesin the range of −2.6–10 km mol–1,while the latter reproduces the best estimates with a MAD of 5 kmmol–1 and deviations between −14.5 and 20.8km mol–1 for the intensities of normal modes 3 and5, respectively. As expected, the hybrid PW6B95-D3 functional showsslightly larger deviations for harmonic wavenumbers with a MAD ofaround 13 cm–1 and maximum deviation of about 35cm–1 for ω2. On the other hand,it well reproduces ChS harmonic intensities with the MAD being 3.6km mol–1.When the ChS best estimates are taken as the reference,it is seenthat the double-hybrid functionals yield, on the whole, similar predictionswith a mean absolute deviation (MAD) from ChS harmonic frequenciesof 6.7 and 4.0 cm4.3–1, a maximum error of 10 cm–1, and anharmonic IR intensities with an error of a few km mol–1; hence, ChS:rDSD simulations have been used to guideand support the analysis of the observed spectra.65Fundamental vibrational frequenciescalculated at different levels of theory are presented in –1 and 400–5000 cm–1 arereported in 9 vibration, corresponding to the CF2 rocking is located at 232.9 cm–1, and the ν12 normal mode, due to the C=C torsion, measured at302.7 cm–1 in agreement with previous measurements.43 Both of them perfectly match the theoreticalpredictions of the ChS:rDSD hybrid force field that places the ν9 and ν12 transitions at 232 and 304 cm–1, respectively. In addition to the fundamentals, the2ν12–ν12 hot band has beenobserved, as detailed in –1, and only four bands fall outside this range.While deviations appear randomly scattered around zero, the only dependencethat can be inferred to some extent is that most experimental valuesare underestimated below c.a. 2000 cm–1, a trendthat reverses moving toward higher frequencies. At room temperature,the populations of the v9 = 1 and v12 = 1 levels are about 32% and 23% of the vibrationalground state; hence, a number of hot bands has to be expected in additionto cold transitions.Survey gas-phase IR spectra of HFO-1123 in the rangesof 200–400cm–1 features a number of absorptions mainly related to the fundamentaltransitions ν8, ν7, ν10, and ν6 and their hot bands involving,as expected, the v9 = 1 and v12 = 1 excited levels. Among these, the most prominentones are ν6, the CF2 symmetric stretching,and ν10, the CHF wagging, with a predicted intensityof 54.9 and 29.3 km mol–1 and located at 929.5 and750.6 cm–1, respectively, in perfect agreement withquantum chemical predictions (929 and 749 cm–1).The CHF rocking and CF2 scissoring motions correspond tothe ν7 and ν8 normal modes, respectively,that have similar intensities and give rise to the bands observedat 623.8 and 484.9 cm–1. In between these two bands,the very weak ν11 feature, stemming from the CF2 wagging motion, can be detected at 553.8 cm–1 only in the spectra recorded at the higher pressures, again in perfectaccord with the ChS:rDSD predictions (554 cm–1)that significantly improves the results obtained from the rev-DSDPBEP86and PW6B95 functionals. The fundamental transitions ν5, ν4, and ν3, associated, respectively,to the C–F stretching of the CHF group, the CF2 asymmetricstretching, and the CH in-plane bending motion cover the 1000–1700cm–1 spectral portion. The ν5 andν4 modes give rise to two A/B hybrid bands with aprevalence of A-type character located at 1172.4 and 1264.8 cm–1 and with a predicted intensity of 51.4 and 175.7km mol–1, respectively. The v5 = 1 level is also involved in a type II Fermi interactionwith the v6 = v9 = 1 level, whose corresponding combination band is observed on thelow-frequency side of ν5 at 1155.1 cm–1. According to the ChS:rDSD hybrid force field, this two-level systemcan be described by the following interaction matrix, where diagonal elements are deperturbed and the off-diagonalcoupling element is the cubic force constant ϕ569/√8. The eigenvalues of this matrix are 1153 and 1171 cm–1 in optimal agreement with the measured transitionfrequencies. The ν3 vibration appears at 1360.8 cm–1 with a B-type band envelope and an intensity (accordingto the ChS:rDSD computations) of 113.3 km mol–1.The remaining relevant absorptions in this spectral region includethe first overtones of ν11 and ν10 at 1106.8 and 1498.1 cm–1, respectively, and theν10 + ν11 combination measured at1304.1 cm–1, all showing an A/B-type envelope withprevailing A-character. When one focuses on the portion between 1700and 3500 cm–1, the strongest features come fromthe ν2 fundamental, corresponding to the stretchingof the C=C double bond and measured at 1787.4 cm–1 and ν1 centered at 3163.7 cm–1 associated with the C–H stretching. The latter is involvedin a quite strong Fermi resonance of type II with the nearby ν2 + ν3 combination appearing at 3119.5 cm–1 as an A/B-type band of intensity comparable to thatof ν1. Using deperturbed frequencies obtained atthe ChS:rDSD level, this resonance can be modeled by the followinginteraction Hamiltonianwith eigenvalues 3116 and 3173 cm–1, andthe perturbed levels are almost a 1:1 mixture of the unperturbed ones.In addition, the region presents a large number of weaker featuresmainly due to two quanta combinations bands, as detailed in –1, shows a number of weak absorption features attributable to binaryand ternary combination vibrations with an intensity within 0.5 kmmol–1. Among these, the most important ones comefrom the first overtone of ν2 (3560 cm–1) and the binary combinations involving the ν1 vibration,namely, ν1 + ν7 (3784.9 cm–1), ν1 + ν6 (4087.9 cm–1), ν1 + ν5 (4330.5 cm–1), ν1 + ν4 (4419.6 cm–1), ν1 + ν3 (4523.7 cm–1), and ν1 + ν2 (4951.0 cm–1). These combination levels of the type v1 = vj = 1 are expected to inherit theFermi resonance ν1/ν2 + ν3 and, hence, to be involved to various extents in 1–2interactions with the corresponding three quanta combination levels v2 = v3 = vj = 1 that in the present investigation havebeen treated by manually setting up the proper interaction matriceson the basis of the computed quantities, resulting in an optimal agreementwith the experiment. In particular for v2 = v3 = v5 = 1, the resonant systems involving ν1/ν2 + ν3 and ν5/ν6 + ν9 intersect, giving rise to a four-levelsystem that using the spectroscopic parameters evaluated from theChS:rDSD hybrid force field has been described by the following interactionHamiltonian:The spectral portion between 400 and 1000 cmUpon diagonalization, the following eigenvalues and eigenvectorsare obtained:–1, the ν2 + ν3 + ν5 and ν1 + ν6 + ν9 bands move to 4281 and 4321 cm–1, respectively, and the ν1 + ν5 combination is expected around 4339 cm–1. Among these, only the ν2 + ν3 + ν5 and ν1 + ν5 transitions are expected to produce weak but observable absorptions,and indeed, the perturbed transition frequencies well reconcile withthe bands observed at 4280.8 and 4330.5 cm–1. Inpassing, it should be noted that the band here assigned as 2ν2 was originally attributed to a three quanta combination inref , measuredat each wavenumber, ṽ, against the sampleconcentration. The slope of the straight line so obtained at eachwavenumber, σ(ṽ), provides the absorbancecross section per molecule (cm2 molecule–1) with the statistical uncertainty obtained in a similar way. Inpassing, it should be noted that the procedure yields IR intensitiesin very good agreement with those determined by the line-by-line analysisof high-resolution spectra  in the 400–5000cmle, refs , and 86)–1 accountsfor almost 50% of the total IR absorption strength measured over the400–5000 cm–1 range. As previously pointedout, it hosts the absorptions stemming from the ν3 and ν4 normal modes involving the asymmetric stretchingof the CF2 moiety and predicted with an anharmonic intensityof 119.7 and 196.4 km mol–1, respectively. Next,the C–F stretching motion of the CHF group (ν5) is located within the 1000–1205 cm–1 spectralrange within the atmospheric window, which accounts for about 23%of the overall integrated band intensity. In particular, accordingto ChS:rDSD anharmonic computations, it has an intensity of 75.5 kmmol–1, almost half with respect to the harmonicvalue (140.4 km mol–1). The reason for this weakeningaccounting for both mechanical and electrical anharmonicity has tobe sought in the aforementioned Fermi type II interaction with ν6 + ν9 that borrows intensity from the fundamental,and indeed, its strength is predicted to be 57.9 km mol–1. The ranges 820–1000 and 1705–1950 cm–1 each contain about the 10% of the total measured intensity. Theformer, still within the atmospheric window, presents ν6, the symmetric CF2 stretching, as the only relevantcontribution, while the latter is dominated by the ν2 C=C stretching vibration whose IR intensity is predictedto be 59.3 km mol–1. Then, the spectral intervals670–820 cm–1 (27 km mol–1) and 2985–3250 cm–1 (8.5 km mol–1) contribute to about 5% and 1% of the overall integrated band intensity,respectively. Their measured intensities are mainly due to the ν10 vibration, whose computed intensity is 29 km mol–1 and the ν1 and ν2 + ν3 bands. In the latter case, as well, the combination vibrationsteals intensity from the C–H stretching vibration, thus reachingan absorption cross section of about 4 km mol–1,while the ν1 fundamental loses about 50% of its strength(from 11.4 to 6.3 km mol–1) upon introduction ofanharmonic effects. The remaining spectral portions account for lessthan 1% of the total integrated absorption cross section measuredover the whole 400–5000 cm–1 range. Overall,a good accord between the measured intensities and the correspondingtheoretical counterparts is noted for the different integration ranges,the mean absolute error of around 4 km mol–1 beingcoherent with the expected accuracy.65As to the greenhouse effect exerted by this molecule,the mostimportant spectral regions are, as expected, those featuring the strongabsorptions due to the C–F stretching vibrations. In particular,the spectral region between 1000 and 1445 cm–2 ppbv–1. Sincethis approach might not be well suited for short-lived molecules likeHFO-1123, the value determined should be taken as an estimate of theactual RF, although the data computed in this way are in good agreementwith those obtained using a more sophisticated model on a similarhalogenated olefin.88 To the best of ourknowledge, there is no data about the atmospheric lifetime of HFO-1123,which however is required in order to evaluate the global warmingpotential index. On the basis of the available literature,89 an atmospheric lifetime of 10–30 daysappears as a reasonable upper limit guess, leading to an estimatedGWP between 2.0 and 5.3 (taking CO2 as the reference) ona time horizon of 100 years.Finally, whenone utilizes the measured cross section spectrumand the narrowband model of ref , the HFO5kβ vibration–rotation constants and the ζkla Coriolis couplingterms, the computed data can be used to drive further high-resolutionvibrational–rotational investigations. Attention has then beenmoved to the integrated experimental–theoretical investigationof HFO-1123 vibrational spectra. From the theoretical point of view,accurate harmonic frequencies and IR intensities have been obtainedusing the ChS method, and subsequently, these have been combined withanharmonic contributions evaluated using the rev-DSDPBEP86 functionalthat has been shown to provide excellent performances for the computationof vibrational transition frequencies and intensities. The anharmonicspectra simulated in this way have been used to assist the characterizationof the experimental gas-phase spectra. To this end, the observed absorptionfeatures have been identified as fundamentals, overtones, a combination,and hot bands up to three quanta of vibrational excitations, and theanalysis resulted in the assignment of almost 120 vibrational transitionsin the 200–5000 cm–1 spectral range. A numberof 1–2 Fermi resonances have been recognized and consistentlytreated, thus explaining the observed spectral pattern and intensityalterations. Furthermore, the integrated absorption cross sectionshave been experimentally determined for the spectral region between400 and 5000 cm–1, reporting also in this case avery good agreement with the theoretical predictions that have furnisheda quantitative description of the overall band shape over the wholespectral region investigated in the present work. Finally, the obtainedspectroscopic data have been employed to obtain an estimate of theHFO-1123 radiative forcing. From this, an upper limit of the globalwarming potential has been determined, which is of interest in viewof the use of HFO-1123 for refrigeration applications.Several research effortshave been devoted to the study of theinfrared spectroscopic behavior of both HFCs and HCFCs with the aimto assess the environmental impacts of these compounds. Indeed, itis important for radiative transfer calculations and atmosphericalmodels to include data about their absorptions, and furthermore, accuratecharacterizations of their spectral properties constitute the basicrequirement for monitoring their concentration in the atmosphere orwork places. The present work aimed at investigating the structuraland rotational/vibrational spectroscopic behavior of HFO-1123, oneof the next generation refrigerants proposed as a replacement forHFCs, which has a supposedly shorter atmospheric lifetime and hencea lower impact on global warming. In particular, an accurate equilibriumgeometry has been first worked out by applying post-Hartree–Fockcomposite methods. On the one side, this has been the first step towardthe detailed modeling of its spectroscopic features, particularlythose of relevance for rotational spectroscopy. On the other side,it represents the most accurate equilibrium geometry presented upto date for this species. The accuracy reached in the geometry hasbeen mirrored in the accuracy in the computed ground state- and excitedfundamental vibrational level- rotational constants of HFO-1123, whichshowed a remarkable agreement with the available experimental datawith deviations well within 0.1%. Quartic and sextic centrifugal distortionparameters have been evaluated as well, addressing the dichotomy existingon the experimental data determined for the latter terms. Togetherwith the α


A large amount of publications had reported the association between incidence of esophageal cancer (EC) and type 2 diabetes mellitus (T2DM) in the past decade. However, those papers' results are inconsistent on relationships between T2DM the incidence of EC. Therefore, the objective of this meta-analysis was to determine the relationship between T2DM and the risk of EC .  We finally extracted 19 articles though Pubmed, Embased, and Cochrane library. Those identify extraction date including 14,312 cases and 24,959,067 control records and then mixed the relative risks (RRs) and corresponding 95% confidence intervals (95%CIs) through STATA. RR = 1.28, 95% CI: 1.05-1.57, P = 0.015).Also, our study showed positive correlation between T2DM and EADC  risk . What's more, subgroup analysis based on ethnicity represented the Caucasian is more susceptible to EC .  We observed that there are significantly positive correlation between T2DM and EC risk ( Those results offer a recent epidemiological and integrated evidence to ascertain the correlations between T2DM and incidence of EC. Those results take public health implications on preventing T2DM and then depress the occurrence of EC. Our study also provides referenced information for the prevention. However, some data is still insufficient, and more research should be carried out. Esophageal cancer has become the commonest cancer around the world in the top eight and the cause of sixth cancer-related mortality, with the five-year survival ratio is not even close 20% , 2. TherMore and more studies represent type 2 diabetes mellitus (T2DM) could increase incidence of gastroenteric tumors now, like liver cancer and pancreatic cancer –23. In p∗} OR {noninsulin∗}” AND“{depend∗}“AND “{odds ratio} OR {OR} OR {hazard risk} OR {hazard ratio} OR {HR} OR {relative risk} OR {RR} OR {rate ratio} OR {P} OR {P value} OR {P=} OR {association} OR {associated} OR {confidence interval} OR {CI} OR {censor} OR {Kaplan-Meier} OR {Cox model} OR {Proportional hazard model} OR {log-rank}.” We selected those publications by screening their title, abstract, and even full article if necessary.All English literatures were searched from Pubmed, Embase, and Cochrane (June 1994 to October 2020) by searching for terms, with the following text word or Medical Subject Heading (MeSH) terms in the title or abstract: “{esophagus} OR {esophageal} OR {oesophagus} OR {oesophageal}” AND“{cancer} OR {carcinoma} OR {adenocarcinoma} OR {squamous cell carcinoma} OR {tumor} OR {malignant} OR {malignancy} OR {neoplasm} OR {neoplasia} OR {oncology}” AND“{diabetes mellitus} OR {diabetes complications}” AND“{diabet}” AND“{ IDDM} OR { NIDDM} OR {MODY} OR {T1DM} OR {T2DM } OR {T1D} OR {T2D}” AND“{inculinWe comply with following criteria to select publications/literatures: (1) The exposure factor was T2DM (or contains T2DM); (2) the prognostic indicator are the risk of EC or its subtypes; (3) studies were all written in English; and (4) provide relative risk (RR) estimates and their 95% confidence intervals (95% CI), or report raw data from which the RR and 95% CI can be calculated. The exclusion criteria are as follows: (1) no or insufficient information is available to calculate the relevant estimates; (2) the outcome indicator was cancer mortality or survival rate (not incidence). (3) patients diagnosed with T1DM .We (Dr. Zhou. and Dr. Huang) selected and assessed literatures  independently, and any discrepancies were resolved by consensus. Jadad quality assessment rating scale can be divided into 2 level. The scale range between 0 to 3 is considered as a low quality research. On the contrary, the scale range between 0 to 3 is considered as a high quality research. In addition, ethnicity was classified into Asian and Caucasian.Two investigators (Dr. Zhou. and Dr. Huang) collected following relevant date from included literatures separately: author, publish time, country/region, ethnicity, study design, assessment method, comparison of population, source/study population, cancer type, controls/cohort size, follow-up years, risk factors, relevant estimate type (odds ratio (OR), RR, hazard ratio (HR), and 95% CI), and the adjustment variables. Finally, Dr. Luo and Dr. Wang checked all data. If we get a disagreement, we will solve it by further discussion.I2 values [I2 is divided this meta-analysis results into three grades: ≤25%, 25-50%, and ≥50 . The stability of the results was evaluated by the sensitivity analysis so that we can try to explain heterogeneity. The publication bias of each study was assessed by Egger's test. When P < 0.05, we decide to think the difference was statistically significant.All extracted data from included literatures meta-analysis processed by STATA 16.0. Also, all extracted data was regarded as a binary variable. The association between incidence of EC and T2DM were evaluated by RRs and 95% CIs by random-effects models. Heterogeneity among studies was evaluated by Cochran's Q test and 2 values , 25. I2 We searched the initial total 1566 literatures through Pubmed, Embase, and Cochrane. We finally screen out total 19 literatures –44 afterRR = 1.28, 95%CI = 1.05–1.57, I2 = 96.9%, I2 = 96.9%, P < 0.001), we analyze the all data in random-effects models.As presented in meta-analysis, a significant association was found between T2DM and risk of EC  and Egger test (P = 0.479), P = 0.806) and Egger test (P = 0.573), P = 0.536) and Egger test (P = 0.731), Moreover, we decided to adopt Begg funnel chart and Egger's test to test the potential publication bias. We found no statistic significant publication bias in our main study (T2DM and EC risk)  some study still missed by our comprehensive search. (2) Different studies list many different adjusted factors ) which may influence our meta-analysis results. (3) Trying to find out source of significant heterogeneities, we performed subgroup analysis. Unfortunately, only ethnicity could be responsible for parts of heterogeneity. (4) Included studies take different historical types as research subjects. So our result of ORs and 95% CI should be explained carefully.Summarily, this study suggested that T2DM had positive association with a growing EC risk, especially EADC and between Caucasian. Hence, for T2DM patients, they should focus not only on treating diabetes, but also on early screening for cancer, especially like EC whose early symptoms hard to detect. Also, our study can provide some useful information for T2DM patients and help them help the implementation of the individualized therapeutic interventions in order to reduce the occurrence of EC risk. However, some data is still insufficient, and more well-designed prospective study should be carried out.


To test this, we quantified Xi and Xa gene expression in individuals with one Xa and zero to three Xis. Our linear modeling revealed modular Xi and Xa transcriptomes and significant Xi-driven expression changes for 38% (162/423) of expressed X chromosome genes. By integrating allele-specific analyses, we found that modulation of Xa transcript levels by Xi contributes to many of these Xi-driven changes (≥121 genes). By incorporating metrics of evolutionary constraint, we identified 10 X chromosome genes most likely to drive sex differences in common disease and sex chromosome aneuploidy syndromes. We conclude that human X chromosomes are regulated both in cis, through Xi-wide transcriptional attenuation, and in trans, through positive or negative modulation of individual Xa genes by Xi. The sum of these cis and trans effects differs widely among genes.The “inactive” X chromosome (Xi) has been assumed to have little impact, in   •Analyzed gene expression in sex chromosome aneuploidy samples using linear models•Xi and Xa transcriptomes are modular•cis and in trans38% of X chromosome genes are affected by Xi copy number—in •10 X chromosome genes likely contribute to male-female differences in somatic tissues cis, they find that Xi modulates Xa transcript levels in trans. They identify 10 X chromosome genes most likely to contribute to male-female differences in common disease.Through RNA sequencing of individuals with sex chromosome aneuploidy, San Roman et al. identify modular “active” (Xa) and “inactive” (Xi) X chromosome transcriptomes. Looking beyond classical X inactivation, which acts in  By combining ΔEX findings with allele-specific analyses performed in the same cell lines and comparing our results with published, independent annotations of genes subject to XCI, we found that Xi positively or negatively modulates steady-state levels of transcripts of at least 121 genes on Xa, in trans. Thus, Xi and Xa expression are highly interdependent. By combining ΔEX with published gene-wise metrics of evolutionary constraint, we identified a set of 10 Chr X genes most likely to drive phenotypes that are associated with natural variation in Xi copy number. These 10 candidate “drivers” can now be prioritized in studies of sex differences in common disease and in explorations of sex chromosome aneuploidy syndromes.Here, we used a series of quantitative approaches to investigate gene expression from Xi and Xa. Inspired by previous studies, we took advantage of the natural occurrence of diverse sex chromosome aneuploidies in the human population. We performed RNA sequencing (RNA-seq) on two cell types (lymphoblastoid cell lines and primary skin fibroblasts) from 176 individuals spanning 11 different sex chromosome constitutions—from 45,X (Turner syndrome) to 49,XXXXY. We analyzed the resulting data from these 176 individuals using linear regression models to identify significant changes in Chr X gene expression in identically cultured cells with zero, one, two, or three copies of Xi. 38% of Chr X genes displayed significant Xi-driven expression changes, which we quantified on a gene-by-gene basis using a novel metric that we developed called ΔETo conduct a robust, quantitative analysis of Xi’s impacts on X-linked gene expression, we recruited individuals with a wide range of sex chromosome constitutions to provide blood samples and/or skin biopsies A. We gen,,To leverage the full power of our datasets, we compiled all RNA-seq data for each cell type into a single analysis. We included protein-coding and well-characterized long non-coding RNA (lncRNA) genes with a median expression in either 46,XX or 46,XY samples of at least 1 transcript per million (TPM). This resulted in 357 Chr X genes expressed in LCLs and 393 expressed in fibroblasts. Combining these two gene lists, we analyzed 423 Chr X genes in all. These genes reside within structurally and evolutionarily distinct regions B: two psWe hypothesized that each copy of Xi would incrementally increase expression of some Chr X genes, and therefore, for each gene, we modeled expression as a linear function of Xi copy number, controlling for Chr Y copy number and batch . To asseX) by the expression from the single Xa —a metric we refer to as ΔEX . To compare genes expressed at different levels, we also quantified the relative changes in expression per Xi. Specifically, we divided the change in expression per Xi  showed no significant change in expression level with additional copies of Xi (ΔEX ≈ 0), and the same was true for 304 (77%) of 393 Chr X genes expressed in fibroblasts  Chr X genes expressed in one or both cell types. NPX genes’ ΔEX values ranged from −0.39 to 1.2 , similar to previous studies using allelic ratio analysis.,XIST and PUDP—and three PAR1 genes—DHSRX, PLCXD1, and PPP2R3B—showed ΔEX values approaching or exceeding one in both LCLs and fibroblasts.Analyzing ΔEroblasts . This is9 to 1.2 F and 2G.ear zero . The staX values  in LCLs and 25 (13%) in fibroblasts had discordant ΔEX values: at least one isoform’s ΔEX differed significantly from zero , while another isoform’s ΔEX did not , we asked whether ΔEisoforms . For mos did not . The mosehaviors .X values correlated well with the ΔEX values calculated from our RNA-seq data  and Chr 21 genes range from 0.74 to 1. By comparison, NPX genes without NPY homologs had median ΔE in LCLs H. This Yboundary  and S4, X conveys the change in a gene’s expression due to an additional Xi regardless of the mechanism(s) responsible for this change. We hypothesized that a Chr X gene’s ΔEX value could reflect the combined effects of two mechanisms: (1) transcription of Xi allele(s) and (2) modulation of the Xa allele by Xi in trans.ΔEX values and published descriptions of the genes as “escaping” XCI (being expressed from Xi) or being subject to it (silenced on Xi). For this purpose, we curated annotations of each expressed gene’s XCI status from studies of allele-specific expression,,,,X > 0 were classified as expressed from Xi (50/74 in LCLs and 43/61 in fibroblasts), indicating that transcription from Xi alleles underlies their ΔEX values  or downregulated (ΔEX < 0) by Xi.For 102 (24%) of the 423 Chr X genes we evaluated in LCLs or fibroblasts, our calculated ΔEI status . For exaed on Xi . Genes wed on Xi C. These To corroborate these findings in our own dataset, we performed an allele-specific analysis in our LCL and fibroblast samples with two X chromosomes. To distinguish between Chr X alleles, we identified heterozygous SNPs in expressed genes . We thenX values. If Xi and Xa expression are fully independent of each other, and therefore additive, we would expect the AR for a given gene to approximate its ΔEX value. However, if Xi modulates the gene’s Xa transcript levels in trans, then independence and additivity will not be observed, and instead the gene’s ΔEX and AR values will differ. Most X-linked genes, e.g., EIF2S3, had AR values that approximate their ΔEX values, and AR and ΔEX were highly correlated among many informative genes in both LCLs and fibroblasts  but nonetheless had ΔEX values significantly different from zero: 0.24 in LCLs and 0.21 in fibroblasts, indicating that levels of Xa-derived transcripts are positively regulated by Xi. Other genes, like DDX3X and PUDP, had significant expression from Xi  and evidence of Xi regulation of steady-state expression levels. DDX3X had an AR  that is significantly higher than its ΔEX value  in both LCLs and fibroblasts, indicating both that DDX3X is expressed on Xi and that its steady-state transcript levels are negatively regulated by Xi. Conversely, PUDP had an AR  that is significantly lower than its ΔEX value , indicating both that PUDP is expressed on Xi and that the gene’s steady-state transcript levels are positively regulated by Xi.However, for 33 informative genes in LCLs or fibroblasts, AR and ΔEin trans . Some geX with published or newly derived AR data, provide a rich portrait of X-linked gene regulation. They show that Xi can impact expression levels of an X-linked gene through two mechanisms: transcription of the Xi allele and modulation of steady-state transcript levels by Xi in trans. These mechanisms can operate independently of each other, or together, on a gene-by-gene basis, and each of the two mechanisms affects a sizable fraction of all X chromosome genes. Of 423 X chromosome genes expressed in LCLs and/or fibroblasts, at least 121 genes (29%) are modulated on Xa by Xi in one or both cell types  and 46,XX individuals, between 47,XXY (Klinefelter syndrome) and 46,XY individuals, or even between 46,XY males and 46,XX females. We hypothesized that phenotypes and predispositions associated with Xi copy number are due to changes in the copy numbers of some of the Chr X genes where we found positive or negative ΔETo gauge the constraints that selection has imposed on each gene’s expression level, we turned to metrics derived from population and evolutionary genetic studies. We assessed tolerance of under-expression using (1) loss of function observed/expected upper fraction (LOEUF), the ratio of observed to expected loss-of-function (LoF) variants in human populations,CT scoreTo assess tolerance of over-expression, we examined conservation of targeting by microRNAs  to least constrained (low percentile). We calculated percentiles separately for autosomal (including PAR1) and NPX genes and then, for each gene, averaged percentile rankings across the four metrics.X values suggested that they may drive phenotypes associated with Xi copy number. Compared with autosomal genes, PAR1 genes are less constrained on average , with most ranking in the least-constrained quartile  copy number.We first examined expression constraints for PAR1 genes whose high ΔEquartile . This inon group . SHOX coX values  in LCLs or fibroblasts and ranked these by their average constraint metrics. Five of the top 10 genes by these criteria  on steady-state transcript levels for each gene, normalized to account for gene-to-gene variation in expression level  of expressed Chr X genes in these cell types .X and AR values, we discovered that Xi up- or downmodulates Xa expression of at least 121 genes, or nearly 29% of the 423 Chr X genes that are demonstrably expressed in either LCLs or fibroblasts. This modulation is manifest whenever a gene’s AR and ΔEX values differ significantly, and it is most starkly apparent when the gene is not expressed from Xi  but nonetheless displays a significantly positive or negative ΔEX value. While “escape” from XCI has been well documented over the past four decades,,,,X metrics reported here was required to observe modulation, explaining why it has previously been unappreciated.We extended the utility of the ΔEX and AR reveals a nuanced, gene-by-gene tapestry of Xi-driven changes in expression of Chr X genes. For some genes, ΔEX was explained entirely by expression from the Xi allele, while for others, ΔEX was explained entirely by modulation—positive or negative—of steady-state RNA levels derived from the Xa allele. For a third set of genes, ΔEX was explained by the combined effects of expression from Xi and modulation of steady-state RNA levels. Proposals of uniform, chromosome-wide “X chromosome upregulation” (XCU) during mammalian development or evolution,Thus, combined analysis of ΔEX metric with population and evolutionary measures of constraint on expression levels to identify 10 NPX genes that are most likely (among the 423 Chr X genes expressed in LCLs and/or fibroblasts) to drive Xi-associated phenotypes .Further information and request for resources and reagents should be directed to and will be fulfilled by lead contact, David C. Page (Cell lines are available upon request to the lead contact.Adults (18+ years of age) with sex chromosome aneuploidies or euploid controls were recruited through an IRB-approved study at the NIH Clinical Center (12-HG-0181) and Whitehead Institute/MIT (Protocol #1706013503). Informed consent was obtained from all study participants. Individuals with a previous karyotype showing non-mosaic sex chromosome aneuploidy were included in the study. From these individuals, blood samples and skin biopsies were collected at the NIH Clinical Center and shipped to the Page lab for derivation of cell lines. In addition, blood samples from individuals with sex chromosome aneuploidies, and euploid family members, ranging in age from 4-44 years were contributed by the Focus Foundation. Additional LCLs and fibroblast cultures were obtained from the Colorado Children’s Hospital Biobank and Coriell Research Institute, and cultured in the Page laboratory for at least two passages prior to collection for RNA-sequencing. Karyotyping of peripheral blood and fibroblast cell cultures was performed at the National Human Genome Research Institute Cytogenetics and Microscopy Core. To reduce the impact of sex chromosome mosaicism on our sex chromosome aneuploidy analysis, we excluded individuals with >15% mosaicism for other karyotypes. Metadata for cell lines represented in the RNA-sequencing dataset are provided in Blood was collected in BD Vacutainer ACD tubes and shipped at room temperature to the Page Lab for processing 1-3 days after collection. The buffy coat was resolved by centrifuging blood at 3300 rpm for 10 min, transferred to a new tube with PBS, and subjected to density gradient centrifugation in 50% Percoll (Cytiva) at 3300 rpm for 10 min. Lymphocytes were transferred to a new tube and washed twice with PBS. Lymphocytes were resuspended in 3 mL complete RPMI medium (RPMI 1640 (Gibco), 25mM HEPES (SAFC), 15% FBS (Hyclone), Fungizone , Gentamicin (Gibco), Penicillin/Streptomycin (Lonza), pH 7.2) per tube of blood and transferred to a T25 flask, supplemented with 0.25mL EBV (produced by B95-8 marmoset lymphoblasts), and 0.2 mL of 1 mg/mL cyclosporine (LC Laboratories). They were incubated for one week at 37°C, fed 1-2 mL complete RPMI, and incubated for another week at 37°C. Once the media began to turn yellow (acidified), cultures were “half-fed” by removing half of the media and replacing it with double the volume. When cultures reached 15 mL, they were transferred to T75 flasks, and gradually expanded to 30 mL, while maintaining a concentration of <1 million cells/mL to ensure viability. Cells were viably frozen for future use by mixing with freezing media (LCL culture media + 5% DMSO), 1 million cells per vial. Cells were also preserved for RNA, DNA, and protein extraction (see below).Our protocol for generating primary skin fibroblast cultures from a skin biopsy is based on Vangipuram et al.Plates were incubated for 1 week at 37°C without disturbance to allow biopsies to attach to the plate and begin to grow out. During week 2, we added 200 μL of fresh media per well every 2-3 days, being careful not to disturb the biopsies. The following week (week 3), we aspirated the media and replaced with 1 mL fresh media per well every 2-3 days. During week 4, we aspirated the media and replaced with 2 mL fresh media per well every 2-3 days. At this point, the fibroblasts generally reached the edges of the wells and were expanded to two T75 gelatinized flasks per 6 well plate. After two days, we combined the cells from the two T75 flasks and split them to three T175 gelatinized flasks. After two days, cells were viably frozen with 1 million cells per vial in freezing media (fibroblast culture media + 5% DMSO). Cells were also preserved for RNA extraction (see below). During optimization of the protocol, cell culture purity was confirmed by immunofluorescence of SERPINH1, a fibroblast marker.Cells were collected when LCL cultures reached 30mL, and fibroblasts were ∼80% confluent in three T175 plates. All cell counting was performed using the Countess II cell counter (Life Technologies) and Trypan Blue exclusion. Cultures with >85% cell viability were used in subsequent experiments. To preserve cells for subsequent RNA extraction, 1 million cells were washed in PBS, pelleted, and resuspended in 500 μL TRIzol (Invitrogen) or 200 μL RNAprotect Cell Reagent (Qiagen). Cell suspensions were then frozen at −80°C. Cell cultures were maintained at low passage number; RNA-sequencing experiments were performed on samples at or below passage 4.Periodically, and on each passage used for experiments, cell cultures were confirmed negative for mycoplasma contamination using either the MycoAlert Kit (Lonza) following the manufacturer’s instructions, or PCR using SapphireAmp Fast PCR Master Mix (Takara) and the following primers:Myco2(cb): 5′ CTTCWTCGACTTYCAGACCCAAGGCAT-3′Myco11(cb): 5′ ACACCATGGGAGYTGGTAAT-3′GAPDH was performed on the same sample, using the following primers:PCR for hGAPDH-F: TGT CGC TGT TGA AGT CAG AGG AGAhGAPDH-R: AGA ACA TCA TCC CTG CCT CTA CTG.Known mycoplasma positive and negative samples were used as a reference.RNA was extracted from 1 million cells per experiment using the RNeasy Plus Mini Kit (Qiagen) following the manufacturer’s instructions, with the following modifications: Cells in RNAprotect Cell Reagent were thawed on ice, pelleted, and lysed in buffer RLT supplemented with 10μL β-mercaptoethanol per mL. For most samples, ERCC control RNAs were added to the lysate based on the number of cells: 10μL of 1:100 dilution of ERCC control RNAs was added per 1 million cells. The lysate was then homogenized using QIAshredder columns (Qiagen), and transferred to a gDNA eliminator column. All subsequent optional steps in the protocol were performed, and RNA was eluted in 30 μL RNase-free water. RNA levels were measured using a Qubit fluorometer and the Qubit RNA HS Assay Kit (ThermoFisher). Before we switched to the per-cell spike-in protocol, we prepared 18 samples in which ERCC control RNAs were added based on amount of RNA after isolation: 2 μL of a 1:100 dilution of ERCC control RNAs was added per 1 μg of RNA. These samples are: #2237, 2245, 6312, 711, 4032, 706, 3429, 3430, 3442, 2690, 2703, 3107, 5297, 5566, 5755, 6029, 2547, and 525. RNA quality control was performed using the 5200 Fragment Analyzer System (Agilent); we consistently purified high-quality RNA with RNA integrity numbers (RIN) near 10. We randomized the samples by karyotype into batches for RNA extraction, library preparation, and sequencing.RNA sequencing libraries were prepared using the TruSeq RNA Library Preparation Kit v2 (Illumina) with modifications as detailed in Naqvi et al,and transcripts recognized by the Consensus Coding Sequence project.All analyses were performed using human genome build hg38, and a custom version of the comprehensive GENCODE v24 transcriptome annotation.TXLNGY, PRKY) and well-characterized long non-coding RNAs (lncRNAs) involved in X-inactivation or other processes . We annotated genes distal to XG, which spans the pseudoautosomal boundary on Xp and is truncated on Chr Y, as part of PAR1 - 15 genes in total. PAR2 comprised the four most distal genes on Xq and Yq. Annotations of non-pseudoautosomal region of the X (NPX) genes with homologs on the non-pseudoautosomal region of the Y (NPY) were derived from Bellott et al.https://www.ncbi.nlm.nih.gov/grc/human/issues?filters=chr:21).Reads were pseudoaligned to the transcriptome annotation, and expression levels of each transcript were estimated using kallisto software v0.42.5.We first defined lists of expressed NPX, NPY, PAR, or Chr 21 genes as those with median TPM of at least 1 in 46,XX or 46,XY samples. To ensure that no genes with robust expression were excluded, we also analyzed LCL and fibroblast expression data from GTEx,For each expressed NPX, NPY, or PAR gene we performed linear modeling using the lm function in R. These calculations suppose that each additional chromosome adds a consistent and equal increment to the total expression level of the gene in question.For NPX and PAR genes we used the following equation:E represents the expression (read counts) per gene, For NPY genes we employed the following equation, analyzing only those samples with one or more copies of Chr Y:For this equation, the intercept represents the 46,XY samples.For Chr 21 genes we employed the following equation, analyzing only those samples with 46,XX; 46,XX; 47,XY,+21; or 47,XX,+21 karyotypes:X) or Y (ΔEY), we divided the coefficient of interest  < 0.05 were considered significant. To compute the normalized expression change per Chr Xi  of ΔEBEX1 was removed from downstream analyses in fibroblasts because two samples  had high expression values for this gene resulting in >25 times higher error values for ΔEX and ΔEY compared to all other genes.To confirm the validity of our approach, we used bootstrapping to sample our dataset with replacement 1000 times and obtained similar results. n subsets of available RNA-seq libraries were sampled randomly without replacement, 100 times for each sample size, n. After confirming that the model matrix would be full rank in each sampling , we performed linear modeling on NPX, PAR, NPY, and Chr 21 genes as described above to identify genes whose expression changes significantly (FDR < 0.05) with copy number of Chr X, Y or 21.For LCLs and fibroblasts, size-., by a fixed amount) with additional X or Y chromosomes, their expression levels across the LCL or fibroblast samples were fit by non-linear least squares to the power curves shown below, using the “nlsLM” function from the R package “minpack.lm”.NPX genes:To assess whether sex-chromosome gene expression changed linearly  were much higher than those of other genes. To compare the linear modeling results, we performed Pearson correlations between the results using all samples, and those from male-only or female-only samples.We took subsets of the samples with either zero Y chromosomes  and with one Y chromosome  and performed the same linear modeling and ΔEA previous study performed gene expression analysis, using Illumina oligonucleotide BeadArrays, of LCLs from 68 individuals of the following karyotypes: 45,X; 46,XX; 46,XY; 47,XXX; 47,XXY; 47,XYY; and 48,XXYY.TMSB4X, one of the most highly expressed genes in LCLs, was excluded from the previously reported list of expressed genes.The raw data from the microarrays was not publicly available, but the authors provided us pre-processed data upon request, which we used to perform our analysis. To identify genes that cleared a minimum signal threshold to be considered expressed in the microarray data, we assessed the median signal in 46,XY samples for all Chr Y genes annotated on the microarray. We focused on Chr Y genes in this analysis because many are known to be expressed exclusively in testes, and therefore could provide us with an appropriate sense of the background signal expected for genes not expressed in LCLs. From this analysis, we concluded that a signal threshold of 111 would be appropriate for identifying expressed genes A. We useX values from the microarray data and compared these to our RNA-seq dataset using a Pearson correlation, which revealed that the results were generally concordant  using linear models as a function of Xi copy number, controlling for Chr Y copy number. We calculated ΔEncordant B. For geX calculations for transcripts were performed as for genes (above) to identify transcripts whose abundance changes significantly with additional copies of Chr X.After estimating counts for each transcript using kallisto software , we used sleuth v0.30.0 to normalize those transcript counts.UBA1 locus.The following ENCODE datasets were used for visualization in IGV softwarehttps://gnomad.broadinstitute.org/), and only used scores with a minimum of 10 expected LoF variants. Updated RVIS http://genic-intolerance.org/data/RVIS_Unpublished_ExAC_May2015.txt. Updated probability of haploinsufficiency (pHI) scoreshttps://www.deciphergenomics.org/files/downloads/HI_Predictions_Version3.bed.gz. To complement these data, we obtained a list of genes with observed homozygous loss-of-function variants.CT).To investigate sensitivity to a reduction in gene dosage, we used three metrics: LOEUF, RVIS, and pHI. We downloaded LOEUF (loss-of-function observed/expected upper fraction) scores from gnomAD  and NPX genes, we ranked autosomal (and PAR) genes separately from NPX genes. All annotated genes, regardless of expression status in LCLs or fibroblasts, were included in the rankings, with the following exceptions: 1) NPX genes previously annotated as “ampliconic”,X ≥ 0.1| (FDR < 0.05) to be most likely to contribute to phenotypes mediated by Xi copy number, prioritizing the top ten genes by the average gene-constraint metric. For PAR1 genes, we prioritized genes with an average gene constraint percentile ranking of at least 50%. To assess the phenotypic roles of highly constrained genes, we annotated them for disease phenotypes with known molecular basis from Online Mendelian Inheritance in Man (OMIM).To obtain an aggregate sense of a gene’s expression constraint across multiple metrics, we calculated the average. Among NPX genes, we considered those with |ΔE,,,We re-compiled XCI status annotations of individual genes from four studies of Chr X allelic ratios.The first dataset that we incorporated was derived from paired genomic and cDNA SNP-chips in skewed LCL and fibroblast cell cultures .The second dataset that we incorporated was derived from bulk or single cell RNA-seq of LCLs.The third dataset that we incorporated was derived from single-cell allelic expression in fibroblasts.The fourth dataset that we incorporated was derived from allele-specific bulk RNA-seq performed on 136 samples with skewed XCI from the set of GEUVADIS LCLs ..Next, we synthesized the calls from these four datasets. We assigned a gene as “Subject” if: 1) all studies were “Subject” or 2) most (>50%) studies were “Subject” and the average AR across all studies was <0.1. We assigned “Escape” if 1) most (>50%) studies were “Escape” or 2) 50% or fewer (but more than 0) studies were “Escape” and either i) there was more than one study with evidence of escape or ii) the average AR across all studies was ≥1. Finally, we assigned “No call” if the gene was not informative in any of the four datasets. For these genes, we investigated whether there were any calls using hybrids from Carrel et al.XG had been listed as a PAR gene, but we excluded it from our list of PAR genes because it is located at the PAR boundary and truncated on the Y chromosome. We updated its annotation to escape (“E”) since the Balaton table lists evidence for escape. The Balaton table lists XIST as “mostly subject” to XCI, but given its exclusive expression from Xi, we updated its status to escape (“E”). We manually examined all genes on our list that were not found in the Balaton list to make sure that genes were not misclassified due to differences in official gene names. For those genes still not present in the Balaton list after this correction, we list “No call”. To compare with our annotations, we grouped the Balaton calls into “Escape” if they were annotated as “PAR”, “Escape”, “Mostly escape”, “Variable Escape”, “Mostly Variable Escape”, or “Discordant”. We grouped Balaton calls into “Subject” if they were annotated as “Mostly subject” or “Subject”.To compare our calls with previous XCI consensus calls, we made the following modifications to the Balaton list: TCEAL3, TMSB4X), there was no call in Balaton, but newer data enabled a call to be made (both “Subject”). Nineteen genes were called “Subject” in Balaton, but new data indicates that they have expression from Xi and we categorize them as “Escape.” The final eighteen genes were called “Escape” in Balaton, but new data suggested they have no expression from Xi. In total, our classification found 86 genes that “Escape”, 315 genes that are “Subject” to XCI, and 22 genes with “No call.”We compared our new calls with the Balaton calls for the 423 genes expressed in fibroblasts or LCLs, finding 48 where they differed. Of these, nine had a call in Balaton, but no call in the newer datasets. For two genes (This workflow is diagrammed in https://gatk.broadinstitute.org/hc/en-us/articles/360035531192-RNAseq-short-variant-discovery-SNPs-Indels-). To perform our skewing analysis, we filtered for SNPs with the following properties: 1) annotated in the dbSNP database, 2) located in an exon of an expressed gene, 3) displaying a minimum coverage of 10 reads, and 4) heterozygous with at least three reads mapping to each of the reference and alternative alleles. We excluded SNPs where the presence of two alleles likely represented technical artifacts rather than biallelic expression, including in WASH6P , ATRX (SNP in a mutation-prone stretch of Ts), and APOOL (SNPs within an inverted repeat). For samples with a copy of Chr Y, we excluded SNPs mapping to PAR genes, to avoid measuring allelic contributions of Chr Y.We called SNPs in each RNA-seq sample with two X chromosomes  following the Broad Institute’s “Best Practices” workflow for identifying short variants in RNA-seq data (and found here to have ΔEX < 0.05 (FDR > 0.5); see −6), and we do not find evidence of such skewing for SNPs on Chr 8, an autosome with a similar number of expressed genes (We classified genes as “Xa only” (only expressed from Xa) if previously characterized as “silenced” ed genes .Several samples had few (≤5) informative SNPs in Xa-only genes, but many SNPs in other genes . We inteAfter identifying the skewed cell lines, we identified genes with informative SNPs values in at least three skewed samples of a given cell type. We then computed the allelic ratio (AR) at each informative SNP by dividing the number of reads from the more lowly expressed allele by the number of reads from the more highly expressed allele. In cell cultures that are partially skewed, genes will appear more biallelic than in completely skewed cell cultures since there are two populations of cells with different active X chromosomes present – the “major” and “minor” cell populations. Using our skewing estimates, we adjusted the AR on a per-sample basis using the following formula:’s exons  and then’s exons .To assess whether AR values for each gene were significantly greater than zero, we performed one-sided t tests using the t.test function in R, asking whether the AR values were greater than zero; we corrected the resulting p values for multiple comparisons with the p.adjust function in R using the Benjamini-Hochberg method . We alsoX values differ significantly, we performed one-sample, two-sided t tests for the AR values across samples, setting mu = ΔEX  included the mean AR value, and those for which both ΔEX and AR were not significantly different from zero (FDR ≥ 0.05).To identify genes whose AR and ΔEmu = ΔEX . We seleWe compared our AR values derived from LCLs or fibroblasts with the four published allelic-ratio datasets described in the above methods on generating XCI status calls .Various statistical tests were used to calculate p values as indicated in the methods section, figure legend, or text, where appropriate. Results were considered statistically significant when p < 0.05 or FDR<0.05 when multiple hypothesis correction was applied, unless stated otherwise. Data are shown as median and interquartile range, unless stated otherwise. All statistics were calculated using R software, version 3.6.3.