Expression and correlation of pre-mRNA 3′ end processing factors in pan-cancer tissuesThe expression patterns of pre-mRNA 3′ end processing factors spanned across 33 unique cancer types (Fig. 1A). The expression data for these pre-mRNA 3′ end processing factors comes from Table S2. Several genes within this family, including CPSF1, CPSF3, CPSF4, NUDT21, PAPOLA, SYMPK, CSTF2, CSTF3, CSTF2T, and CSTF1, manifested elevated expression levels universally across these cancers. We embarked on an exploration of the interplay among various pre-mRNA 3′ end processing factors’ genes (Fig. 1B). Our data revealed a predominantly positive correlation among the expression profiles of most pre-mRNA 3′ end processing factors. To delve deeper, we meticulously examined the expression patterns of all pre-mRNA 3′ end processing factors within these 33 cancer types (Fig. 1C). The p values for comparing the gene expression differences between cancer types and disease groups and normal groups that can be analyzed for multiple differences are shown in Table S3. Notably, CPSF6 showcased pronounced expression in the CHOL category, while CSTF2T’s expression was notably subdued in pan-cancer tissues, especially within KICH (Fig. 1C).Figure 1Expression levels and correlations between pre-mRNA 3′ end processing factors in various cancers from TCGA. (A) Overall expression of pre-mRNA 3′ end processing factors in 33 types of cancers. (B) Correlations between pre-mRNA 3′ end processing factors. Blue and red dots represent positive and negative correlations, respectively. (C) Expression data from TCGA database showing the expression of pre-mRNA 3′ end processing factors in 18 types of cancers. The color of each small rectangle represents high or low expression of pre-mRNA 3′ end processing factors in each cancer. Red and green indicate high and low expression, respectively.Further, we harnessed RNA sequencing data from the TCGA database, processed using R software, aiming to discern the differential expression of pre-mRNA 3′ end processing factors across a myriad of cancer types. Our analysis revealed that CPSF2’s expression was heightened in a variety of cancers, such as BRCA, BLCA, and CHOL, while it was diminished in KIRC (Fig. 2A). CPSF3′s expression trajectory was elevated in numerous cancers, yet it was subdued within kidney chromophobe (KICH) (Fig. 2B). CSTF2 and SYMPK displayed elevated expression levels in a host of cancer types, including but not limited to bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and cholangiocarcinoma (CHOL) (Fig. 2C,D). The subsequent genes, from CPSF4 to PCF11, exhibited varied expression patterns across different cancers, as detailed in (Fig. S1A–M).Figure 2pre-mRNA 3′ end processing factors expression levels in different cancer types and normal tissue. (A) CPSF2, (B) CPSF3, (C) CSTF2, (D) SYMPK. The red rectangle box represents gene expression levels in tumor tissue and the blue rectangle box represents normal tissue. *p < 0.05; **p < 0.01; ***p < 0.001. Red- and blue-colored names indicate high and low expressions of the corresponding pre-mRNA 3′ end processing factors, respectively. BLCA Bladder urothelial carcinoma, BRCA Breast invasive carcinoma, CHOL Cholangiocarcinoma, COAD Colon adenocarcinoma, ESCA Esophageal carcinoma, GBM Glioblastoma multiforme, HNSC Head and neck squamous cell carcinoma, KICH Kidney chromophobe, KIRC Kidney renal clear cell carcinoma, KIRP Kidney renal papillary cell carcinoma, LIHC Liver hepatocellular carcinoma, LUAD Lung adenocarcinoma, LUSC Lung squamous cell carcinoma, PRAD Prostate adenocarcinoma, READ Rectum adenocarcinoma; STAD Stomach adenocarcinoma, THCA Thyroid carcinoma, UCEC Uterine corpus endometrial carcinoma. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).To further dissect the expression profiles of pre-mRNA 3′ end processing factors across diverse cancer cell lines, we sourced data from the CCLE database, embarking on an exhaustive statistical analysis. The gene expression patterns of NUDT21, CPSF1, and PAPOLA were positively correlated with a plethora of cancer cells. In contrast, the gene expression of PCF11, WDR33, CLP1, and CSTF2T was negatively associated with these malignancies (Fig. 3A). In specific cellular contexts, genes like CPSF1, CPSF6, FIP1L1, and NUDT21 displayed elevated expression levels in cell lines derived from breast and other sources. On the flip side, genes like CLP1, CSTF2T, PCF11, and WDR33 manifested reduced expression within these cancer cell lines (Fig. 3B). Additionally, we observed significant mutation events in specific genes across distinct cancer types, as illustrated in (Fig. 3C).Figure 3(A) The pre-mRNA 3′ end processing factors expression in different cancer cell lines (breast, central nervous system, kidney, large intestine, liver, urinary). Red represents high level expression, blue represents low level expression. (B) The pre-mRNA 3′ end processing factors expression in different cancer cell lines (breast, central nervous system, kidney, large intestine, liver, urinary). (C) Mutation frequency of pre-mRNA 3′ end processing factors in different cancer cell lines (LIHC, BRCA, GBM, KIRC, COAD, BLCA) from CCLE database. Red color represents high mutation frequency whereas blue color represents low mutation frequency.Prognostic value of pre-mRNA 3′ end processing factors in pan-cancerWe embarked on a comprehensive exploration of the prognostic implications of pre-mRNA 3′ end processing factors across a spectrum of cancers. Using COX analysis (Fig. 4), we assessed the prognostic risk of these genes in a pan-cancer setting. Supplementary COX analysis results for other pre-mRNA 3′ end processing factors are depicted in (Fig. S2). The detailed data of COX regression analysis is shown in Table S4.Figure 4Correlation analysis of pre-mRNA 3′ end processing factors CPSF2, CPSF3, SYMPK, CSTF2 expression with survival by the COX method in different types of cancers. Different colored lines indicate the risk value of different genes in tumors, hazard ratio < 1 represent low risk and hazard ratio > 1 represent high risk.To gauge the prognostic significance of differentially expressed pre-mRNA 3′ end processing factors in various tumor patients, Kaplan–Meier survival curves were employed. These curves highlighted the relationships between specific pre-mRNA 3′ end processing factors and clinical outcomes. Intriguingly, elevated expression of pre-mRNA 3′ end processing factors correlated with enhanced patient survival rates, whereas diminished expression was linked to decreased survival rates (Fig. 5).Figure 5Kaplan–Meier survival curves comparison of high and low expression of pre-mRNA 3′ end processing factors in pan-cancer. OS survival curves of pre-mRNA 3′ end processing factors in different cancers: (A–D) CPSF2, (E–I) CPSF3, (J–M) CSTF2, (N–Q) SYMPK.For instance, CPSF2 had adverse implications in ACC, LIHC, and UVM but was protective in LGG (Fig. 5A). CPSF3 was detrimental in ACC, KIRC, LIHC, and MESO but beneficial in THYM (Fig. 5B). Similarly, CSTF2 was associated with unfavorable outcomes in LAML, LIHC (Fig. 5C). Meanwhile, SYMPK played a detrimental part in ACC and KIRC and embraced a protective function in UVM and PAAD (Fig. 5D). The patterns continued with CPSF1, CPSF4, NUDT21, CPSF6, CSTF1, CSTF3, CSTF2T, CLP1, WDR33, FIP1L1, RBP6, PAPOLA and PCF11 each showing varied expression implications across different cancer types, as detailed in (Fig. S3A–M).To further understand the expression profiles of pre-mRNA 3′ end processing factors in various cancer cell lines, we sourced data from the CCLE database. Genes like NUDT21, CPSF1, and PAPOLA showed positive correlations with several cancer cells, while genes like PCF11, WDR33, CLP1, and CSTF2T had negative associations. In specific cellular contexts, certain genes displayed elevated or reduced expression levels, influencing the prognosis in various ways. In summary, our findings provide a comprehensive overview of the prognostic implications of pre-mRNA 3′ end processing factors across a range of cancers, offering valuable insights for future research and potential therapeutic interventions.Association of pre-mRNA 3′ end processing factors with TME and stemness score in pan-cancer tissuesThe tumor microenvironment (TME) plays a crucial role in driving cancer cell diversity, enhancing drug resistance, and steering cancer progression and metastasis. Our previous research confirmed the predictive potential of pre-mRNA 3′ end processing factors across various cancers. Understanding the relationship between pre-mRNA 3′ end processing factors expression and the TME in pan-cancer tissues is essential. Table S5 shows the p values and correlation scores of RNA and DNA, as well as the correlation scores of estimate scores.Using the ESTIMATE algorithm, we calculated immune and stromal scores across pan-cancer tissues, as shown in Fig. 6. Notably, there was a strong positive correlation between the scores and the expression of CSTF2T and CLP1 (Fig. 6A,B). Additionally, significant positive or negative correlations were observed between pre-mRNA 3′ end processing factors expression and RNA signatures (Fig. 6C) and DNA signatures (Fig. 6D). We further explored the relationship between pre-mRNA 3′ end processing factors expression and scores related to the immune system, stroma, estimate, and stemness in specific cancers, including KIRC (top) and LIHC (bottom) (Fig. 8). In BLCA and COAD, pre-mRNA 3′ processing factors showed significant correlations with TME, as well as with DNAss and RNAss (Figs. S4–S6). In essence, our findings highlight the profound connection between pre-mRNA 3′ end processing factors and the TME, offering valuable insights for future cancer research.Figure 6Correlation of pre-mRNA 3′ end processing factors expression with tumor microenvironment, Stemness score in pan-cancer. (A, B) Pre-mRNA 3′ end processing factors expression associated with stromal score and immune score in different cancers. Red dots indicate a positive correlation between gene expression in the tumor and stromal score, and blue dots indicate a negative correlation. (C, D) Pre-mRNA 3′ end processing factors expression associated with RNAss and DNAss in different cancers. Red dots indicate a positive correlation between gene expression in the tumor and immune score, and blue dots indicate a negative correlation. RNAss RNA stemness score, DNAss DNA stemness score. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. The color of the circle represents the correlation coefficient, red represents a positive correlation between gene expression and RNA score, while blue represents a negative correlation and the circle size is related to correlation.Association of pre-mRNA 3′ end processing factors with immune subtypes in pan-cancer tissuesPrevious research identified six unique immune subtypes, labeled C1–C629, through an in-depth immunogenomic analysis. The expressioin of pre-mRNA 3′ end processing factors have shown significantly different among these subtypes. Building on this, we explored the relationship between pre-mRNA 3′ end processing factors and these immune subtypes.Distinct expression patterns of pre-mRNA 3′ end processing factors were observed across various pan-cancers (Fig. 7A). In particular, CPSF4, CPSF2, CPSF3, PCF11, CLP1, and CSTF2 showed marked differential expression in bladder urothelial carcinoma (BLCA) (Fig. 7B). In breast invasive carcinoma (BRCA), a range of pre-mRNA 3′ end processing factors, including CPSF1, WDR33, FIP1L1, and others, displayed significant variations in expression (Fig. 7C). In liver hepatocellular carcinoma (LIHC), genes such as CPSF1, WDR33, FIP1L1, and CPSF4, among others, showed notable differences in expression, with CPSF1 being especially elevated (Fig. 7D). Lastly, in kidney renal clear cell carcinoma (KIRC), distinct expression levels were observed for genes like WDR33, CPSF3, NUDT21, and several others (Fig. 7E). These correlations indicate potential areas for further investigation in the context of cancer therapy, contributing to the broader field of precision medicine (Fig. 8).Figure 7Correlation between the expression of pre-mRNA 3′ end processing factors and immune subtypes in BLCA, BRCA, KIRC, LIHC. (A) Correlation between the expression of pre-mRNA 3′ end processing factors and immune subtypes in pan cancers. (B) Correlation between the expression of pre-mRNA 3′ end processing factors and immune subtypes in BLCA. (C) Correlation between the expression of pre-mRNA 3′ end processing factors and immune subtypes in BRCA. (D) Correlation between the expression of pre-mRNA 3′ end processing factors and immune subtypes in LIHC. (E) Correlation between the expression of pre-mRNA 3′ end processing factors and immune subtypes in KIRC. X-axis represents immune subtype, and y-axis represents gene expression. C1, Wound healing; C2, IFN-gdominant; C3, Inflammatory; C4, Lymphocyte depleted; C5, Immunologically quiet; C6, TGF-βdominant. *p < 0.05; **p < 0.01; ***p < 0.001.Figure 8Correlation analysis of the expression of pre-mRNA 3′ end processing factors with RNAss, DNAss, stromal score, immune score, and ESTIMATE score in KIRC and LIHC. The X-axis represents the expression level of genes, while the Y-axis represents RNA, DNA, Stormal Score, Immune Score, Estimate Score.Association of pre-mRNA 3′ end processing factors with pan-cancer drug sensitivity gene therapy treatmentsTo investigate the potential relationship between the expression of pre-mRNA 3′ end processing factors and the susceptibility of various human cancer cell lines to different drugs, as recorded in the CellMiner™ database, we conducted an in-depth correlation analysis. All relevant data, including the expression profiles of these cell lines and their associated drug sensitivities, are detailed in (Table S6). We systematically outlined the set of 17 pre-mRNA 3′ end processing factors in Fig. 9 and (Table S6), each showing a unique association with certain drugs. Our research highlighted significant associations, such as NUDT21 having a positive correlation with susceptibility to pyrazoloacndine, amonaflide, chelerythrine, Fludarabine (Fig. 9A,C,F,M), while it negatively correlated with sensitivity to okadaic acid and hydrastinine HCL (Fig. 9G,N). Additionally, FIP1L1 showed a positive correlation with chelerythrine sensitivity (Fig. 9B). Notably, CPSF6 revealed positive correlations with sensitivity to chelerythrine (Fig. 9D), while CSTF3 was positively associated with ifosfamide (Fig. 9L) and CPSF1 was positively assosciated with fludarabine (Fig. 9E). Similarly, CPSF3 was positively correlated with chelerythrine sensitivity (Fig. 9F), and PCF11 was positively associated with susceptibility to chelerythrine, and PX-316 (Fig. 9J,K). Finally, RBBP6 showed positive correlations with sensitivity to both chelerythrine and PX-316 (Fig. 9H,I). Considering the varied expression patterns of pre-mRNA 3′ end processing factors in tumor tissues compared to adjacent non-tumor tissues, along with the unique RNAss and DNAss profiles and the prognostic significance of pre-mRNA 3′ end processing factors, we identified CPSF2, CPSF3, CSTF2, and SYMPK as standout members of the pre-mRNA 3′ end processing factors.Figure 9Drug sensitivity analysis of pre-mRNA 3′ processing factors. The x-axes are the sensitivity of certain drugs and the y-axes are the expression level of certain pre-mRNA 3′ processing factors.Association of pre-mRNA 3′ end processing factors with pan-cancer immune microenvironmentAs a component of the pre-mRNA 3′end processing machinery, the cooperative interplay among CPSF2, CPSF3, SYMPK and CSTF2 modulates mRNA 3′UTR processing activity. Although recent studies have implicated their involvement in certain tumors, a comprehensive analysis is still lacking30,31,32,33. TMB has been recognized as a valuable biomarker for predicting the outcomes of immunotherapy. Importantly, a higher TMB level suggests increased effectiveness in tumor immunotherapies34,35. Moreover, Microsatellite Instability (MSI) has been linked to tumor progression36,37. Given these insights, we sourced TMB and MSI data from the TCGA database to explore the complex relationship between TMB/MSI and the expression of CPSF2, CPSF3, CSTF2, and SYMPK (Fig. 10A,B). Tables S7 and S8 represent the MSI and TMB scores of CPSF2, respectively. A significant correlation was observed between CPSF2 expression and TMB in various cancers, including LUAD, LUSC, STAD, THYM, and UCEC (Fig. 10A). Similarly, a pronounced association was found between CPSF3 expression and TMB in cancers like BLCA, BRCA, and HNSC. A significant relationship was also noted between CSTF2 expression and TMB in cancers such as BLCA, LGG, LUAD, SARC, SKCM, STAD, and UCEC. Concurrently, CPSF2 expression showed a strong correlation with MSI in several cancers, including BRCA, COAD, DLBC, HNSC, OV, PRAD, SKCM, THCA, and UCEC. CSTF2 expression also correlated significantly with MSI in cancers like ACC, BRCA, COAD, KIRC, and UCEC (Fig. 10B). Similarly, SYMPK expression had a significant association with MSI in cancers such as BRCA, HNSC, LIHC, LUAD, LUSC, PRAD, and READ.Figure 10(A) Correlation of TMB with the expression of CPSF2, CPSF3, CSTF2, and SYMPK. (B) Correlation of MSI with the expression of CPSF2, CPSF3, CSTF2 and SYMPK. *p < 0.05; **p < 0.01; ***p < 0.001.
