polyGBLUP: a modified genomic best linear unbiased prediction improved the genomic prediction efficiency for autopolyploid species.

Given the universality of autopolyploid species in nature, it is crucial to develop genomic selection methods that consider different allele dosages for autopolyploid breeding. However, no method has been developed to deal with autopolyploid data regardless of the ploidy level. In this study, we developed a modified genomic best linear unbiased prediction (GBLUP) model (polyGBLUP) through constructing additive and dominant genomic relationship matrices based on different allele dosages. polyGBLUP could carry out genomic prediction for autopolyploid species regardless of the ploidy level. Through comprehensive simulations and analysis of real data of autotetraploid blueberry and guinea grass and autohexaploid sweet potato, the results showed that polyGBLUP achieved higher prediction accuracy than GBLUP and its superiority was more obvious when the ploidy level of autopolyploids is high. Furthermore, when the dominant effect was added to polyGBLUP (polyGDBLUP), the greater the dominance degree, the more obvious the advantages of polyGDBLUP over the diploid models in terms of prediction accuracy, bias, mean squared error and mean absolute error. For real data, the superiority of polyGBLUP over GBLUP appeared in blueberry and sweet potato populations and a part of the traits in guinea grass population due to the high correlation coefficients between diploid and polyploidy genomic relationship matrices. In addition, polyGDBLUP did not produce higher prediction accuracy than polyGBLUP for most traits of real data as dominant genetic variance was not captured for these traits. Our study will be a significant promising method for genomic prediction of autopolyploid species.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing.

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

Role of membrane fouling layer in microbial fuel cell-membrane bioreactor (MFC-MBR) for controlling sulfamethoxazole and corresponding resistance genes.

Integrated MFC-MBR systems effectively remove antibiotics and control the release of antibiotic resistance genes (ARGs). However, the fouling layers on membranes can potentially act as reservoirs for ARGs. This study aims to elucidate the roles of membrane fouling layers and levels in influencing sulfamethoxazole (SMX) removal and ARGs control within an MFC-MBR system. Our findings demonstrate that low-intensity bioelectricity (400-500 mV) mitigates membrane fouling rates. The membrane fouling layer significantly contributes (39%-47%) to SMX removal compared to the cathode/anode zones. Higher extracellular polymeric substance (EPS) content and a lower protein/polysaccharide (PN/PS) ratio favor SMX removal by the membrane fouling layer. Across different levels of membrane fouling, the PN/PS ratio rather than EPS concentration plays a crucial role in SMX removal efficiency. The MFC-MBR with low fouling achieved superior SMX removal (69.1%) compared to medium (54.3%) and high fouling conditions (46.8%). The presence of ARGs in the membrane fouling layer increases with fouling formation, with intrinsic ARGs prevailing. Dense membrane fouling layers effectively retain ARGs, thereby reducing the risk of extracellular ARGs (eARGs) diffusion in effluents. These results provide insights into controlling ARGs in MFC-MBR systems and underscore the significant role of membrane fouling layers in antibiotics and ARGs removal.

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands.

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

Application in Gene Editing in Ovarian Cancer Therapy.

The onset and progression of ovarian cancer (OC) are closely related to dysregulated gene expression. Current treatments for OC are mainly limited to surgery and chemotherapy. However, due to low drug sensitivity, the prognosis OC is exceptionally poor and the recurrence rate remains high. Hence, it is vital to develop new treatment strategies. Gene editing for site-specific genomic modification is a powerful novel tool for the treatment of OC. In this article, current gene editing research for the treatment of OC is reviewed to provide a reference for the clinical application of new approaches to improve treatment outcomes and prognosis.

Quinones contained in wastewater as redox mediators for the synergistic removal of azo dye in microbial fuel cells.

The present paper aimed to investigate the roles of quinones contained in wastewater and the enhanced effects on microbial fuel cells (MFCs) under different redox conditions. The feasibility of using wastewater rich in quinones to act as co-substrate and redox mediators (RMs) library to strengthen the synergistic removal of azo dye in MFCs was evaluated. The results demonstrated that quinones achieved enhanced effects on electricity generation and COD removal of MFC better at higher current intensity. The addition of pure quinone decreased electron transfer resistance (Rct) of MFCs from 4.76 Ω to 2.13 Ω under 1000 Ω resistance and 1.16 Ω-0.75 Ω under 50 Ω resistance. Meanwhile, higher coulombic efficiency was achieved. Compared with sodium acetate, using quinone-rich traditional Chinese medicine (TCM) wastewater as the co-substrate enhanced the synergistic removal of reactive red 2 (RR2) in MFCs from 79.58% to 92.45% during 24 h. RR2 was also degraded more thoroughly due to the accelerated electron transfer process mediated by RMs. Microbial community analysis demonstrated that the presence of quinone in TCM wastewater can enrich different exoelectrogens under varied redox conditions and thus influenced the enhanced effects on MFC. Metagenomic functional prediction results further indicated that the abundance of functional genes involved in carbohydrate metabolism, membrane transport metabolism, biofilm formation, and stress tolerance increased significantly in presence of RMs. Redundancy analyses revealed that RMs addition was the more important factor driving the variation of the microorganism community. This study revealed the potential effect of quinones as redox mediators on the bioelectrochemical system for pollutants removal.

The theory on and software simulating large-scale genomic data for genotype-by-environment interactions.

BACKGROUND: With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. RESULTS: We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. CONCLUSIONS: This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

A mutation in MAP2 is associated with prenatal hair follicle density.

Hairlessness is usually a rare trait in pigs; however, in this study, we found hairless (HR) pigs at a relatively high frequency in 1 pig herd. We observed that, the lower hair shaft density of HR pigs could be mainly attributed to the lower hair follicle density, and during the embryonic period, d 39-45 were a critical stage for the formation of the hair follicle. In this regard, d 41 during gestation was a particularly important point. Hair follicle morphogenesis occurring at an early stage of embryo development is similar to humans and mice. Further analyses of association studies based on single-nucleotide polymorphism chip as well as sequence data, mRNA sequencing, immunohistochemistry, and comparative genomics demonstrated that microtubule-associated protein 2 (MAP2) is a key gene responsible for hair follicle density and 1 missense mutation of A-to-G at rs328005415 in MAP2, causing a valine-to-methionine substitution leads to the HR phenotype. Considering the high homology between pigs and humans, our research has some significance for the study of the mechanisms of skin development, hair morphogenesis, and hair loss in humans by showing that the pig may be a more appropriate model in which to study these processes.-Jiang, Y., Jiang, Y., Zhang, H., Mei, M., Song, H., Ma, X., Jiang, L., Yu, Z., Zhang, Q., Ding, X. A mutation in MAP2 is associated with prenatal hair follicle density.

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single-step genomic reaction norm model.

Economically important traits are usually complex traits influenced by genes, environment and genotype-by-environment (G × E) interactions. Ignoring G × E interaction could lead to bias in the estimation of breeding values and selection decisions. A total of 1,778 pigs were genotyped using the PorcineSNP80 BeadChip. The existence of G × E interactions was investigated using a single-step reaction norm model for growth traits of days to 100 kg (AGE) and backfat thickness adjusted to 100 kg (BFT), based on a pedigree-based relationship matrix (A) or a genomic-pedigree joint relationship matrix (H). In the reaction norm model, the herd-year-season effect was measured as the environmental variable (EV). Our results showed no G × E interactions for AGE, but for BFT. For both AGE and BFT, the genomic reaction norm model (H) produced more accurate predictions than the conventional reaction norm model (A). For BFT, the accuracies were greater based on the reaction norm model than those based on the reduced model without exploiting G × E interaction, with EV ranging from 0.5 to 1, and accuracy increasing by 3.9% and 4.6% in the reaction norm model based on A and H matrices, respectively, while reaction norm model yielded approximately 8.4% and 7.9% lower accuracy for EVs ranging from 0 to 0.4, based on A and H matrices, respectively. In addition, for BFT, the highest accuracy was obtained in the BJLM6 farm for realizing directional selection. This study will help to apply G × E interactions to practical genomic selection.

Constructed Wetland Revealed Efficient Sulfamethoxazole Removal but Enhanced the Spread of Antibiotic Resistance Genes.

Constructed wetlands (CWs) could achieve high removal efficiency of antibiotics, but probably stimulate the spread of antibiotic resistance genes (ARGs). In this study, four CWs were established to treat synthetic wastewater containing sulfamethoxazole (SMX). SMX elimination efficiencies, SMX degradation mechanisms, dynamic fates of ARGs, and bacterial communities were evaluated during the treatment period (360 day). Throughout the whole study, the concentration of SMX in the effluent gradually increased (p < 0.05), but in general, the removal efficiency of SMX remained at a very high level (>98%). In addition, the concentration of SMX in the bottom layer was higher compared with that in the surface layer. The main byproducts of SMX degradation were found to be 4-amino benzene sulfinic acid, 3-amino-5-methylisoxazole, benzenethiol, and 3-hydroxybutan-1-aminium. Temporally speaking, an obvious increase of sul genes was observed, along with the increase of SMX concentration in the bottom and middle layers of CWs. Spatially speaking, the concentration of sul genes increased from the surface layer to the bottom layer.

Bioelectrochemically-assisted nitrogen removal in osmotic membrane bioreactor.

Nitrogen removal in osmosis membrane bioreactor (OMBR) is important to its applications but remains a challenge. In this study, a bioelectrochemically-assisted (BEA) operation was integrated into the feed side of OMBRs to enhance nitrogen removal, and sodium acetate was served as a draw solute and supplementary carbon source for the growth of denitrifying bacteria due to reversed-solute. The effects of operation mode and influent ammonium (NH4 +) concentration were systematically examined. Compared to a conventional OMBR, the integrated BEA-OMBR achieved higher total nitrogen removal efficiency of 98.13%, and chemical oxygen demand removal efficiency of 95.83% with the influent NH4 +-N concentration of 39 mg L-1. The sequencing analyses revealed that ammonia-oxidizing bacteria (0-0.04%), nitrite-oxidizing bacteria (0-0.16%), and denitrifying bacteria (1.98-8.65%) were in abundance of the microbial community in the feed/anode side of integrated BEA-OMBR, and thus BEA operation increased the diversity of the microbial community in OMBR. Future research will focus on improving nitrogen removal from a high ammonium strength wastewater by looping anolyte effluent to the cathode. These findings have demonstrated that BEA operation can be an effective approach to improve nitrogen removal in OMBRs toward sustainable wastewater treatment.

Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs.

BACKGROUND: For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs. RESULTS: In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations. CONCLUSIONS: The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

Simulated wastewater reduced Klebsiella michiganensis strain LH-2 viability and corresponding antibiotic resistance gene abundance in bio-electrochemical reactors.

A previous study revealed that the electrolytic stimulation process in bio-electrochemical reactors (BER) can accelerate growth of sulfadiazine (SDZ) antibiotic resistant bacteria (ARB) in nutrient broth medium. However, the influence of different medium nutrient richness on the fate of ARB and the relative abundance of their corresponding antibiotic resistance genes (ARGs) in this process is unknown. Specifically, it is not clear if the fate of ARB in minimal nutrition simulated wastewater is the same as in nutrient broth under electrolytic stimulation. Therefore, in this study, nutrient broth medium and the simulated wastewater were compared to identify differences in the relative abundance of Klebsiella michiganensis LH-2 ARGs in response to the electrolytic stimulation process, as well as the fate of the strain in simulated wastewater. Lower biomass, specific growth rates and viable bacterial counts were obtained in response to the application of increasing current to simulated wastewater medium. Furthermore, the percentage of ARB lethality, which was reflected by flow cytometry analysis, increased with current in the medium. A significant positive correlation of sul genes and intI gene relative abundance versus current was also observed in nutrient broth. However, a significant negative correlation was observed in simulated wastewater because of the higher metabolic burden, which may have led to decreased ARB viability. Further investigation showed that the decrease in ARGs abundance was responsible for decreased strain tolerance to SDZ in simulated wastewater. These results reveal that minimal nutrition simulated wastewater may reduce ARB and ARGs propagation in BER.

Effects of voltage on sulfadiazine degradation and the response of sul genes and microbial communities in biofilm-electrode reactors.

Few studies have been performed on both the potential and the risks of biofilm-electrode reactors (BERs) with regard to the removal of antibiotics. This study used 33 BERs to investigate the removal rate and degradation pathway of sulfadiazine (SDZ). Furthermore, the effects of additional electrons on sul genes and microbial community composition were examined. The study found that rapid elimination rates of 20mg/L SDZ were observed during the first 3h with different DC voltage rates. Even high concentrations (160mg/L) could be rapidly removed after 24h of system operation. Pyrimidin-2ylsulfamic acid and aniline were noted to be principal products, and an SDZ degradation mechanism was proposed. The study identified 41 species of microorganism; based on bacterial community divergence caused by voltage, and six samples were grouped into four clusters. The relative abundances of sul genes from biofilm were in the following order: sulII >sulIII >sulI >sulA. The sulI, sulII, and sulA genes were enhanced with electrical stimulation in the cathode layer. It is noteworthy that sul genes were not detected in the effluent after 24h of operation.

Toll-like receptor 4 gene polymorphisms influence milk production traits in Chinese Holstein cows.

The research reported in this Research Communication aimed to describe the influence of Toll-like receptor 4 gene polymorphisms on milk production traits in Chinese Holstein cows. Toll-like receptor 4 (TLR4) is an important member of the toll-like receptor gene family that is widely found in various organisms. Since TLR4 can identify molecular patterns from various pathogenic microorganisms and induce natural and acquired immunity, it plays an important role in disease resistance in dairy cows. Two single nucleotide polymorphisms (SNPs) of TLR4 (c.-226 G > C and c.2021 C > T) that were previously found to be associated with health traits were genotyped using Sequenom MassARRAY (Sequenom Inc., San Diego, CA) for Chinese Holstein cows (n = 866). The associations between SNPs or their haplotypes and milk production traits and somatic cell count were analyzed by the generalized linear model procedure of Statistics Analysis System software (SAS). The c.-226 G > C and c.2021 C > T showed low linkage disequilibrium (r2 = 0·192). There was no association between these two SNPs and SCC, but significant effects were found for SNP c.-226 G > C on test-day milk yield, fat content, protein content, and total solid and milk urea nitrogen (P T and the SNP haplotypes on test-day milk yield, fat content, protein content, lactose content and total solids (P C was located within several potential transcription factor binding sites, including transcription factor AP-2. The polymorphisms c.-226 G > C and c.2021 C > T had significant effects on the milk production for Chinese Holstein, and these SNP could be used for molecular marker-assisted selection of milk production.

The effect of continuous Ni(II) exposure on the organic degradation and soluble microbial product (SMP) formation in two-phase anaerobic reactor.

A two-phase anaerobic reactor fed with glucose substrate (3 g chemical oxygen demand (COD)/L) was used to investigate the effects of toxic metals on the degradation of organics and the soluble microbial product (SMP) formation. Low concentrations of Ni(II) (5 and 10 mg/L) promoted the acid phase, whereas high concentrations (15, 20, and 25 mg/L) exhibited an inhibitory effect on, but did not alter the fermentative method, which mainly involved the fermentation of propionic acid. The methanogenic microorganism exhibited a strong capability adapting constantly increased Ni(II) levels. The acid phase was an accumulation stage of SMP. In the absence of Ni(II), the high-molecular-weight material in the effluent SMP mainly contained polysaccharide, tryptophan, and casein. Methanogens metabolized most of the polysaccharide, the whole tryptophan content, and part of the casein, leading to the presence of humic acid and protein in effluent. After Ni(II) dosage, the protein and polysaccharide of the acid phase increased, and tryptophan changed, while casein remained stable. More protein than polysaccharide was produced, suggesting the prominent function of protein when addressing the negative effect of toxic metals. The analysis of DNA confirmed the change of bacterial activity.

A review of spatial distribution of typical antibiotic resistance genes in marine environment surrounding China.

Antibiotic resistance genes (ARGs) have been steadily increasing due to the extensive overuse of antibiotics in the marine environment. Currently, the research considering ARGs distribution in marine ecosystems gains more interest. As the coastal sea has been regarded as one of the most polluted areas by antibiotic contaminants in China. However, no comprehensive review of the spatial distribution of ARGs in marine environment surrounding China. The main objective of this review is to investigate the level, characteristic, and spatial distribution of ARGs in the marine environment (seawater and sediments) surrounding China. Key sea areas, such as Bohai Sea, Yellow Sea, East China Sea, and South China Sea were selected in this review. The marine environment was the reservoir of ARGs, and ARGs in seawater were generally 1 to 2 orders of magnitude higher than that in sediments. Total ARGs were more abundant in the Yellow Sea, followed by the Bohai Sea, the East China Sea, and the South China Sea. This study raises questions regarding the spread and distribution for antibiotic resistance in marine environments.

Insights to the cooperation of double-working potential electroactive biofilm for performance of sulfamethoxazole removal: ARG fate and microorganism communities.

Bioelectrochemical systems (BESs) have shown great potential in enhancing sulfamethoxazole (SMX) removal. However, electroactive biofilms (EBs) constructed with single potentials struggle due to limited biocatalytic activity, hindering deep SMX degradation. Here, we constructed a double-working potential BES (BES-D) to investigate its ability to eliminate SMX and reduce the levels of corresponding antibiotic resistance genes (ARGs). The preferable electrochemical activity of EB in BES-D was confirmed by electrochemical characterization, EPS analysis, physical structure, viability of the biofilm, and cytochrome content. BES-D exhibited a notably greater SMX removal efficiency (94.2 %) than did the single-working potential BES (BES-S) and the open-circuit group (OC). Degradation pathway analysis revealed that the cooperative EB could accelerate the in-depth removal of SMX. Moreover, EB interaction in BES-D decreased the relative abundance of ARGs in biofilms compared to that in BES-S, although the absolute number of ARG copies increased in BES-D effluents. Compared to those in BES-S and OC, more complex cross-niche microbial associations in the EB of BES-D were observed by network analysis of the bacterial community and ARG hosts, enhancing the degradation efficiency of SMX. In conclusion, BES-D has significant potential for SMX removal and the enhancement of EB activity. Nonetheless, the risk of ARG dissemination in effluent remains a concern.

Chaos M-ary modulation and demodulation method based on Hamilton oscillator and its application in communication.

Chaotic communication has aroused general interests in recent years, but its communication effect is not ideal with the restriction of chaos synchronization. In this paper a new chaos M-ary digital modulation and demodulation method is proposed. By using region controllable characteristics of spatiotemporal chaos Hamilton map in phase plane and chaos unique characteristic, which is sensitive to initial value, zone mapping method is proposed. It establishes the map relationship between M-ary digital information and the region of Hamilton map phase plane, thus the M-ary information chaos modulation is realized. In addition, zone partition demodulation method is proposed based on the structure characteristic of Hamilton modulated information, which separates M-ary information from phase trajectory of chaotic Hamilton map, and the theory analysis of zone partition demodulator's boundary range is given. Finally, the communication system based on the two methods is constructed on the personal computer. The simulation shows that in high speed transmission communications and with no chaos synchronization circumstance, the proposed chaotic M-ary modulation and demodulation method has outperformed some conventional M-ary modulation methods, such as quadrature phase shift keying and M-ary pulse amplitude modulation in bit error rate. Besides, it has performance improvement in bandwidth efficiency, transmission efficiency and anti-noise performance, and the system complexity is low and chaos signal is easy to generate.

A comprehensive analysis of the prognostic and immunotherapeutic characteristics of KIFC1 in pan-cancer and its role in the malignant phenotype of pancreatic cancer.

BACKGROUND: Kinesin family member C1 (KIFC1) is an essential member of the motor protein family, which is critically involved in various cellular events, such as mitosis, meiosis, and macromolecular transport, but also in carcinogenesis, malignant progression, and tumor recurrence. METHODS: The analysis determined the relationship between KIFC1 expression, prognosis significance, immune characteristics landscape, and genetic alterations in pan-cancer with the data extracted from web-based platforms and databases, including but not limited to UCSC, NCBI, GEPIA2, HPA, cBioPortal, SangerBox, UALCAN, GEO and TCGA. Additionally, the expression of KIFC1 in pancreatic cancer tumor tissues and adjacent normal tissues was evaluated through immunohistochemistry. In vitro Edu, colony formation, wound healing, and Transwell assay were done to elucidate the biological functions of KIFC1 in pancreatic cancer cells. RESULTS: The analysis revealed that KIFC1 is upregulated in most cancers, and its increased expression is significantly associated with reduced overall survival and disease-free survival in multiple cancer types. Additionally, strong correlations between KIFC1 expression and tumor immunotherapy were observed across various malignancies. Through univariate and multivariate Cox regression analyses using TCGA data, KIFC1 was identified as an independent predictor of prognosis in pancreatic cancer cases. Furthermore, cellular experiments demonstrated that knockdown of KIFC1 resulted in the suppression of cell proliferation, migration, and invasive ability. CONCLUSIONS: Our study indicated that KIFC1 harbors the potential to be a prognostic and immunotherapeutic biomarker of tumors, and it can have an impact on the metastasis and the cell cycle of pancreatic cancer cells.