Human leukocyte antigen class I (HLA-I) is central to tumor immune recognition, but its regulatory mechanisms in cervical cancer remain poorly understood. This study aimed to elucidate the impact of HLA-I regulatory mechanisms on CD8+ T-cell infiltration and identify distinct histotype-specific immune escape strategies across cervical cancer subtypes. Using 98 cervical cancer cases, including squamous cell carcinoma (SCC; n = 53), adenocarcinoma (n = 32), gastric-type adenocarcinoma (GAS; n = 5), small cell carcinoma (Small, n = 4), and mixed histologic types (n = 4), we examined the relationship between CD8+ T-cell infiltration patterns (categorized as infiltrated, excluded, or absent) and HLA-I expression, HLA-A DNA methylation, and HLA-I loss of heterozygosity (LOH). CD8+ T-cell infiltration patterns varied significantly by histologic subtype (P < 0.0001). SCC showed the highest frequency of the infiltrated pattern (73.6%), whereas GAS and Small predominantly displayed an absent pattern. Reduced CD8+ T-cell infiltration correlated with poor survival (P < 0.0001). HLA-I expression mirrored these trends being highest in SCC and lowest in Small and GAS. HLA-A DNA methylation emerged as a key driver of HLA-I downregulation, leading to reduced CD8+ infiltration (P < 0.05). In SCC, both HLA-A methylation and HLA-I LOH contributed to immune evasion; cases lacking these alterations exhibited the highest CD8+ T-cell infiltration levels (P < 0.01). This study identifies distinct HLA-I regulatory mechanisms in cervical cancer, highlighting HLA-A methylation-and particularly HLA-I LOH in SCC-as key drivers of immune evasion. These findings provide a foundation for developing predictive biomarkers and suggest that targeting these specific HLA-I regulatory mechanisms could enhance immunotherapy efficacy.

Human papillomavirus (HPV) infection is a primary driver of cervical cancer. Integration of HPV into the human genome causes persistent expression of viral oncogenes E6 and E7, which promote carcinogenesis and disrupt host genomic function. However, the impact of integration on host gene expression remains incompletely understood. We used multimodal RNA sequencing, combining total RNA-seq and Cap Analysis of Gene Expression (CAGE), to clarify virus-host interactions after HPV integration. HPV-derived transcripts were detected in 17 of 20 clinical samples. In most specimens, transcriptional start sites (TSSs) showed predominant early promoter usage, and transcript patterns differed with detectable E4 RNA region. Notably, the high RNA expressions of E4 region and viral-human chimeric RNAs were mutually exclusive. Chimeric RNAs were identified in 13 of 17 samples, revealing 16 viral integration sites (ISs). CAGE data revealed two patterns of TSS upregulation centered on the ISs: a two-sided pattern (43.8%) and a one-sided pattern (31.3%). Total RNA-seq showed upregulation of 12 putative cancer-related genes near ISs, including MAGI1-AS1, HAS3, CASC8, BIRC2, and MMP12. These findings indicate that HPV integration drives transcriptional activation near ISs, enhancing expression of adjacent oncogenes. Our study deepens understanding of HPV-induced carcinogenesis and informs precision medicine strategies for cervical cancer.

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

Abstract

Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.