Human endogenous retroviruses (HERVs) and other long terminal repeat (LTR)-type retrotransposons (HERV/LTRs) have regulatory elements that possibly influence the transcription of host genes. We systematically identified and characterized these regulatory elements based on publicly available datasets of ChIP-Seq of 97 transcription factors (TFs) provided by ENCODE and Roadmap Epigenomics projects. We determined transcription factor-binding sites (TFBSs) using the ChIP-Seq datasets and identified TFBSs observed on HERV/LTR sequences (HERV-TFBSs). Overall, 794,972 HERV-TFBSs were identified. Subsequently, we identified “HERV/LTR-shared regulatory element (HSRE),” defined as a TF-binding motif in HERV-TFBSs, shared within a substantial fraction of a HERV/LTR type. HSREs could be an indication that the regulatory elements of HERV/LTRs are present before their insertions. We identified 2,201 HSREs, comprising specific associations of 354 HERV/LTRs and 84 TFs. Clustering analysis showed that HERV/LTRs can be grouped according to the TF binding patterns; HERV/LTR groups bounded to pluripotent TFs (e.g., SOX2, POU5F1, and NANOG), embryonic endoderm/mesendoderm TFs (e.g., GATA4/6, SOX17, and FOXA1/2), hematopoietic TFs (e.g., SPI1 (PU1), GATA1/2, and TAL1), and CTCF were identified. Regulatory elements of HERV/LTRs tended to locate nearby and/or interact three-dimensionally with the genes involved in immune responses, indicating that the regulatory elements play an important role in controlling the immune regulatory network. Further, we demonstrated subgroup-specific TF binding within LTR7, LTR5B, and LTR5_Hs, indicating that gains or losses of the regulatory elements occurred during genomic invasions of the HERV/LTRs. Finally, we constructed dbHERV-REs, an interactive database of HERV/LTR regulatory elements (http://herv-tfbs.com/). This study provides fundamental information in understanding the impact of HERV/LTRs on host transcription, and offers insights into the transcriptional modulation systems of HERV/LTRs and ancestral HERVs. Human endogenous retroviruses (HERVs) are genomic “fossils” of ancient exogenous retroviruses and their descendants that were replicated in host germ cells. The traits and evolutionary dynamics of ancient retroviruses and their descendants can be inferred by scrutinizing present-day HERVs. We systematically identified regulatory elements of HERVs based on publicly available datasets of ChIP-Seq of 97 TFs. Clustering analysis showed that HERV/LTRs can be grouped by the TF-binding patterns; HERV/LTR groups bounded by pluripotent TFs (e.g., SOX2, POU5F1, and NANOG), embryonic endoderm/mesendoderm TFs (e.g., GATA4/6, SOX17, and FOXA1/2), and hematopoietic TFs (e.g., SPI1 (PU1), GATA1/2, and TAL1) were identified. By analyzing the three-dimensional chromosomal interactions, we demonstrated that regulatory elements of HERVs tend to interact with host immune-response genes. We further demonstrated heterogeneities of regulatory elements within LTR7; SOX2, POU5F1, and KLF4-binding sites were highly enriched in the youngest subgroup of LTR7, which had the highest transcriptional activity in pluripotent cells. This suggests that the subgroup acquired those regulatory activities for efficient replication in the host germ cells. Finally, we constructed dbHERV-REs, an interactive database of HERV/LTR regulatory elements (http://herv-tfbs.com/). This study provides insights into regulatory elements of HERVs and transcriptional modulations of host genes by HERVs.

Prostate cancer (PC) is the second most common cancer in men. Family history is the major risk factor for PC. Only two susceptibility genes were identified in PC, BRCA2 and HOXB13. A comprehensive search of germline variants for patients with PC has not been reported in Japanese families. In this study, we conducted exome sequencing followed by Sanger sequencing to explore responsible germline variants in 140 Japanese patients with PC from 66 families. In addition to known susceptibility genes, BRCA2 and HOXB13, we identified TRRAP variants in a mutually exclusive manner in seven large PC families (three or four patients per family). We also found shared variants of BRCA2, HOXB13, and TRRAP from 59 additional small PC families (two patients per family). We identified two deleterious HOXB13 variants (F127C and G132E). Further exploration of the shared variants in rest of the families revealed deleterious variants of the so-called cancer genes (ATP1A1, BRIP1, FANCA, FGFR3, FLT3, HOXD11, MUTYH, PDGFRA, SMARCA4, and TCF3). The germline variant profile provides a new insight to clarify the genetic etiology and heterogeneity of PC among Japanese men.

This study developed a two-stage, explainable machine learning framework to predict 18-month MMSE-based cognitive status from baseline multimodal data in community-dwelling older adults in Japan. A hierarchical design was used in which Stage 1 distinguished cognitively Normal participants from those with any abnormality (Possible Mild Cognitive Impairment (MCI) or Impaired), and Stage 2 further separated Possible MCI from Impaired within the abnormal subgroup. Both an Imbalanced-Learn Random Forest and a penalized logistic regression baseline were trained under Leave-One-Out Cross-Validation, yielding fair discrimination in Stage 1 (Random Forest AUC = 0.72, accuracy = 0.71; logistic regression AUC = 0.71, accuracy = 0.76) and apparently strong separability in Stage 2 (Random Forest AUC = 0.95, accuracy = 0.96; logistic regression AUC = 0.82, accuracy = 0.92) in a small sample size with high class imbalance. SHapley Additive exPlanations (SHAP) with TreeExplainer for Random Forest and LinearExplainer for logistic regression were used to identify interpretable biomarkers at each stage though feature attribution. In Stage 1, both models highlighted renal and systemic metabolic markers (e.g., creatinine, uric acid, blood urea nitrogen), amino acid and redox-related metabolites (including D-serine, D-amino acid proportions, L-asparagine, alanine, L-glutamic acid, cysteine, methionine sulfoxide), and wearable-derived activity variability (e.g., fluctuation coefficients and steps per minute), with the Simpson index of gut microbiome diversity also contributing in the logistic model. In Stage 2, the models converged on a distinct signature involving glucose and albumin, uric acid and uridine, choline and carnitine, multiple amino acids (such as phenylalanine, proline, ornithine, tryptophan, threonine, and short-chain amino acids), oxidative/energy markers (niacinamide, methionine, methionine sulfoxide, ergothioneine), hematologic indices, and high-MET activity fluctuation metrics. Collectively, these results support a stage-dependent, multisystem view of cognitive aging in which broad renal–metabolic, amino acid, and behavioral vulnerabilities characterize early abnormality, whereas more pronounced alterations in energy metabolism, nucleotide and choline pathways, oxidative stress, and activity irregularity accompany progression from Possible MCI to Impaired status. By combining routine clinical chemistry, targeted metabolomics, gut microbiome diversity, and wearable-derived behavioral measures within an explainable AI framework, this two-stage approach illustrates a scalable, biologically grounded strategy for stage-aware risk stratification and monitoring of cognitive decline in community settings.

Long non-coding RNAs (lncRNAs) are important regulators of oncogenesis. In this study, we investigated the tumor-promoting role and prognostic value of the lncRNA PSMB8-AS1 in renal cell carcinoma (RCC). Using lncRNA microarray analysis, we identified PSMB8-AS1 as a candidate gene associated with disease progression and immune checkpoint inhibitor resistance. We examined PSMB8-AS1 expression in tumors and adjacent normal renal tissues from 192 patients with RCC. In vitro functional assays were performed to assess their role in cell proliferation and invasion. We also conducted bioinformatics and luciferase reporter assays to clarify the interaction between miR-204-5p/miR-211 and the transcription factor TFAP2A. PSMB8-AS1 was significantly overexpressed in clear cell RCC (ccRCC) tissues and correlated with poor prognosis. Knockdown experiments revealed that PSMB8-AS1 promoted proliferation and invasion. Mechanistically, PSMB8-AS1 functions as a competing endogenous RNA, sponging miR-204-5p/miR-211 and enhancing TFAP2A expression. These findings suggest that PSMB8-AS1 represents a promising biomarker for postoperative ccRCC recurrence.