Zheying Zhang

Cancer immunotherapy works by stimulating and strengthening the body's anti-tumor immune response to eliminate cancer cells. Over the past few decades, immunotherapy has shown remarkable efficacy in the treatment of cancer, particularly the success of immune checkpoint blockade targeting CTLA-4, PD-1 and PDL1, which has led to a breakthrough in tumor immunotherapy. Tumor neoantigens, a new approach to tumor immunotherapy, include antigens produced by tumor viruses integrated into the genome and antigens produced by mutant proteins, which are abundantly expressed only in tumor cells and have strong immunogenicity and tumor heterogeneity. A growing number of studies have highlighted the relationship between neoantigens and T cells' recognition of cancer cells. Vaccines developed against neoantigens are now being used in clinical trials in various solid tumors. In this review, we summarized the latest advances in the classification of immunotherapy and the process of classification, identification and synthesis of tumor-specific neoantigens, as well as their role in current cancer immunotherapy. Finally, the application prospects and existing problems of neoantigens were discussed.

Serine/arginine-rich splicing factors (SRSFs) are a family of 12 RNA-binding proteins crucial for the precursor messenger RNA (pre-mRNA) splicing. SRSFs are involved in RNA metabolism events such as transcription, translation, and nonsense decay during the shuttle between the nucleus and cytoplasm, which are important components of genome diversity and cell viability. SRs recognize splicing elements on pre-mRNA and recruit the spliceosome to regulate splicing. In tumors, aberrant expression of SRSFs leads to aberrant splicing of RNA, affecting the proliferation, migration, and anti-apoptotic ability of tumor cells, highlighting the therapeutic potential of targeted SRSFs for the treatment of diseases. The body's immune system is closely related to the occurrence and development of tumor, and SRSFs can affect the function of immune cells in the tumor microenvironment by regulating the alternative splicing of tumor immune-related genes. We review the important role of SRSFs-induced aberrant gene expression in a variety of tumors and the immune system, and prospect the application of SRSFs in tumor. We hope that this review will inform future treatment of the disease.

Context: Organizations adopting Artificial Intelligence (AI) face challenges in eliciting and analyzing requirements that align with strategic objectives, especially when human oversight and iterative refinement are needed. Large Language Models (LLMs)-based Multi-agent systems provide a potential solution by supporting structured and collaborative Requirements Engineering (RE) processes for AI adoption planning. Objective: The objective of this study is to investigate whether a multi-agent system, built on LLMs and supported by human input, can assist in requirements analysis for AI adoption. Method: We used a mixed-method approach: (i) designed and developed a multi-agent system to support the generation and prioritization of requirements for AI adoption, (ii) conducted multiple case studies with four companies to evaluate the system, and (iii) collected data through post-session questionnaires from nine participants and follow-up interviews, one per company. Results: Questionnaire and interview findings together indicate that the system may assist in identifying relevant and goal-aligned requirements. Seven participants considered the generated requirements relevant, and six found them aligned with organizational goals. Participants noted that iterative feedback improved completeness and feasibility, often within two feedback rounds. Both data sources show that human input was essential to clarify technical details, ensure contextual accuracy, and validate prioritization results. Participants from all companies also identified usability, transparency, and scalability as areas requiring further refinement for broader organizational use. Conclusions: LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts. Future work will include more interviews with stakeholders and adjustments to system features to improve transparency, usability, and scalability.