Summary. Introduction. Appendiceal mucinous neoplasms (AMNs) represent a rare and diagnostically challenging group of tumors. This systematic review aims to summarize the reported molecular and immunohistochemical markers (IHC) associated with AMNs and compare them with ovarian mucinous neoplasms (OMNs) and colorectal adenocarcinoma (CRC). Methods. A comprehensive search was performed in PubMed/MEDLINE/PMC, Scopus, Embase, and Web of Science databases to identify studies looking at IHC and molecular markers in AMNs. Chi-squared and Fisher’s exact tests were utilized to compare the marker expression across different tumor types. Results. We identified 27 articles reporting several potential biomarkers for distinguishing between different subtypes of AMNs. Mutations in KRAS, GNAS, and RNF43 emerged as notable biomarkers, with KRAS mutations being the most prevalent across all subtypes. Additionally, p53 IHC overexpression was associated with higher tumor grades. When comparing AMNs with OMNs, we observed a higher prevalence of CK20, CDX2, SATB2, and MUC2 IHC expression, as well as KRAS and GNAS mutations, in AMNs. Conversely, CK7 and PAX8 IHC expression were more prevalent in OMNs. Comparing AMNs with CRCs, we found a higher prevalence of TOPO1 and PTEN IHC expression, as well as KRAS and GNAS mutations, in AMNs. Conversely, nuclear β-catenin IHC expression, as well as TP53, APC, and PIK3CA mutations, were more prevalent in CRCs. Conclusion. This systematic review identified possible markers for distinguishing AMNs and differentiating between AMNs, OMNs, or CRCs. Key words: Appendiceal mucinous neoplasm, Ovarian mucinous neoplasm, Colorectal adenocarcinoma, Immunohistochemistry, Molecular Introduction Appendiceal neoplasms are rare gastrointestinal tumors, present in approximately 0.2% of all appendectomy specimens (Hananel et al., 1998). Different classification methods have been used for appendiceal mucinous neoplasms (AMNs) which are a heterogeneous group of disorders with diverse malignant potential (Shaib et al., 2017). Early-stage AMNs are generally discovered by coincidence during resection for appendicitis (Kelly, 2015), whereas advanced forms usually present as abdominal distension due to mucus accumulation in the peritoneal cavity known as pseudomyxoma peritonei (PMP) (Valasek et al., 2017). In 2010, the World Health Organization (WHO) defined low-grade appendiceal mucinous neoplasm (LAMN) as a distinct entity with undulating or flat epithelium with low-grade cytological atypia, expansile or diverticulum-like growth patterns and a tendency to produce penetrating acellular mucin deposits. In contrast, mucinous tumors with high-grade cytological atypia and/or infiltrative growth patterns or desmoplastic stroma were classified by default as adenocarcinomas (Carr and Sobin, 2010). In 2016, the term 'high‐grade appendiceal mucinous neoplasm' (HAMN) was proposed to describe lesions with growth patterns comparable to LAMN but featuring high‐grade cytological atypia (Carr et al., 2016). According to the most recent WHO classification in 2019 (Nagtegaal et al., 2020), AMNs are classified histologically into LAMNs, HAMNs, and Molecular and immunohistochemical markers in appendiceal mucinous neoplasms: A systematic review and comparative analysis with ovarian mucinous neoplasms and colorectal adenocarcinoma Basel Elsayed1, Amgad Mohamed Elshoeibi1, Mohamed Elhadary1, Abdullah M. Al-Jubouri1, Noof Al-Qahtani1, Semir Vranic2 and Rafif Al-Saady2* 1College of Medicine and 2Department of Pathology, College of Medicine, QU Health, Qatar University, Doha, Qatar *Senior author Histol Histopathol (2025) 40: 621-633 Corresponding Author: Basel Elsayed, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar. e-mail: be1905231@qu.edu.qa www.hh.um.es. DOI: 10.14670/HH-18-830 istology and istopathology H REVIEWOpen Access ©The Author(s) 2024. Open Access. This article is licensed under a Creative Commons CC-BY International License. From Cell Biology to Tissue Engineering mucinous adenocarcinomas (MAC). Another classification is the AJCC 8th Cancer Staging Manual, which groups AMNs into three grades (Amin et al., 2017). Grade 1 (G1) is well-differentiated, whereby tumors lack invasion, similar to LAMN in the WHO criteria. Grade 2 (G2) and Grade 3 (G3) are considered moderately and poorly differentiated, respectively. The classification favors G3 if the tumor shows signs of infiltrative invasion, with a prominent histologic feature being the presence of signet-ring cells (Kang et al., 2021). G3 is similar to MAC in the WHO classification. Differentiating between low and high-grade AMNs is crucial prognostically and for management but it can be challenging morphologically. IHC and molecular techniques may assist in this differentiation, emphasizing their importance in clinical practice. AMNs occasionally display vague clinical manifestations, making it difficult to diagnose them before surgery. However, due to their proximity to the adnexa, gynecologists may mistakenly identify these tumors as adnexal masses (Shaib et al., 2017). A correct preoperative diagnosis of either a primary ovarian mucinous neoplasm (OMN) or an AMN in female patients is very challenging due to the lack of specific tumor biomarkers and inconsistent imaging manifestations, such as in ultrasonography, computed tomography, and magnetic resonance (Papoutsis et al., 2012; Tanaka et al., 2019; Zhang et al., 2019). In addition, it is challenging to differentiate between these two neoplasms intraoperatively due to similar macroscopic and microscopic characteristics associated with primary and metastatic mucinous ovarian tumors (Dundr et al., 2021). Regarding the differentiation between AMNs and colorectal adenocarcinoma (CRC), AMNs are usually less aggressive than CRC and rarely metastasize outside the peritoneal cavity (Shaib et al., 2017). Some markers are commonly prevalent in AMNs and CRC, an example is the expression of SATB2 (Carr et al., 2017). Researchers have also suggested that the CDX2 IHC marker should also be added as it is the most sensitive when trying to discern a tumor of appendiceal/colonic origin (Aldaoud et al., 2019). The differences in expression of these markers and others are crucial when trying to correlate the rates of expression to the histological, pathological, and clinical presentations (Tokunaga et al., 2019). Due to the rarity of AMNs, similar published reviews are scarce. For instance, the latest review conducted by Yanai et al. in 2021 focused solely on the molecular characteristics of AMNs (Yanai et al., 2021). Our systematic review had two primary objectives. Firstly, we aimed to conduct a comparative analysis of molecular alterations and IHC findings across various AMN subtypes. Secondly, we aimed to identify markers that can effectively distinguish between AMNs, OMNs, and CRC. In our review, we assessed IHC and molecular biology methods to provide a comprehensive analysis of marker expression across different tumor types. This approach allows for a holistic view of biomarker prevalence and variation. Materials and methods Protocol and Registration The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to prepare this systematic review (see PRISMA checklist in Elsayed-40-621-633-2025.pdf | Supplementary File ) (Moher et al., 2009). The review protocol was submitted to the online database of the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42022379547). Search strategy The literature search was performed up to the 20th of January 2024, initially in the PubMed/MEDLINE database, employing Medical Subject Heading (MeSH) terms and free text terms. MeSH terms included: ‘Appendiceal Neoplasms’, ‘Adenocarcinoma, Mucinous’, ‘Immunohistochemistry’, ‘Pathology, Molecular’, ‘Biomarkers, Tumor’, and ‘Genetic Techniques’. Free text terms included: “Low grade appendiceal mucinous”, “High grade appendiceal mucinous”, “Appendiceal mucinous adenocarcinoma”, “Immunohistochemistry”, “molecular”, “genetic”, “sequencing”, and “mutation” with keywords centered on those terms to avoid missing related articles. The search strategy developed was transferred to Scopus, Embase, and Web of Science databases using the Polyglot translator (Clark et al., 2020). The search included grey literature and was not restricted by language or time frame. The complete strategy for each database can be found in Elsayed-40-621-633-2025.pdf | Supplementary File . The resulting studies were transferred to EndNote X9, where duplicates were identified and removed. Eligibility criteria All studies with pathologically confirmed AMNs were included, provided they had information on IHC and/or molecular markers. Research articles were excluded from our review for one or more of the following reasons: (1) having a different outcome, (2) having no IHC/molecular data, (3) being a case report or review, (4) not assessing primary tumors, or (5) assessing treatment markers only. If more than one study used the same sample, only the article with more robust details or the most recent article was included. Study selection and screening The remaining articles were uploaded to the Rayyan platform for additional screening after our search approach had been applied to the databases we had chosen and the duplicates had been eliminated using EndNote X9 (Ouzzani et al., 2016). Two investigators 622 Biomarkers of appendiceal mucinous neoplasms reviewed the titles and abstracts independently, and any discrepancies were settled by consensus. The entire texts of the papers determined to be eligible were then acquired and independently double-screened, with any discrepancies being resolved through team discussion. Data extraction The following data were extracted from each study: last name of the first author, year of publication, country, sample size, tumor type, molecular findings tested, and proportions of positive findings, immunohistochemical findings tested, and proportion of positive findings. The level of positivity expression was categorized as present/absent, as characterized by each article. Two investigators reviewed and extracted data from qualified research independently. A third member was brought in when they could not reach an agreement. Quality of studies The Methodological Standards for Epidemiological Research (MASTER) scale was used to appraise the quality of the included studies (Stone et al., 2021). The MASTER tool provides objective quality evaluation across various analytic research designs by utilizing unified approaches for methodological quality assessment (Stone et al., 2021). This scale has 36 safeguard questions answered in binary form, with one point awarded if the safeguard is applied and zero if it is not. The 36 safeguards are divided into seven categories: equal recruitment, equal retention, equal ascertainment, equal implementation, equal prognosis, adequate analysis, and temporal precedence (Stone et al., 2021). Two investigators rated the quality of the qualified research independently. A third member was called in when they could not reach a consensus. Outcomes We had two primary outcomes. Firstly, the identification of different molecular and IHC marker expression and their proportions within AMN subtypes. Secondly, the identification of IHC and molecular markers that can differentiate between AMNs and OMNs or CRC. 623 Biomarkers of appendiceal mucinous neoplasms Fig. 1. PRISMA flowchart of the literature review process and selection. Data analysis All statistical analyses were performed using Stata 17. For categorical variables, we reported frequencies and percentages. To assess the association between specific markers and any two tumor types, we constructed 2x2 contingency tables. These tables allowed us to compare the presence or absence of each marker in AMNs, OMNs, and/or CRC. We employed the chi-squared test to determine whether there were statistically significant differences in marker prevalence between tumors. The chi-squared test assessed the independence of marker presence and tumor type. In cases where the expected values in any of the cells of the contingency tables were less than 5, Fisher's exact test was used instead of the chi-squared test. This was done to ensure the validity of the statistical analysis, mainly when dealing with small sample sizes or rare marker occurrences. We have reported exact p-values and adopted a significance level of 0.05. Results Study selection The search strategy began by identifying 726 articles from PubMed/MEDLINE/PMC, Embase, Scopus, and Web of Science Core Collection (Fig. 1). These articles were imported into EndNote, where 276 duplicates were removed. After transferring the remaining 450 articles to Rayyan, another 75 duplicate articles were manually identified and removed. This left 375 unique articles, which were then screened based on their title and abstract, resulting in the removal of 236 articles due to not complying with our inclusion criteria. Of the remaining 139 articles, 29 were not retrievable or with abstracts available only, leaving 110 for full-text screening. After review, 84 articles were excluded and one additional article was included manually. Ultimately, 27 articles were included in our final study set. Sixteen studies described markers of AMN subtypes, while eleven studies compared markers of AMNs, OMNs, and CRC. Omitted records and the reason for their exclusion are available in Elsayed-40-621-633-2025.pdf | Supplementary File . Study Characteristics and Data Collection Table 1 displays the characteristics and data collected from included studies, which span from 1997 to 2022, with the majority published originating from the USA (n=13). The mean age in the included studies ranged from 51.5 to 64.2 years, with one study reporting 624 Biomarkers of appendiceal mucinous neoplasms Table 1. Characteristics of studies included and data collected. Author Country Age (mean) Study design Tumor(s) (N) Aldaoud et al. (2019) Jordan N/A Cross-sectional LAMN (8); MAC (1); OMN (50); CRC (63) Chang et al. (2012) Korea 62.7 Retrospective cohort MA (22); MU (20); MAC (14) Chezar and Minoo (2022) Canada 58.2 Cross-sectional LAMN (18) Davison, Choudry, et al. (2014) USA 53 Retrospective cohort AJCCG1 (69); AJCCG2 (55) Davison, Hartman, et al. (2014) USA 53.5 Retrospective cohort AJCCG1 (42); AJCCG2 (47) Ekinci (2018) Turkey 60.27 Cross-sectional LAMN (14); MAC (13) Elias et al. (2014) USA N/A Retrospective cohort MAC (26); OMN (21); CRC (18) Gonzalez et al. (2022) USA 57 Cross-sectional HAMN (35) Hara et al. (2015) Japan 57.9 Retrospective cohort LAMN (11); MAC (5) Jian et al. (2022) China N/A Cross-sectional MAC (15) Li et al. (2017) USA N/A Cross-sectional AMN-U (40); OMN (18) Liao et al. (2020) USA 53 Retrospective cohort LAMN (8); HAMN (9); MAC (10) Liu et al. (2014) Lebanon N/A Cross-sectional LAMN (15); MAC (8) Moh et al. (2016) USA N/A Cross-sectional LAMN (6); OMN (111); CRC (251) Munari et al. (2021) Italy 71.95 (median) Cross-sectional LAMN (18); HAMN (3); MAC (9) Nishikawa et al. (2013) Japan 61.34 Cross-sectional LAMN (32); MAC (3); OMN (62); CRC (33) Ronnett et al. (1997) USA N/A Cross-sectional MA (13); OMN (11) Schmoeckel, Kirchner, and Mayr (2018) Germany 60 Cross-sectional LAMN (7); OMN (40) Singhi et al. (2014) USA 51.5 Retrospective cohort AJCCG1 (23); AJCCG2 (19) Stewart et al. (2014) USA 57 (median) Cross-sectional LAMN (16); OMN (6) Strickland and Parra-Herran (2016) Canada 55 Cross-sectional LAMN (25); MAC (7); OMN (40) Tokunaga et al. (2019) USA N/A Cross-sectional MAC (44); CRC (2074) Tsai et al. (2019) Taiwan 62.3 Cross-sectional LAMN (23); HAMN (5); MAC (3) Yanai et al. (2021) Japan 57.1 Retrospective cohort LAMN (34); HAMN (8); MAC (9) Yoon et al. (2009) Korea 64.2 Retrospective cohort MA (32); MU (23); MAC (15) Zauber et al. (2011) USA 56.7 Cross-sectional LAMN (31); OMN (56) Zhu et al. (2019) USA 53.2 Retrospective cohort AJCCG1 (21) ; AJCCG2 (21) LAMN, Low-grade appendiceal mucinous neoplasm; HAMN, High-grade appendiceal mucinous neoplasm; MAC, Mucinous adenocarcinoma; MA, Mucinous adenoma; MU, Appendiceal mucinous neoplasm of uncertain malignant potential; AJCCG1, G1 AJCC mucinous neoplasm; AJCCG2, G2 AJCC mucinous neoplasm; AMN-U, Unspecified appendiceal mucinous neoplasm; OMN, Ovarian mucinous neoplasm; CRC, Colorectal adenocarcinoma. a median age of 71.95. Studies utilized a cross-sectional (n=17) or a retrospective cohort design (n=10). The sample sizes for AMN subtypes ranged from 3 to 69, while those for differentiating AMN from OMNs and CRC ranged from 6 to 62 and 18 to 2074, respectively. Subtypes of AMNs included in the analysis were AJCC G1, AJCC G2, LAMN, HAMN, MAC, mucinous adenoma (MA), and mucinous neoplasm of uncertain malignant potential (MU). One study reported data for both right and left CRC, which was combined in the analysis due to a similar sample size and prevalence of markers reported in both groups (Tokunaga et al., 2019). Elsayed-40-621-633-2025.pdf | Supplementary File and Elsayed-40-621-633-2025.pdf | Supplementary File contain immunohistochemical and molecular markers extracted from each publication for studies comparing AMN subtypes and AMNs with OMNs and/or CRC. Quality assessment The studies included in our analysis exhibited varying degrees of adherence to safeguards within the MASTER scale, as indicated by the number of safeguards implemented, which ranged from 12 to 18 out of 36. None of the studies fulfilled sufficient safeguards regarding equal prognosis or temporal precedence, with the latter criterion not being met by any of the studies. In contrast, most studies demonstrated robust safeguards for equal implementation and sufficient analysis. However, there were discernible differences in the number of safeguards applied for formal recruitment, equal retention, and equal ascertainment domains. The specific safeguards utilized in each study are shown in Figure 2. Comprehensive responses to all quality assessment questions can be found in Elsayed-40-621-633-2025.pdf | Supplementary File . Appendiceal mucinous neoplasms Table 2 presents the combined prevalences and percentages of common markers observed in different 625 Biomarkers of appendiceal mucinous neoplasms Table 2. Comparison of immunohistochemical and molecular alterations between subtypes of appendiceal mucinous neoplasm. Tumor/Marker p53 lossi (+/N) p53 overexpressioni (+/N) Nuclear B-catenini (+/N) KRASm (+/N) GNASm (+/N) TP53m (+/N) RNF43m (+/N) LAMN 24% (11/45) 7% (3/45) 38% (18/48) 61% (66/109) 35% (32/91) 22% (14/63) 16% (9/57) HAMN 17% (3/18) 22% (4/18) NA 79% (26/33) 33% (10/30) 36% (12/33) 25% (4/16) MAC 14% (2/14) 43% (6/14) 33% (4/12) 66% (29/44) 19% (6/32) 34% (15/44) 39% (7/18) LAMN vs. MAC (p) 0.713 0.004* 1.000 0.536 0.084 0.174 0.037* LAMN vs. HAMN (p) 0.739 0.095 NA 0.055 0.855 0.139 0.463 HAMN vs. MAC (p) 1.000 0.267 NA 0.216 0.190 0.836 0.388 i, IHC; m, molecular; *, statistically significant (p<0.05); LAMN, Low-grade appendiceal mucinous neoplasm; HAMN, High-grade appendiceal mucinous neoplasm; MAC, Mucinous adenocarcinoma. Fig. 2. Quality assessment scores for studies included using the MASTER scale. AMN subtypes and p-values for comparisons between tumor types (Fig. 3). Detailed findings can be found in Elsayed-40-621-633-2025.pdf | Supplementary File . Upon reviewing the classification of AMNs, it became apparent that there was considerable uncertainty and variability in subclassifications (Maedler et al., 2018). This was evident in the reviewed articles, where some adhered to the WHO classification while others employed different criteria. WHO classification Some studies explored IHC markers within LAMN, HAMN, and MAC. β-catenin nuclear expression, p53 loss, and p53 overexpression were the most studied biomarkers. For β-catenin, nuclear expression was found to be similar in MAC (4/12, 33%) compared to LAMN (18/48, 38%), with no statistical significance (p=1.000). On the other hand, p53 overexpression was more common in MAC (6/14, 43%), followed by LAMN (3/45, 7%) and HAMN (4/18, 22%). The difference in p53 overexpression between LAMN and MAC was statistically significant (p=0.004). As for p53 loss, it was more frequently detected in LAMN (11/45, 24%) than in HAMN (3/18, 17%) and MAC (2/14, 14%), however, these variations were not statistically significant. Studies also explored molecular markers within LAMN, HAMN, and MAC. To begin with, in comparing these three entities, minimal to negligible disparities were noted in the prevalence of molecular alterations affecting essential genes such as BRAF, APC, SMAD4, CTNNB1, TP53, PIK3CA, and RB1. Nonetheless, some variability surfaced in the context of KRAS, GNAS, and RNF43. KRAS mutations, for instance, proved to be the most prevalent in LAMN, HAMN, and MAC, occurring in 66/109 (61%), 26/33 (79%), and 29/44 (66%) of cases, respectively. Only the comparison between LAMN and HAMN approached statistical significance for KRAS (p=0.055). Likewise, RNF43 mutations exhibited a higher frequency in MAC (7/18, 39%) and HAMN (4/16, 25%) than in LAMN (9/57, 16%), and this disparity yielded a statistically significant p-value of 0.037 in the comparison between LAMN and MAC only. GNAS mutations were more frequently detected in LAMN (32/91, 35%) and HAMN (10/30, 33%) in contrast to MAC (6/32, 19%). AJCC classification Four studies utilized the AJCC staging system to compare grade 1 (G1) and grade 2 (G2) AMNs. Like LAMN, HAMN, and MAC, KRAS and GNAS mutations were the most common. They were identified in 61/90 (67.8%) and 21/44 (47.7%) of G1 cases and 57/75 (76%) and 21/40 (52.5%) of G2 cases. However, when assessing their ability to differentiate between G1 and G2 tumors, they did not exhibit statistically significant differences (0.244 for KRAS and 0.662 for GNAS). Appendiceal versus non-appendiceal tumors Tables 3 and 4 present the combined prevalences and percentages of common markers observed in AMNs compared with OMNs and CRC, respectively. Detailed 626 Biomarkers of appendiceal mucinous neoplasms Table 3. Comparison of immunohistochemical and molecular alterations between appendiceal mucinous neoplasms and ovarian mucinous neoplasms. Tumor/Marker CK7i (+/N) CK20i (+/N) CDX2i (+/N) PAX8i (+/N) SATB2i (+/N) MUC2i (+/N) KRASm (+/N) GNASm (+/N) LAMN 32% (18/56) 80% (53/66) 95% (38/40) 0% (0/40) 93% (43/46) 96% (55/57) 97% (61/63) 50% (16/32) MAC 26% (9/34) 92% (48/52) 94% (32/34) 0% (0/34) 88% (7/8) 94% (34/36) 100% (3/3) 0% (0/3) OMN 92% (145/157) 42% (74/175) 37% (63/169) 45% (68/151) 4% (11/259) 16% (10/61) 50% (59/118) 3% (2/62) AMN vs. OMN (p) 0.000* 0.000* 0.000* 0.000* 0.000* 0.000* 0.000* 0.000* i,IHC; m, molecular; *, statistically significant (p<0.05); LAMN, Low-grade appendiceal mucinous neoplasm; MAC, Appendiceal mucinous adenocarcinoma; OMN, Ovarian mucinous neoplasm; AMN, Appendiceal mucinous neoplasm. Table 4. Comparison of immunohistochemical and molecular alterations between appendiceal mucinous neoplasms and colorectal adenocarcinomas. Marker/ Nuclear TOPO1i PTENi GNASm KRASm TP53m APCm PIK3CAm Tumor B-catenini (+/N) (+/N) (+/N) (+/N) (+/N) (+/N) (+/N) (+/N) LAMN NA NA NA 50% (16/32) 94% (30/32) NA NA NA MAC 8% (2/26) 70% (31/44) 89% (39/44) 23% (11/47) 66% (31/47) 57% (25/44) 16% (7/44) 7% (3/44) CRC 50% (9/18) 52% (1088/2074) 65% (1358/2074) 2% (34/2107 49% (1030/2107) 71% (1465/2074) 77% (1593/2074) 19% (403/2074) AMN vs CRC (p) 0.003 0.018 0.001 0.000 0.000 0.047 0.000 0.033 i, IHC; m, molecular; *, statistically significant (p<0.05); LAMN, Low-grade appendiceal mucinous neoplasm; MAC, Appendiceal mucinous adenocarcinoma; CRC, Colorectal adenocarcinoma; AMN, Appendiceal mucinous neoplasm. findings can be found in Elsayed-40-621-633-2025.pdf | Supplementary File . AMNs and OMNs Ten studies collected data on primary OMNs, seven also included data on LAMN, and four on MACs (Table 3, Fig. 4). Commonly tested IHC markers included CK7, CK20, CDX2, SATB2, MUC2, and PAX8. Specifically, CK20, CDX2, SATB2, and MUC2 mutations were more prevalent in AMNs compared with OMNs with statistical significance (p=0.000). CK20 demonstrated a markedly higher prevalence in LAMN (53/66, 80%) and MAC (48/52, 92%) compared with OMNs (74/175, 42%). Similarly, CDX2 showed notably higher prevalence in LAMN (38/40, 95%) and MAC (32/34, 627 Biomarkers of appendiceal mucinous neoplasms Fig. 3. Comparison of biomarkers across subtypes of appendiceal mucinous neoplasms. Fig. 4. Comparison of biomarkers across appendiceal and ovarian mucinous neoplasms. Fig. 5. Comparison of biomarkers across appendiceal mucinous neoplasms and colorectal adenocarcinomas. =0.000). Regarding CK7 expression, OMNs (145/157, 92%) exhibited a significantly higher expression compared with LAMN (18/56, 32%) and MAC (9/34, 26%). Similarly, OMNs (68/151, 45%) had PAX8 expression, compared with complete absence in LAMN (0/40) and MAC (0/34). 94%) than in OMNs (63/169, 37%). SATB2 was significantly higher in LAMN (43/46, 93%) and MAC (7/8, 88%) compared with OMNs (11/259, 4%). Also, MUC2 exhibited significantly higher prevalence in LAMN (55/57, 96%) and MAC (34/36, 94%) than in OMN (10/61, 16%). Conversely, CK7 and PAX8 were more prevalent in OMNs compared with AMNs, also with statistical significance (p Frequently examined molecular markers included KRAS and GNAS. KRAS mutations were highly prevalent in LAMN (61/63, 97%) but less frequent in OMN (59/118, 50%). GNAS mutations were more prevalent in LAMN (16/32, 50%) compared with OMN (2/62, 3%). AMNs and CRC Five studies aimed to differentiate AMNs from CRCs. Four focused on comparing MAC and CRC, while three specifically compared LAMN with CRC (Table 4, Fig. 5). Only two studies provided data on IHC markers. One study compared MAC to both right and left-sided CRC; for our analysis, we combined them into a single group. Several commonly tested IHC markers, including nuclear β-catenin, TOPO1, and PTEN, were statistically significant in differentiating between CRC and AMNs (p=0.003, 0.018, 0.001, respectively). Nuclear β-catenin expression exhibited a higher prevalence in CRC (9/18, 50%) compared with MAC (2/26, 8%). Conversely, TOPO1 exhibited a higher prevalence in MAC (31/44, 70%) compared with CRC (1088/2074, 52%). Similarly, PTEN had a higher prevalence in MAC (39/44, 89%) than in CRC (1358/2074, 65%). For molecular markers, GNAS, KRAS, TP53, APC, and PIK3CA were also statistically significant in differentiating between CRC and AMNs (p=0.000, 0.000, 0.047, 0.000, 0.033, respectively). GNAS mutations were more prevalent in MAC (11/47, 23%) compared with CRC (34/2107, 2%). Similarly, KRAS mutations were also more prevalent in MAC (31/47, 66%) than in CRC (1030/2107, 49%). However, APC mutations had a significantly higher prevalence in CRC (1593/2074, 77%) than in MAC (7/44, 16%). Similarly, PIK3CA was more frequently mutated in CRC (403/2074, 19%) than in MAC (3/44, 7%). TP53 mutations exhibited a slightly higher prevalence in CRC (1465/2074, 71%) compared with MAC (25/44, 57%). Discussion This systematic review identified 27 studies with AMNs of different grades analyzed for molecular and immunohistochemical alterations. The statistically significant biomarker differences are summarized in Table 5. Several limitations are associated with the evidence in this systematic review. Heterogeneity in study designs, patient populations, and methodologies introduces potential variability and bias. Some studies used different classification systems, which may not align with the current WHO classifica-tion. Quality assessments revealed discrepancies in safeguard implementation, affecting the overall reliability of the findings. Moreover, the review is based on published studies, possibly introducing publication bias. These limitations emphasize the need for further research with consistent methodologies to provide robust, up-to-date evidence for clinical practice. Our analyses of AMNs identified mutations in KRAS, GNAS, and RNF43, as well as IHC overexpression of p53 protein, as potential markers for diagnosis and subtyping. While KRAS and GNAS have been consistently reported in the literature, we also highlight RNF43 and p53 IHC overexpression as potential markers deserving further study. KRAS was the most common mutation in all subtypes of AMNs. Mutations in the KRAS gene, which produce a mutant protein called K-Ras, play a critical role in oncogenesis through aberrant signal transduction, cell cycle regulation disruption, tumor microenvironment modulation, and therapy resistance (Mitin et al., 2005; Roberts and Der, 2007; Ihle et al., 2012; Roberts and Stinchcombe, 2013; Wu et al., 2019). KRAS mutations were highly prevalent in HAMN specimens, with two studies revealing KRAS mutations in all HAMN specimens tested (Tsai et al., 2019; Liao et al., 2020). The second most common mutation within AMNs was GNAS, with LAMNs and HAMNs having higher 628 Biomarkers of appendiceal mucinous neoplasms Table 5. Summary of statistically significant biomarker comparisons across tumor types. Biomarker Comparison P-value Immunohistochemistry P53 overexpression MAC>LAMN 0.004 CK20 expression AMN>OMN <0.001 CDX2 expression AMN>OMN <0.001 SATB2 expression AMN>OMN <0.001 MUC2 expression AMN>OMN <0.001 CK7 expression OMN>AMN <0.001 PAX8 expression OMN>AMN <0.001 TOPO1 expression AMN>CRC 0.018 PTEN expression AMN>CRC 0.001 B-catenin expression CRC>AMN 0.003 Molecular RNF43 mutations MAC>LAMN 0.037 KRAS mutations AMN>OMN or CRC <0.001 GNAS mutations AMN>OMN or CRC <0.001 TP53 mutations CRC>AMN 0.047 APC mutations CRC>AMN <0.001 PIK3CA mutations CRC>AMN 0.033 LAMN, Low-grade appendiceal mucinous neoplasm; MAC, Appendiceal mucinous adenocarcinoma; AMN, Appendiceal mucinous neoplasm; OMN, Ovarian mucinous neoplasm; CRC, Colorectal adenocarcinoma. mutation prevalence than MACs. GNAS encodes the G alpha subunit in G protein signaling and cAMP production (Landis et al., 1989; Lyons et al., 1990). Mutations in GNAS have been linked to various gastrointestinal tumors (Furukawa et al., 2011; Wu et al., 2011; Yamada et al., 2012; Matsubara et al., 2013; Takano et al., 2014; Afolabi et al., 2022). In addition, in previous studies, the cAMP signaling pathway has been shown to induce mucin production in colonic epithelial cells, suggesting that GNAS mutations could be directly related to mucin production in AMNs (Laburthe et al., 1989; Hokari et al., 2005). The similar pattern of GNAS mutations in LAMNs and HAMNs compared to MACs supports the classification of LAMNs and HAMNs as related entities distinct from MACs. This is also supported by the high KRAS and GNAS co-mutation rates reported in LAMN and HAMN specimens compared with MACs (Liu et al., 2014; Tsai et al., 2019; Liao et al., 2020). RNF43 mutations were significantly more prevalent in HAMNs and MACs compared with LAMNs. The protein encoded by RNF43 is an E3 ubiquitin ligase that regulates the turnover of cell surface receptors, including Frizzled receptors, which are involved in Wnt signaling. Mutations in RNF43 and overactivation of the Wnt/β-catenin pathway could be implicated in the progression of LAMN to HAMN and MAC (Tsai et al., 2019; Munari et al., 2021; Yanai et al., 2021). Moreover, overexpression of p53 protein was higher in MAC when compared with LAMN. p53 is a transcription factor encoded by the TP53 gene, which serves as a tumor suppressor of the cell cycle and promoter of DNA repair or apoptosis during stress (Inoue et al., 2012). The TP53 gene is recognized as a key component in the human carcinogenesis pathway as it is mutated in almost 50% of all human cancers (Perri et al., 2016). In gastrointestinal cancers, IHC overexpression of the p53 protein has been linked to higher tumor grades and poorer patient prognosis (Starzynska et al., 1993; Melling et al., 2019; Kim et al., 2022; Zhang et al., 2023). This may explain why p53 protein expression is higher in MAC compared with LAMN. Our analysis of IHC markers in studies comparing AMNs and OMNs revealed CK7 and PAX8 as markers of OMNs and SATB2, CK20, CDX2, and MUC2 as markers of AMNs. CK7 and CK20 are members of a group of intermediate filaments, the cytokeratin family, found in the cytoskeleton of epithelial cells. Several studies have shown that CK7 is a good marker of OMNs (Kelemen and Köbel, 2011; Landau et al., 2014). CK20, on the other hand, is commonly expressed in mucinous neoplasms of the lower GI tract (Vang et al., 2006). PAX8 is a transcription factor involved in developing various tissues, including the female reproductive system, making it a specific marker of OMNs (Song et al., 2013). SATB2 is a transcriptional factor that influences gene expression through the modulation of chromatin architecture (Cai et al., 2006). It is involved in oncogenesis by regulating cell division, cell cycle progression, pluripotency, and stem cell renewal (Roy et al., 2020). In a previous study, the dual immunostaining of CK20 and SATB2 could distinguish OMNs from AMNs (Li et al., 2017). CDX2 is an intestine-specific nuclear transcription factor that regulates intestinal epithelium cell proliferation and differentiation (Werling et al., 2003; De Lott et al., 2005). Recent studies suggest that CDX2 is an oncogene in various cancers of the GI tract, including CRC, appendiceal, pancreatic, and gastric cancers (Yu et al., 2019). MUC2 is a major secreted mucin in the gastrointestinal system, and its elevated expression in AMNs is due to tumor cells producing excessive mucin (Van Klinken et al., 1999; O'Connell et al., 2002). When examining the molecular markers that differentiate AMNs from OMNs, we found that KRAS and GNAS mutations were the most useful for AMNs. The analysis of IHC markers in AMNs and CRC showed β-catenin to be the most specific marker of CRC compared with MACs. β-catenin primarily mediates the Wnt signaling pathway and enhances cell-cell adhesion, thereby contributing to the stability of nuclear proteins involved in transcriptional regulation (Cheng et al., 2019). In CRC, the Wnt/β-catenin signaling pathway was found to be associated with tumor invasion and chemotherapy resistance. Notably, this signaling pathway is dysregulated in over 90% of CRC patients, underscoring its potential as a prominent diagnostic and therapeutic target (Cancer Genome Atlas, 2012; Yuan et al., 2020). The analysis of molecular markers differentiating CRC from AMNs showed GNAS to be the most specific marker of AMNs, whereas PIK3CA was the most specific for CRCs compared with MACs. PIK3CA (phosphatidylinositol-4,5-bisphosphate 3- kinase catalytic subunit alpha) is a crucial component of the PI3K pathway and activates downstream effectors such as AKT and mTOR. This pathway regulates various cellular processes, including cell proliferation, survival, and apoptosis. In CRC, PIK3CA mutations have been linked to worse clinical outcomes and poor response to targeted anti-EGFR monoclonal antibodies. However, they have also been found to be positive predictive biomarkers of longer survival using aspirin as adjuvant therapy (Kato et al., 2007; Sartore-Bianchi et al., 2009; Liao et al., 2012; Cathomas, 2014). The findings of this systematic review have important implications for the management of AMNs. In practical terms, the identified molecular and IHC biomarkers can aid in the accurate diagnosis, subtyping, and differentiation of AMNs from other mucinous neoplasms with similar clinical presentations. This agrees with previous literature and can be instrumental in guiding treatment decisions and surgical approaches. However, there is still a need for further research to validate these markers in larger and more diverse patient populations. Additionally, future studies should explore the genetic and molecular mechanisms underlying these 629 Biomarkers of appendiceal mucinous neoplasms markers and their potential as therapeutic targets, thereby advancing precision medicine in the context of AMNs. Conclusion This systematic review underscores the potential of molecular and IHC biomarkers in classifying AMN subtypes and differentiating them from OMNs and CRC, thereby improving diagnostic accuracy and management. It also highlights the importance of additional validation and research to further our understanding of mucinous neoplasms. Ethical Statement. An ethics statement is not applicable because this study is based exclusively on published literature. Declaration of Interest Statement. The authors declare no potential conflicts of interest concerning the research, authorship, and/or publication of this article. Funding Sources. The open-access publication of this study was granted by Qatar University. The funder had no role in the design, data collection, data analysis, and reporting of this study. Author Contributions. Conceptualization, R.A. and S.V.; Methodology, B.E., A.M.E., and M.E.; Software, B.E., A.M.E., and M.E.; Validation, R.A., S.V., B.E., A.M.E., and M.E.; Investigation, all authors; Resources, B.E., A.M.E., M.E., and R.A.; Funding acquisition, R.A.; Writing— original draft, all authors; Writing—review and editing, B.E., A.M.E., M.E., R.A., and S.V.; Supervision, R.A. and S.V.; Project administration, B.E., R.A., and S.V.; All authors have read and agreed to the submitted version of the manuscript. Data Availability Statement. 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