Fig 2: Functional prediction and validation of IL11 in the development of PCa and AD. (A) Bar graph showing the knockdown efficiency of IL11 in RM-1 cells as determined by qRT-PCR. *p < 0.05 by two tailed unpaired t test, compared to si-NC group. (B) Bar graph showing the knockdown efficiency of IL11RA in RM-1 cells as determined by qRT-PCR. *p < 0.05 by two tailed unpaired t test, compared to si-NC group. (C) Colony formation assay examining the effect of IL11 intervention on the growth of the mouse PCa cell line RM-1. (D) Wound healing assay examining the effect of IL11 intervention on the invasiveness of RM-1 cells. Bar plot showing the results of quantitative comparison between groups. ****p < 0.0001 by two tailed unpaired t test. (E) Transwell assay examining the effect of IL11 intervention on the migration ability of RM-1 cells. ****p < 0.0001 by two tailed unpaired t test. (F) In vivo bioluminescence imaging, and quantification in bar plots of orthotopic prostate tumor growth in mice between two groups. *p < 0.05 by two tailed unpaired t test. BLI, bioluminescence intensity. (G) IF staining and quantitative analyses of CD206 (red) abundance in orthotopic prostate tumors of mice across IL11-treated and control groups. Scale bar = 50 μm. **p < 0.01 by two tailed unpaired t test. (H) IF staining and quantitative analyses of FOXP3 (red) abundance in orthotopic prostate tumors of mice across IL11-treated and control groups. Scale bar = 50 μm. ****p < 0.0001 by two tailed unpaired t test. (I) Escape latency of IL11-treated mice and the controls for 5 consecutive days in the MWM test. *p < 0.05 by two tailed unpaired t test, compared to NC group. (J) Percentage of time spent in the platform quadrant in the spatial probe trial of the MWM test. **p < 0.01 by two tailed unpaired t test, compared to NC group. (K) The number of platform crossings in the spatial probe trial of the MWM test. **p < 0.01 by two tailed unpaired t test, compared to NC group. (L) Representative heat maps of swimming paths in the spatial probe trial of the MWM test. (M) IF staining and quantitative analyses of Aβ (6E10, red) in the hippocampus of IL11-treated and control mice. Scale bar = 100 μm. ***p < 0.001 by two tailed unpaired t test. (N) Boxplots of CSF p-tau 181 levels in high-IL-11 (top 25%) and low-IL-11 (bottom 75%) groups in ADNI cohort. Groups were compared by Welch’s t-test. The boxes represent the median and interquartile range (IQR), with the whiskers representing the data range (1.5 × IQR). (O) Boxplots of CSF Aβ42 levels in high-IL-11 (top 25%) and low-IL-11 (bottom 75%) groups in ADNI cohort. Groups were compared by Welch’s t-test. The boxes represent the median and interquartile range (IQR), with the whiskers representing the data range (1.5 × IQR).

Fig 3: Machine learning screening of diagnostic and prognostic comorbidity genes for AD-PCa. (A) Biomarker screening based on the SVM-RFE algorithm in AD datasets. The lines represent the error rates as the number of decision trees increases: the black line typically denotes the Out-of-Bag (OOB) error, while other lines denote class-specific errors. (B) Lollipop plot showing the top 50 comorbidity genes ranked by importance based on Mean Decrease Accuracy (MDA) in the Random Forest (RF) model. The top ranked gene, IL11, is highlighted with a red rectangle. (C) Lollipop plot showing the top 50 comorbidity genes ranked by importance based on Mean Decrease Gini (MDG) in the Random Forest (RF) model. The top ranked gene, IL11, is highlighted with a red rectangle. (D) Venn diagram showing that a total of 43 genes exhibited high importance after evaluation by both MDA and MDG. (E) Biomarker screening based on the Lasso regression algorithm in AD datasets. The curve’s lowest point corresponded to the optimal gene count. Each curve in LASSO coefficient diagram represented one specific gene. The lines depict the coefficient profiles of the individual candidate genes as a function of the penalization parameter (Log Lambda). (F) Forest plot showing the prognostic value of 10 candidate comorbidity genes, ANLN, AGAP9, IL11, DNASE1, DYNC1I1, NDRG4, SPP1, ENO2, ISG15 and ATP6V1G2, selected by LASSO regression for progression-free interval (PFI) in prostate cancer, as evaluated by a multivariate Cox regression model. (G) Box plots comparing the expression levels of candidate comorbidity genes between the AD group and healthy control group samples. ***p < 0.001 by two tailed unpaired t test. Multi-comparisons were corrected using FDR. The boxes represent the median and IQR, with the whiskers representing the data range (1.5 × IQR). (H) Box plots comparing the expression levels of candidate comorbidity genes between the PCa group and normal group samples. **p < 0.01, ***p < 0.001 by two tailed unpaired t test. Multi-comparisons were corrected using FDR. The boxes represent the median and IQR, with the whiskers representing the data range (1.5 × IQR).