## 4 Discussion
This observation may be related to differences in microenvironmental cell composition within the relapsed MCL tumor tissues. Evaluation of tumor-infiltrating immunocytes, together with clinical variables or prognostically significant transcripts, can be used to refine risk-groups during the diagnosis of MCL cases. However, it should be noted that our results in this study are largely exploratory, and they need to be validated in an independent cohort of MCL cases. There are some limitations of this study. First, the number of MCL cases included in this study is not very large. Especially, the observations related to patient survival need to be validated in an independent cohort of MCL patients. Second, we have not compared the expression level changes in diagnostic vs. relapsed MCL cases, as the number of tumor samples from relapsed MCL cases was too few. Comparison of the changes in expression level in patient-matched tumor samples collected at diagnosis and relapse stages may provide useful information regarding oncogenes or tumor suppressors associated with MCL relapse. Third, our study has not evaluated the transcriptional biomarkers associated with drug resistance, as tumor samples were not collected after therapy. Future studies comparing transcript expression levels before and after specific types of therapies can potentially reveal transcriptional biomarkers whose expression predicts therapy response. A recent study proposed four different prognostically distinct clusters based on integrative analysis of transcriptomic and genomic profiles [19] of MCL tumor samples. Given that Clusters 3 and 4 were identified as being associated with poor outcome, integration of the cancer-associated transcript (e.g., VWF) levels and/or TME immunocyte (e.g., CD8+T-cells) ratios into this model may be useful in further refinement of these MCL subgroups. It would also be important to evaluate the expression levels of the candidate oncogenes or tumor suppressors identified in this study in different parts of the tumor tissues in order to shed light onto their expression patterns and dynamics in different stages of malignant tumorigenesis, for which single-cell transcriptomic approaches may be especially useful. Of note, only one single-cell transcriptomic-based study has been reported for MCL tumors, which included a limited number of MCL cases [102]. Different quality checks were applied to ensure the reliability of the WTS analyses performed in this study, as total RNA from FFPE tumor tissues and freshly sorted tonsil B-cell subsets were analyzed altogether. First, we addressed whether mRNAs or lncRNAs known to be overexpressed in MCL cases have markedly high expression in our cohort of MCL cases or not. Consistent with the observations in previous reports [103], we detected significant upregulation of CCND1 and SOX11 mRNAs in mantle cell lymphoma cases (Figure 2). Similarly, ROR1-AS1 lncRNA was overexpressed in MCL cases compared to control cases in our cohort, which is in line with a previous publication showing ROR1-AS1 upregulation in MCL cases [73]. Overexpression of LINK-A lncRNA observed in MCL tumor samples further supported the reliability of the analytical pipeline for differential expression of WTS data applied in this study, as LINK-A was previously reported to be overexpressed in MCL cases [20]. As a second quality check for WTS analyses, overexpression of CCND1 and underexpression of SNHG5 were successfully cross-validated with qRT-PCR (Figure 12A-D). The finding that sample-by-sample comparison of CCND1 mRNA and SNHG5 lncRNA expression levels showed a strong positive correlation in linear correlation analyses further supported the reliability of the differential expression analyses of the WTS data of mantle cell lymphoma cases and reactive tonsl B-cell subsets.