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C19H16O7, monoclinic, C2/c (no. 15), a = 30.5661(14) Å, b = 7.0209(3) Å, c = 17.2028(7) Å, β = 117.342(1)°, V = 3279.3(2) Å3, Z = 8, Rgt(F) = 0.0489, wRref(F2) = 0.1398, T = 273(2) K.

C20H18O8, monoclinic, P21/c (no. 14), a = 7.3855(2) Å, b = 28.1424(9) Å, c = 8.5863(3) Å, β = 98.4040(10)°, V = 1765.46(10) Å3, Z = 4, R gt(F) = 0.0545, wR ref(F 2) = 0.1641, T = 273(2) K.

C19H16O7, monoclinic, C2/c (no. 15), a = 30.5661(14) Å, b = 7.0209(3) Å, c = 17.2028(7) Å, β = 117.342(1)°, V = 3279.3(2) Å3, Z = 8, Rgt(F) = 0.0489, wRref(F2) = 0.1398, T = 273(2) K. [ABSTRACT FROM AUTHOR]

C19H16O7, monoclinic, C2/c (no. 15), a = 30.5661(14) Å, b = 7.0209(3) Å, c = 17.2028(7) Å, β = 117.342(1)°, V = 3279.3(2) Å3, Z = 8, Rgt(F) = 0.0489, wRref(F2) = 0.1398, T = 273(2) K. [ABSTRACT FROM AUTHOR]

C20H18O8, monoclinic, P21/c (no. 14), a = 7.3855(2) Å, b = 28.1424(9) Å, c = 8.5863(3) Å, β = 98.4040(10)°, V = 1765.46(10) Å3, Z = 4, R gt (F) = 0.0545, wR ref (F 2) = 0.1641, T = 273(2) K. [ABSTRACT FROM AUTHOR]

Objective: To explor the potential mechanisms of ferroptosis involvement in non-obstructive azoospermia based on bioinformatics and machine learning methods., Methods: To obtain disease-related datasets and ferroptosis-related genes, we utilized the GEO database and FerrDb database, respectively. Using the R software, the disease dataset was subjected to normalization, differential analysis, and GO and KEGG enrichment analysis. The differentially expressed genes from the disease dataset were then intersected with the ferroptosis-related genes to identify common genes. Core genes were selected using three machine learning algorithms, namely LASSO, SVM-RFE, and random forest. Further analysis included exploring immune infiltration correlation, predicting target drugs, and conducting molecular docking simulations., Results: The differential analysis of the GSE45885 dataset yielded 1751 differentially expressed genes, while the GSE145467 dataset yielded 4358 differentially expressed genes. The intersection of these two gene sets resulted in a disease-related gene set consisting of 508 genes. Taking the intersection of the disease-related gene set and the ferroptosis-related gene set, we obtained 17 disease-related ferroptosis genes. After machine learning-based screening, three core genes were identified: GPX4, HSF1, and KLHDC3., Conclusion: The mechanism underlying the involvement of ferroptosis in non-obstructive azoospermia may be linked to the downregulation of GPX4, HSF1, and KLHDC3 expression. This finding provides a basis for subsequent in-depth mechanistic and therapeutic studies.