BackgroundIncreasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.MethodsThe ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.ResultsThree immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.ConclusionsOur study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.
NAC transcription factors play an important regulatory role in tomato fruit ripening. We chose a novel perspective to explore the traces left by two paleopolyploidizations in the NAC family using a bioinformatics approach. We found that 85 (S. lycopersicum) and 88 (S. pennellii) members of the NAC family were present in two tomatoes, and most of them were amplified from two paleohexaploidizations. We differentiated NAC family members from the different paleohexaploidizations and found that the SWGT-derived NAC genes had more rearrangement events, so it was different from the DWGT-derived NAC genes in terms of physicochemical properties, phylogeny, and gene location. The results of selection pressure show that DWGT-derived NAC genes tended to be positively selected in S. lycopersicum and negatively selected in S. pennellii. A comprehensive analysis of paleopolyploidization and expression reveals that DWGT-derived NAC genes tend to promote fruit ripening, and are expressed at the early and middle stages, whereas SWGT-derived NAC genes tend to terminate fruit growth and are expressed at the late stages of fruit ripening. This study obtained NAC genes from different sources that can be used as materials for tomato fruit development, and the method in the study can be extended to the study of other plants.
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