Bias and mean squared error in Mendelian randomization with invalid instrumental variables.

Mendelian randomization (MR) is a statistical method that utilizes genetic variants as instrumental variables (IVs) to investigate causal relationships between risk factors and outcomes. Although MR has gained popularity in recent years due to its ability to analyze summary statistics from genome-wide association studies (GWAS), it requires a substantial number of single nucleotide polymorphisms (SNPs) as IVs to ensure sufficient power for detecting causal effects. Unfortunately, the complex genetic heritability of many traits can lead to the use of invalid IVs that affect both the risk factor and the outcome directly or through an unobserved confounder. This can result in biased and imprecise estimates, as reflected by a larger mean squared error (MSE). In this study, we focus on the widely used two-stage least squares (2SLS) method and derive formulas for its bias and MSE when estimating causal effects using invalid IVs. Using those formulas, we identify conditions under which the 2SLS estimate is unbiased and reveal how the independent or correlated pleiotropic effects influence the accuracy and precision of the 2SLS estimate. We validate these formulas through extensive simulation studies and demonstrate the application of those formulas in an MR study to evaluate the causal effect of the waist-to-hip ratio on various sleeping patterns. Our results can aid in designing future MR studies and serve as benchmarks for assessing more sophisticated MR methods.

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.

What do women undergoing in vitro fertilization (IVF) understand about their chance of IVF success?

STUDY QUESTION: How well informed are Australian women who undergo IVF about their chances of having a baby? SUMMARY ANSWER: Only one in four women estimated their individual chance of success with IVF accurately, with most women overestimating their chance. WHAT IS KNOWN ALREADY: Limited knowledge about infertility and infertility treatment in the general population is well-documented. The few studies that have investigated patients' knowledge about the chance of IVF success suggest that while IVF patients are aware of average success rates, they tend to be unrealistic about their own chance of success. STUDY DESIGN, SIZE, DURATION: We conducted an anonymous online survey of 217 women who had started IVF since 2018 in Australia. The survey was advertised on social media, enabling women from across Australia to participate. Responses were collected in June 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: The survey included questions on demographic characteristics and IVF history. It asked what participants thought their chance of having a baby from one IVF treatment cycle was, how they rated their knowledge about chance of success, and about their experience of receiving IVF-related information. Participants' estimations of their chance of success were compared with their chance as calculated by the Society for Assisted Reproductive Technology's (SART) online calculator. Responses to a free-text question about what information women wished they had been given when they started treatment were analysed thematically. MAIN RESULTS AND THE ROLE OF CHANCE: Only about a quarter (58/217, 27%) of participants accurately estimated their chance of having a baby within 20% relative to their SART calculated chance, with more than half (118/217, 54%) overestimating their chance. Ninety percent of women indicated that their preferred source of treatment information was a consultation with their doctor, despite less than half (44%) reporting that doctors explained the probability of having a baby with IVF well (mean 5.9/10). In free-text responses, many women also reported that they wished they had been given more realistic information about IVF and their chance of success. LIMITATIONS, REASONS FOR CAUTION: The dissemination method precludes calculation of response rate, and it is not possible to know if participants are representative of all women undergoing IVF. Additionally, we only surveyed women undergoing IVF, while those who decided not to have IVF were not included. Therefore, women who overestimated their chance may have been overrepresented. There is also inherent imprecision in the way understanding of chance of success was estimated. The potential impact of recall bias could neither be quantified nor excluded. It is difficult to determine to what extent women's lack of understanding of what is possible with IVF is due to poor information-provision by clinicians and the clinic, and how much can be explained by optimism bias. WIDER IMPLICATIONS OF THE FINDINGS: The finding of poor understanding of personal chance of success amongst women undergoing IVF in Australia requires further investigation to determine potential reasons for this. The findings can be used by clinics to develop strategies for improvement in the information-provision process to ensure that women can make informed decisions about their fertility treatment. STUDY FUNDING/COMPETING INTEREST(S): This study received no external funding. S.L. is supported by a NHMRC Investigator Grant (APP1195189). R.W. is supported by a NHMRC Investigator Grant (APP2009767). B.W.M. is supported by a NHMRC Investigator Grant (GNT1176437). B.W.M. reports consultancy for Merck and ObsEva and has received research funding and travel funding from Merck. The other authors have no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Neuromuscular consequences of spinal cord injury: New mechanistic insights and clinical considerations.

The spinal cord facilitates communication between the brain and the body, containing intrinsic systems that work with lower motor neurons (LMNs) to manage movement. Spinal cord injuries (SCIs) can lead to partial paralysis and dysfunctions in muscles below the injury. While traditionally this paralysis has been attributed to disruptions in the corticospinal tract, a growing body of work demonstrates LMN damage is a factor. Motor units, comprising the LMN and the muscle fibers with which they connect, are essential for voluntary movement. Our understanding of their changes post-SCI is still emerging, but the health of motor units is vital, especially when considering innovative SCI treatments like nerve transfer surgery. This review seeks to collate current literature on how SCI impact motor units and explore neuromuscular clinical implications and treatment avenues. SCI reduced motor unit number estimates, and surviving motor units had impaired signal transmission at the neuromuscular junction, force-generating capacity, and excitability, which have the potential to recover chronically, yet the underlaying mechanisms are unclear. Furthermore, electrodiagnostic evaluations can aid in assessing the health lower and upper motor neurons, identify suitable targets for nerve transfer surgeries, and detect patients with time sensitive injuries. Lastly, many electrodiagnostic abnormalities occur in both chronic and acute SCI, yet factors contributing to these abnormalities are unknown. Future studies are required to determine how motor units adapt following SCI and the clinical implications of these adaptations.

Scaled average bioequivalence methods for highly variable drugs: Leveling-off soft limits and the EMA's 2010 guideline (some ways to improve its type I error control).

The regulatory EMA's reference scaled average bioequivalence (RSABE) approach for highly variable drugs suffers from some type I error control problems at the neighborhood of the 30% coefficient of variation (CV), where the bioequivalence (BE) limits change from constant to linearly scaled. This paper analyses BE inference methods based on the "Leveling-off" (LO) soft sigmoid expanding BE limits that were proposed as an appealing surrogate for the EMA's limits and compares both approaches, on the replicated and partially replicated crossover designs. The initially proposed version of the LO method also has type I error inflation problems, albeit attenuated. But given its more mathematically regular character, it is more suitable for analytical corrections. Here we introduce two improvements over LO, one based on the application of Howe's method and the other based on correcting the estimation error. They further reduce the type I error inflation, although it does not disappear completely. Finally, the effect of heteroscedasticity on the above results is studied. It leads to inflation or deflation of the type I error, depending on the design and the type of heteroscedasticity (variability of the test product greater than that of the reference product or the opposite). The replicated design is much more stable against these effects than the partially replicated design and maintains these improvements much better.

A comparison of Bayesian and score methods for interval estimates of positive/negative likelihood ratios in support of diagnostic device performance evaluation.

BACKGROUND: Positive and negative likelihood ratios (PLR and NLR) are important metrics of accuracy for diagnostic devices with a binary output. However, the properties of Bayesian and frequentist interval estimators of PLR/NLR have not been extensively studied and compared. In this study, we explore the potential use of the Bayesian method for interval estimation of PLR/NLR, and, more broadly, for interval estimation of the ratio of two independent proportions. METHODS: We develop a Bayesian-based approach for interval estimation of PLR/NLR for use as a part of a diagnostic device performance evaluation. Our approach is applicable to a broader setting for interval estimation of any ratio of two independent proportions. We compare score and Bayesian interval estimators for the ratio of two proportions in terms of the coverage probability (CP) and expected interval width (EW) via extensive experiments and applications to two case studies. A supplementary experiment was also conducted to assess the performance of the proposed exact Bayesian method under different priors. RESULTS: Our experimental results show that the overall mean CP for Bayesian interval estimation is consistent with that for the score method (0.950 vs. 0.952), and the overall mean EW for Bayesian is shorter than that for score method (15.929 vs. 19.724). Application to two case studies showed that the intervals estimated using the Bayesian and frequentist approaches are very similar. DISCUSSION: Our numerical results indicate that the proposed Bayesian approach has a comparable CP performance with the score method while yielding higher precision (i.e. a shorter EW).

Estimating the Real Impact of the COVID-19 Pandemic in Kazakhstan: Factors Associated with Detection of the "True Infections".

The COVID-19 pandemic is ongoing worldwide, and various case and death numbers are being reported to track its spread. However, the number of actual cases is uncertain due to under-reporting. Using mortality data as a more reliable indicator, this study in Kazakhstan evaluated the extent of under-reporting and under-detection of COVID-19 cases from March 2020 to September 2022 using back-casting and capture-recapture methods. The results indicate that official case reporting in Kazakhstan significantly underestimates the number of infections by at least 50%. The study also suggests that improved testing capabilities may have led to a decrease in the percentage of unreported cases, however, early in the pandemic, Kazakhstan faced significant testing shortages. The study presents a mathematical model based on mortality data that highlights the severe under-reporting of COVID-19 cases in Kazakhstan and argues that understanding the true estimate of actual cases could aid in making informed decisions to end the pandemic.

Canine sexual dimorphism in Ardipithecus ramidus was nearly human-like.

Body and canine size dimorphism in fossils inform sociobehavioral hypotheses on human evolution and have been of interest since Darwin's famous reflections on the subject. Here, we assemble a large dataset of fossil canines of the human clade, including all available Ardipithecus ramidus fossils recovered from the Middle Awash and Gona research areas in Ethiopia, and systematically examine canine dimorphism through evolutionary time. In particular, we apply a Bayesian probabilistic method that reduces bias when estimating weak and moderate levels of dimorphism. Our results show that Ar. ramidus canine dimorphism was significantly weaker than in the bonobo, the least dimorphic and behaviorally least aggressive among extant great apes. Average male-to-female size ratios of the canine in Ar. ramidus are estimated as 1.06 and 1.13 in the upper and lower canines, respectively, within modern human population ranges of variation. The slightly greater magnitude of canine size dimorphism in the lower than in the upper canines of Ar. ramidus appears to be shared with early Australopithecus, suggesting that male canine reduction was initially more advanced in the behaviorally important upper canine. The available fossil evidence suggests a drastic size reduction of the male canine prior to Ar. ramidus and the earliest known members of the human clade, with little change in canine dimorphism levels thereafter. This evolutionary pattern indicates a profound behavioral shift associated with comparatively weak levels of male aggression early in human evolution, a pattern that was subsequently shared by Australopithecus and Homo.

Estimating sexual size dimorphism in fossil species from posterior probability densities.

Accurate characterization of sexual dimorphism is crucial in evolutionary biology because of its significance in understanding present and past adaptations involving reproductive and resource use strategies of species. However, inferring dimorphism in fossil assemblages is difficult, particularly with relatively low dimorphism. Commonly used methods of estimating dimorphism levels in fossils include the mean method, the binomial dimorphism index, and the coefficient of variation method. These methods have been reported to overestimate low levels of dimorphism, which is problematic when investigating issues such as canine size dimorphism in primates and its relation to reproductive strategies. Here, we introduce the posterior density peak (pdPeak) method that utilizes the Bayesian inference to provide posterior probability densities of dimorphism levels and within-sex variance. The highest posterior density point is termed the pdPeak. We investigated performance of the pdPeak method and made comparisons with the above-mentioned conventional methods via 1) computer-generated samples simulating a range of conditions and 2) application to canine crown-diameter datasets of extant known-sex anthropoids. Results showed that the pdPeak method is capable of unbiased estimates in a broader range of dimorphism levels than the other methods and uniquely provides reliable interval estimates. Although attention is required to its underestimation tendency when some of the distributional assumptions are violated, we demonstrate that the pdPeak method enables a more accurate dimorphism estimate at lower dimorphism levels than previously possible, which is important to illuminating human evolution.

Automated size-specific dose estimates framework in thoracic CT using convolutional neural network based on U-Net model.

PURPOSE: This study aimed to develop an automated method that uses a convolutional neural network (CNN) for calculating size-specific dose estimates (SSDEs) based on the corrected effective diameter (Deff corr ) in thoracic computed tomography (CT). METHODS: Transaxial images obtained from 108 adult patients who underwent non-contrast thoracic CT scans were analyzed. To calculate the Deff corr according to Mihailidis et al., the average relative electron densities for lung, bone, and other tissues were used to correct the lateral and anterior-posterior dimensions. The CNN architecture based on the U-Net algorithm was used for automated segmentation of three classes of tissues and the background region to calculate dimensions and Deff corr values. Then, 108 thoracic CT images and generated segmentation masks were used for network training. The water-equivalent diameter (Dw ) was determined according to the American Association of Physicists in Medicine Task Group 220. Linear regression and Bland-Altman analysis were performed to determine the correlations between SSDEDeff corr(automated) , SSDEDeff corr(manual) , and SSDEDw . RESULTS: High agreement was obtained between the manual and automated methods for calculating the Deff corr SSDE. The mean values for the SSDEDeff corr(manual) , SSDEDw , and SSDEDeff corr(automated) were 14.3 ± 2.1 mGy, 14.6 ± 2.2 mGy, and 14.5 ± 2.4 mGy, respectively. The U-Net model was successfully trained and used to accurately predict SSDEs, with results comparable to manual-labeling results. CONCLUSION: The proposed automated framework using a CNN offers a reliable and efficient solution for determining the Deff corr SSDE in thoracic CT.

Monocular Depth Estimation via Self-Supervised Self-Distillation.

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

One fish, two fish, three fish, more: novel resighting method produces precise and cost-effective estimates of abundance.

Estimates of abundance are essential to manage and conserve marine species. Numerous methods are available to determine population size, but the suitability of methods for schooling fishes and the associated precision can vary depending on the species and system. Here, we developed and compared three mark-recapture/resight methods to assess the most robust method to estimate the abundance of silver trevally (Pseudocaranx georgianus). While the recapture rate was similar across the methods, the swim pass method (resighting) recorded the largest number of individuals (mean ± standard error 211 ± 14.9) and had the lowest coefficient of variation (CV; 4.5%-12%) compared to 360-video (resighting, 45 ± 2.1 individuals surveyed, 14.8%-22.2% CV) and large-scale capture methods (recapture, 30 ± 3.8 individuals surveyed, 17.3%-26.5% CV). The inclusion of individual identification into the abundance estimator models for large-scale capture did not change the abundance estimates and showed similar resolution between the models (CV 18.2%-26.7%). We showed that the swim pass method is logistically easy to implement and generates precise estimates of silver trevally abundance. This new method provides a low-cost, time-efficient resighting method that can be adapted to suit similar aggregating pelagic species interacting with wildlife tourism operations, enabling researchers to rapidly estimate the abundance of species that have been previously difficult to count.

Urban flood-bearing vulnerability evaluation based on the moment estimate weighting and improved gray target model.

Increasingly severe flooding seriously threatens urban safety. A scientific urban flood-bearing vulnerability assessment model is significant to improve urban risk management capacity. The gray target model (GTM) has advantages in urban flood-bearing vulnerability assessment. However, indicator correlation and single bull's-eye are commonly neglected, leading to defective evaluation results. By integrating the four base weights, an improved weighting method based on the moment estimate was proposed. Then, the marginal distance was used to quantify the indicator correlation, and the TOPSIS model was introduced to define the relative bull's-eye distance. Thus, an improved gray target evaluation method was established. Finally, an urban flood-bearing vulnerability evaluation model was presented based on the moment estimate weighting-improved GTM. In this study, Zhengzhou City, China, was taken as an example. The spatial and temporal changing characteristics of the flood-bearing vulnerability of Zhengzhou from 2006 to 2020 were investigated. The results show that: (1) On the temporal scale, the disaster-bearing vulnerability of Zhengzhou City showed an upward trend during the 15 years; (2) On the spatial scale, Guancheng District of Zhengzhou City had the relatively highest vulnerability to urban flooding. This study is expected to provide a scientific reference for urban flood risk management.

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models.

Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis-Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method.

Alcoholic Liver Disease/Nonalcoholic Fatty Liver Disease Index at Diagnosis Is Associated with All-Cause Mortality during Follow-Up in Patients with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis.

Background and Objectives: The purpose of this study was to investigate whether a new index related to chronic liver disease, the alcoholic liver disease/nonalcoholic fatty liver disease index (ANI) at diagnosis, is associated with all-cause mortality during follow-up in patients with antineutrophil cytoplasmic antibody-associated vasculitis (AAV). Materials and Methods: In this study, we included 270 patients with AAV. ANI was calculated using the following equation: ANI = -58.5 + 0.637 (adjusted mean corpuscular volume) + 3.91 (adjusted aspartate transaminase/alanine transaminase) - 0.406 (body mass index) + 6.35 (if male sex). All-cause mortality was defined as death from any cause during follow-up. Results: The median age of the 270 patients with AAV was 61.0 years (34.4% male and 66.6% female). The median ANI was significantly higher in deceased patients than in surviving patients. In the receiver operating characteristic curve analysis, ANI at diagnosis exhibited a statistically significant area under the curve for all-cause mortality during follow-up, and its cut-off was determined to be -0.59. Patients with ANI at diagnosis ≥ -0.59 exhibited a significantly higher risk for all-cause mortality and a significantly lower cumulative patient survival rate than those without. In the multivariable Cox analysis, ANI at diagnosis ≥ -0.59, together with age at diagnosis, was independently associated with all-cause mortality. Conclusions: This study is the first to demonstrate the predictive potential of ANI at diagnosis for all-cause mortality during follow-up in AAV patients without significant chronic liver diseases.

peaksat: an R package for ChIP-seq peak saturation analysis.

BACKGROUND: Epigenomic profiling assays such as ChIP-seq have been widely used to map the genome-wide enrichment profiles of chromatin-associated proteins and posttranslational histone modifications. Sequencing depth is a key parameter in experimental design and quality control. However, due to variable sequencing depth requirements across experimental conditions, it can be challenging to determine optimal sequencing depth, particularly for projects involving multiple targets or cell types. RESULTS: We developed the peaksat R package to provide target read depth estimates for epigenomic experiments based on the analysis of peak saturation curves. We applied peaksat to establish the distinctive read depth requirements for ChIP-seq studies of histone modifications in different cell lines. Using peaksat, we were able to estimate the target read depth required per library to obtain high-quality peak calls for downstream analysis. In addition, peaksat was applied to other sequence-enrichment methods including CUT&RUN and ATAC-seq. CONCLUSION: peaksat addresses a need for researchers to make informed decisions about whether their sequencing data has been generated to an adequate depth and subsequently sufficient meaningful peaks, and failing that, how many more reads would be required per library. peaksat is applicable to other sequence-based methods that include calling peaks in their analysis.

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation.

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses.

CD1C is associated with breast cancer prognosis and immune infiltrates.

BACKGROUND: The tumor microenvironment (TME) in breast cancer plays a vital role in occurrence, development, and therapeutic responses. However, immune and stroma constituents in the TME are major obstacles to understanding and treating breast cancer. We evaluated the significance of TME-related genes in breast cancer. METHODS: Invasive breast cancer (BRCA) samples were retrieved from the TCGA and GEO databases. Stroma and immune scores of samples as well as the proportion of tumor infiltrating immune cells (TICs) were calculated using the ESTIMATE and CIBERSORT algorithms. TME-related differentially expressed genes (DEGs) were analyzed by a protein interaction (PPI) network and univariate Cox regression to determine CD1C as a hub gene. Subsequently, the prognostic value of CD1C, its response to immunotherapy, and its mechanism in the TME were further studied. RESULTS: In BRCA, DEGs were determined to identify CD1C as a hub gene. The expression level of CD1C in BRCA patients was verified based on the TCGA database, polymerase chain reaction (PCR) results, and western blot analysis. Immunohistochemical staining (IHC) results revealed a correlation between prognosis, clinical features, and CD1C expression in BRCA. Enrichment analysis of GSEA and GSVA showed that CD1C participates in immune-associated signaling pathways. CIBERSORT showed that CD1C levels were associated with tumor immune infiltrating cells (TILs), such as different kinds of T cells. Gene co-expression analysis showed that CD1C and the majority of immune-associated genes were co-expressed in BRCA. In renal cell carcinoma, patients with a high expression of CD1C had a better immunotherapy effect. CONCLUSION: CD1C is an important part of the TME and participates in immune activity regulation in breast tumors. CD1C is expected to become a prognostic marker and a new treatment target for breast cancer.

Update of the keratin gene family: evolution, tissue-specific expression patterns, and relevance to clinical disorders.

Intermediate filament (IntFil) genes arose during early metazoan evolution, to provide mechanical support for plasma membranes contacting/interacting with other cells and the extracellular matrix. Keratin genes comprise the largest subset of IntFil genes. Whereas the first keratin gene appeared in sponge, and three genes in arthropods, more rapid increases in keratin genes occurred in lungfish and amphibian genomes, concomitant with land animal-sea animal divergence (~ 440 to 410 million years ago). Human, mouse and zebrafish genomes contain 18, 17 and 24 non-keratin IntFil genes, respectively. Human has 27 of 28 type I "acidic" keratin genes clustered at chromosome (Chr) 17q21.2, and all 26 type II "basic" keratin genes clustered at Chr 12q13.13. Mouse has 27 of 28 type I keratin genes clustered on Chr 11, and all 26 type II clustered on Chr 15. Zebrafish has 18 type I keratin genes scattered on five chromosomes, and 3 type II keratin genes on two chromosomes. Types I and II keratin clusters-reflecting evolutionary blooms of keratin genes along one chromosomal segment-are found in all land animal genomes examined, but not fishes; such rapid gene expansions likely reflect sudden requirements for many novel paralogous proteins having divergent functions to enhance species survival following sea-to-land transition. Using data from the Genotype-Tissue Expression (GTEx) project, tissue-specific keratin expression throughout the human body was reconstructed. Clustering of gene expression patterns revealed similarities in tissue-specific expression patterns for previously described "keratin pairs" (i.e., KRT1/KRT10, KRT8/KRT18, KRT5/KRT14, KRT6/KRT16 and KRT6/KRT17 proteins). The ClinVar database currently lists 26 human disease-causing variants within the various domains of keratin proteins.