Survival and mutational analysis of small cell carcinoma in pan-cancer

Survival and mutational analysis of small cell carcinoma in pan-cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Survival and mutational analysis of small cell carcinoma in pan-cancer Chunqian Yang, Ting Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3914949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study aims to delve into the differences and commonalities among small cell carcinomas (SCC) originating from different sites, including extrapulmonary small cell carcinoma (EPSCC) and small cell lung carcinoma (SCLC). We focus on understanding the trends in incidence, genomic characteristics, and treatment strategies for these subtypes, addressing the gaps in our knowledge of these rare and heterogeneous diseases. Methods: A comprehensive approach was employed using data from Cosmic, SEER, and GDSC databases. Epidemiological data were obtained from the SEER database, genomic mutation information from the Cosmic database, and drug sensitivity data from the GDSC database. Statistical tests were applied to analyze the data, revealing epidemiological variations in SCC across different populations and regions and identifying genomic variations. Results: Analysis indicates a significant difference in the incidence rates of EPSCC and SCLC, with EPSCC currently accounting for 2% − 4% of all SCC diagnoses. Genomic analysis unveils both shared and unique mutational landscapes between these two subtypes, guiding future therapeutic strategies. Tailored treatment plans were formulated based on the site of origin, and analysis of the SEER database highlighted epidemiological variations in SCC, emphasizing key factors associated with survival rates. Conclusion: This study provides in-depth insights into the differences and commonalities among small cell carcinomas originating from different sites, offering crucial clues for precision treatment strategies. The rising incidence of EPSCC underscores its clinical significance. These findings not only expand our understanding of SCC biology but also have profound implications for improving clinical treatment outcomes for patients.. Small Cell Carcinoma Pan-Cancer Research Extrapulmonary Small Cell Carcinoma Genomic Mutation Drug Sensitivity Cosmic Database SEER Database GDSC Database Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The World Health Organization classifies neuroendocrine cancers into three categories: highly differentiated (true carcinoid), moderately differentiated (atypical carcinoid), and poorly differentiated tumors. Small cell carcinoma is considered to be a poorly differentiated neuroendocrine cancer.[ 1 , 2 ] Small cell carcinomas, predominantly originating in the pulmonary system, have been increasingly documented in various non-pulmonary sites as medical research advances. In the realm of oncology, small cell carcinomas are most frequently observed originating within the pulmonary system. However, with the continuous evolution of medical research, the incidence of extrapulmonary small cell carcinomas (EPSCC) has been increasingly reported.[ 3 ] These EPSCC cases have been identified in a variety of organs, and they represent approximately 2–4% of the total small cell carcinoma diagnoses, as indicated by recent studies. [ 4 , 5 ] In the largest documented series of 1618 extrapulmonary small cell carcinoma (EPSCC) cases, the esophagus is most frequently affected (18%), followed by other gastrointestinal sites (15%), the genitourinary system (20%), head and neck (11%), and breast (10%).[ 6 ] Owing to the rarity and varied presentation of EPSCC, limited data from randomized trials exist, leading to a lack of consensus regarding effective treatment. Current recommendations are based on retrospective studies and experiences with small-scale, single-institution treatments.[ 7 – 9 ] In fact, EPSCC is distinct from both small cell lung carcinoma (SCLC) and the common malignancies of its respective sites of occurrence, possessing unique clinical and pathological characteristics. Unfortunately, due to its rarity in clinical settings, no prospective clinical studies have been conducted, thus precluding the provision of high-level evidence-based medical guidance. To delineate the distinctions between EPSCC and SCLC and to broaden the clinical understanding of this relatively rare tumor for enhanced therapeutic guidance, this investigation compiled and analyzed extant genomic mutation data of small cell carcinoma from the Cosmic database. Our analysis focused on identifying genomic variances and congruities in small cell carcinomas emanating from diverse anatomical origins. The objective is to elucidate the differential characteristics of small cell carcinomas contingent upon their site of origin. Furthermore, this study integrates these genomic variations with relevant GDSC drug sensitivity data, thereby facilitating the formulation of personalized treatment strategies for small cell carcinoma based on their primary location. Materials and Methods Genomic mutation data were meticulously collated from the Cosmic database, employing CosmicMutantExport and CosmicSample datasets. Targeted retrieval was executed using the keyword 'Small_cell_carcinoma' to filter pertinent data pertaining to small cell carcinoma. This endeavor resulted in the acquisition of mutational profiles for 2,223 lung, 148 bladder, 126 prostate, 102 ovarian, 84 uterine, 40 esophageal, 36 bile duct, and 10 thymic small cell carcinoma cases. Furthermore, an in-depth genomic exploration was conducted on 87 patient samples from our medical institution via Whole Exome Sequencing (WES). Subsequent mutation spectrum analyses were rigorously performed utilizing R studio. Concomitantly, comprehensive patient data spanning from 2001 to 2018 were extracted from the SEER database, adhering to the specific coding criteria designated for small cell carcinoma. The delineation of primary tumor sites was based on the SEER database’s explicit annotations. The inclusion criteria were meticulously defined, excluding non-primary tumors and cases lacking complete follow-up information. This stringent selection process culminated in the inclusion of 53,806 intrapulmonary and 8,674 extrapulmonary small cell carcinoma cases. The data collation adhered scrupulously to the coding and staging guidelines outlined in the SEER database manual. The methodology encompassed in this research, alongside the data collection paradigm, is systematically illustrated in the ensuing schematic representation(Fig. 1). Statistical Analysis The comprehensive bioinformatic analyses were executed utilizing R version 3.6.3. For delineating the genetic mutation landscape, the 'maftools' package in R was employed, which facilitated the generation of mutation landscape plots that elucidate the genomic alterations across various genes[ 10 ]. Visualization of drug sensitivity data, particularly highlighting the potential drug susceptibilities in small cell carcinomas of diverse primary origins, was accomplished using the 'ggplot2' package, known for its robust graphical capabilities[ 11 ]. The 'forest' package was adeptly applied to construct forest plots for univariate Cox proportional hazards regression analysis, offering insights into variable-specific hazard ratios. Survival curves and their respective statistical interpretations were elegantly presented through the utilization of the 'survival' and 'survminer' packages in R, ensuring a comprehensive and visually appealing survival analysis.[ 12 ] [ 13 ]Significance in the context of statistical testing was determined by a threshold p-value of less than 0.05, adhering to the convention of using two-tailed tests for all statistical inferences. Results Higher Mutation Frequency of TP53, RB1, and TTN in Intrapulmonary Small Cell Carcinoma and Diverse Mutational Landscapes Across Ethnicities The overall mutation pattern in small cell carcinoma predominantly comprises missense mutations. Specifically, mutations in TP53, RB1, and TTN were observed at frequencies of 53%, 31%, and 17%, respectively, in small cell carcinoma. Notably, the replacement of Cytosine (C) with Adenine (A) and the substitution of Cytosine (C) with Thymine (T) were significantly more prevalent than other types of mutations. This mutation pattern may be associated with exposure to specific carcinogens, such as ultraviolet radiation and chemical carcinogens[ 14 ]. Due to the large volume of intrapulmonary samples, there is a potential dilution effect on the mutational characteristics of extrapulmonary small cell carcinoma. Therefore, the study subdivided small cell carcinoma into each primary site for corresponding mutation analysis. It was found that intrapulmonary small cell carcinoma exhibited high-frequency mutations in TP53 and RB1. The top 10 genes with the highest mutation frequency in intrapulmonary cases were TP53 (54%), RB1 (30%), TTN (20%), LRP1B (14%), CSMD3 (13%), USH2A (12%), RYR2 (12%), MUC16 (11%), ZFHX4 (11%), and KMT2D (10%), as shown in Fig. 2A. These mutations predominantly cluster in pathways related to genomic integrity, cell cycle, and RTK signaling. Local data from patients with small cell lung carcinoma were also collected for similar mutational analysis (Fig. 2B), revealing that the top 10 mutated genes were TP53 (83%), TTN (72%), MUC16 (52%), RYR2 (45%), CSMD3 (43%), USH2A (43%), OBSCN (36%), HMCN1 (34%), and SYNE1 (34%), primarily impacting pathways associated with genomic integrity. Comparing our local data with the information from the Cosmic database, we observed potential ethnic variations in mutation patterns. Our local cohort from Zhujiang Hospital predominantly comprises Asian patients,[ 15 ] while the Cosmic database mainly contains data from Caucasian individuals. This indicates that genetic research should thoroughly consider ethnic differences. Diverse Mutation Spectra in Extrapulmonary Small Cell Carcinoma Across Different Primary Sites Extrapulmonary small cell carcinomas (EPSCCs) also exhibit high mutation frequencies in TP53 and RB1 genes. Notably, in EPSCC, the top 10 genes with the highest mutation frequencies include TP53 (52%), RB1 (34%), SMARCA4 (14%), PIK3CA (13%), FAT1 (12%), ERBB2 (10%), CREBBP (9%), KMT2A (9%), KMT2D (9%), and ARID1A (9%). Similar to intrapulmonary small cell carcinomas, these genes are predominantly involved in pathways related to genomic integrity and the cell cycle signaling. However, a distinguishing feature of EPSCC is the enrichment of high-frequency mutations in pathways associated with chromosomal alterations. The mutation profiles of small cell carcinomas originating from different primary sites are illustrated in Figs. 2C and 2D. Small Cell Carcinoma of the Bladder The top ten genes with the highest mutation frequencies in small cell carcinoma of the bladder are TP53 (80%), RB1 (69%), ARID1A (24%), FAT1 (24%), KMT2D (24%), ERBB2 (22%), CREBBP (20%), KDM6A (20%), KMT2A (16%), and PIK3CA (16%). Intriguingly, in bladder small cell carcinoma, mutations in the chromatin remodeling complex pathway account for 39% of the alterations. Small Cell Carcinoma of the Prostate In small cell carcinoma of the prostate, the top ten genes exhibiting the highest mutation frequencies are TP53 (45%), RB1 (29%), FOXA1 (26%), PIK3CD (26%), BRCA2 (19%), KMT2A (19%), GRIN2A (16%), APC (13%), ARID1B (13%), and CREBBP (13%). The three most significantly mutated pathways include genomic integrity (61%), cell cycle (29%), and transcription factor-related pathways (29%). Small Cell Carcinoma of the Biliary Tract For biliary tract small cell carcinoma, the genes exhibiting elevated mutation frequencies are distinctively TP53 (55%), TTN (45%), HMCN1 (35%), MUC4 (35%), VPS13D (35%), HRNR (30%), NACAD (30%), OBSCN (30%), PKD1L1 (30%), and ZNF208 (30%). The mutational landscape in these cases primarily revolves around pathways involving genomic integrity, transcriptional regulation, and chromatin remodeling complexes. Small Cell Carcinoma of the Uterus In uterine small cell carcinoma, the genes most frequently mutated include PIK3CA (34%), TP53 (23%), KRAS (20%), PIEZO2 (11%), ERBB4 (9%), FLNC (9%), GCN1 (9%), PLEC (9%), VRNP200 (9%), and ISC2 (9%). The mutation patterns are primarily concentrated in the PI3K pathway (43%), genomic integrity (32%), and the MAPK signaling pathway (25%). There appears to be a mutational exclusivity between PIK3CA and TP53 mutations as opposed to KRAS mutations, while the rest exhibit a tendency for co-occurrence. Small Cell Carcinoma of the Ovary Ovarian small cell carcinoma exhibits a relatively singular mutation pattern, with the top ten mutated genes identified as SMARCA4 (90%), ASXL1 (3%), JAK3 (3%), KMT2A (3%), MPL (3%), NOTCH2 (3%), PTPRT (3%), TP53 (3%), and WT1 (3%). The primary alterations in signaling pathways are observed in chromatin histone modification (3%), other aspects of chromatin (3%), and genomic integrity (3%). Small Cell Carcinoma of the Thymus The top ten mutated genes identified in thymic small cell carcinoma are ABHD2 (33%), YRX (33%), CDH26 (33%), CTNNB1 (33%), EEF2KMT (33%), NPIPB15 (33%), TP53 (33%), and ZNF814 (33%). In summary, the frequency of mutated genes varies depending on the primary site of origin. Cancer Treatment Strategy Analysis with Limited Samples: Differences Between Chemotherapy and Targeted Therapy Given the variation in mutation spectra across different primary sites of cancer, an individualized and targeted approach is imperative in devising cancer treatment strategies. Within our limited dataset, we gathered drug sensitivity data from two gastric small cell carcinoma cell lines, ECC10 and ECC12, and one cervical small cell carcinoma cell line, TC-YIK, from the GDSC database. Combining this with drug sensitivity information from five commonly used small cell carcinoma cell lines, we further analyzed the sensitivity of cells from different primary sites to existing conventional drugs (Fig. 3). We observed that in terms of chemotherapy: whether it be lung, gastric, or cervical small cell carcinoma cell lines, all exhibited resistance (high IC50) to DNA-damaging alkylating agents, cyclophosphamide (an alkylating agent), and cytarabine. The commonly used chemotherapy drug cisplatin might be more effective for lung small cell carcinoma, but it may not be the optimal choice for gastric or cervical small cell carcinomas. Notably, the cervical small cell carcinoma showed higher sensitivity to gemcitabine and Mitoxantrone compared to other cell lines. Regarding targeted therapy, drugs targeting the SMO and ERK/MAPK signaling pathways showed high IC50 values, while those targeting BCL2 apoptosis regulation and the EGFR signaling pathway had lower IC50 values, suggesting that drugs targeting BCL2 apoptosis regulation and the EGFR pathway could be more effective in treating small cell carcinoma. Among various targeted therapies, cervical small cell carcinoma seems to be more sensitive to Axitinib, Crizotinib, and Talazoparib. Compared to gastric small cell carcinoma, Osimertinib and Trametinib showed stronger responses in cervical small cell carcinoma. Furthermore, our analysis indicated a high frequency of mutations in the MAPK signaling pathway in colorectal small cell carcinoma, and GDSC drug sensitivity data suggested that MAPK pathway inhibitors are not very effective in targeted therapy of small cell carcinoma, whereas PI3K pathway inhibitors showed slightly better efficacy. Survival Analysis As elucidated previously, small cell carcinomas from varied primary origins manifest distinct mutational profiles. In the current era of rapidly advancing scientific technologies and the burgeoning field of multi-omics, a growing corpus of literature indicates that disparate mutational signatures may prognosticate divergent outcomes. In light of this, an extensive survival analysis was undertaken using the SEER database, encompassing the comprehensive categorization of small cell carcinoma. The dataset, spanning from 2000 to 2018, was rigorously examined. The findings delineate a consistent downward trajectory in incidence rates over the years. Moreover, a pronounced disparity in incidence rates was observed between genders, with males exhibiting a higher rate than females and a more precipitous decline. This phenomenon may be indicative of an increasing public health consciousness and alterations in lifestyle factors, exerting a salutary effect on cancer incidence. The collected clinical baseline data of the cohort is systematically tabulated (Table 1 ). Moreover, we have gathered data pertaining to the variations in the incidence rates of small cell lung cancer spanning the years 1980 to 2018. This dataset reveals an annual escalation in incidence rates prior to 1990. Post-1990, however, there is a noticeable decrement in the prevalence of small cell lung cancer. Notably, the incidence rates among males consistently surpass those in females.( Supplementary Fig. 3) Primary Sites of Small Cell Carcinoma: Predominantly in the Lung, Followed by the Urinary, Digestive, and Female Reproductive Systems Our analysis of data extracted from the SEER database, spanning 2000 to 2018, revealed a total of 122,218 small cell carcinoma cases. Notably, 53,806 of these were primary intrapulmonary small cell carcinomas, while 8,674 cases were classified as extrapulmonary (EPSCC). This data highlights a higher incidence of intrapulmonary small cell carcinoma compared to EPSCC. In the subset of EPSCC, the urinary system was the most prevalent primary site, succeeded by the digestive system and the female reproductive system, as illustrated in Supplementary Fig. 1.. A significant observation was the greater proportion of positive lymph nodes found in intrapulmonary small cell carcinoma patients compared to EPSCC, suggesting a less extensive metastatic pattern in EPSCC(Table 1 ). Demographic analysis indicated that small cell carcinoma affects more males than females, with a notably higher incidence among Caucasians. Intrapulmonary cases often presented with a larger number of positive lymph nodes, implying a higher tendency for metastasis to peripheral lymph nodes and a greater malignancy degree. Regarding treatment, chemotherapy alone or in combination with radiotherapy was the predominant approach. More than half of both intrapulmonary and extrapulmonary small cell carcinoma patients underwent surgery at the primary site, with a higher prevalence of surgery-only treatments observed in EPSCC patients. The majority of patients, irrespective of the carcinoma location, received external beam radiation therapy as part of their treatment regimen(Table 1 ). Table 1 Subject Respiratory system N(%) 115978 Digestive system N(%) 2069 Female Rep N(%) 1247 Head and neck region N(%) 344 Urinary System N(%) 2548 Endocrine system N(%) 32 Sex Female Male 57779 58199 901 1168 1247 0 94 250 454 2194 15 17 Age 80 2 481 27911 75247 12337 0 62 592 1071 344 27 307 425 418 70 0 9 89 175 71 0 14 359 1549 726 1 5 12 12 2 Marital status Divorced Married Separated Single Widowed Unmarried/Domestic Partner Unknown 15984 56910 1338 14723 21840 167 5016 221 1102 21 297 316 6 106 129 529 20 333 168 4 64 37 194 0 39 52 0 22 201 1608 31 282 404 5 117 2 22 0 4 3 0 1 Race Black White Other Unknown 9954 4487 4487 109 252 1661 152 4 189 916 137 5 16 314 14 0 178 2336 131 3 3 25 3 1 Tumor size ≤ 0.1cm 0.2 ~ 98.8cm > 98.8cm Unknown 136 12685 4349 98808 2 277 138 1652 1 173 69 1004 0 45 15 284 1 294 353 2000 0 10 0 22 Lymph nodes Positive Negative 9195 16090 1593 2958 764 4625 315 1505 470 6241 33 56 Therapy Surgery Radiation Chemotherapy Radiation + Chemotherapy Surgery + Radiation Surgery + Chemotherapy Surgery + Chemoradiotherapy Others 854 6678 33697 42511 110 916 1063 30149 182 62 607 375 3 164 107 569 112 50 149 262 15 287 218 154 32 13 36 110 31 17 73 32 647 55 252 156 104 759 382 293 2 4 8 5 0 4 1 10 Methods of radiotherapy External irradiation Internal exposure External + internal exposure Dilemma 49287 88 142 66461 539 3 0 1527 387 31 119 710 224 0 0 120 675 8 0 1965 9 0 0 23 Seer stage Localized Regional Distant Others 4328 18525 65339 27786 155 337 1077 500 200 332 410 305 45 124 85 90 885 511 845 407 5 3 14 10 Gender, Ethnicity, Surgical Status, Chemotherapy/Radiotherapy, SEER Stage, and Marital Status as Prognostic Factors for Overall Survival in Small Cell Carcinoma Patients Incorporating variables such as gender, ethnicity, and treatment modality, a comprehensive univariate and multivariate Cox proportional hazards model was developed (Fig. 4A). The analysis yielded statistically significant findings regarding the impact of gender on survival time, with male patients demonstrating poorer prognoses. Furthermore, the influence of ethnicity on survival time was significant, with Caucasian patients experiencing different outcomes compared to other ethnic groups. Surgical intervention at the primary site was associated with a reduced risk of death compared to those who did not undergo surgery. Both radiotherapy and chemotherapy significantly affected survival time. Interestingly, marital status also had a significant impact on survival time. Patients with partners showed better prognoses compared to those who were single or widowed, which may be attributed to the enhanced medical support provided by stable family relationships. Site- and Stage-Specific Prognoses in Small Cell Carcinoma: SEER Data Insights Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we amassed survival data for small cell carcinoma (SCLC) originating from diverse anatomical locations including the respiratory, digestive, endocrine, female reproductive, and urinary systems, as well as the head and neck region. Kaplan-Meier survival analysis yielded significant insights. Specifically, in localized SCLC, the most favorable prognosis was observed in neoplasms of the female reproductive system, followed sequentially by those in the head and neck region. Conversely, the respiratory and urinary systems demonstrated notably poorer prognoses. In instances of regional and distant SCLC, patients with neoplasms in the head and neck or female reproductive system manifested the most advantageous prognostic outcomes within our dataset. In stark contrast, SCLC of the respiratory system exhibited the most adverse prognosis, with urinary and digestive system SCLCs occupying an intermediate prognostic position (as depicted in Fig. 4C). To investigate the survival disparities across different primary sites of small cell carcinoma (SCC), this study conducted a detailed division of SCCs within each system and further analyzed survival data. The findings revealed significant prognostic variations even within the same system. For instance, within the reproductive system, breast cancer showed the most favorable prognosis, followed by ovarian and uterine cancers with intermediate outcomes, and vaginal small cell carcinoma with the least favorable prognosis. In the urinary system, bladder carcinoma exhibited the best prognosis, prostate carcinoma showed an intermediate prognosis, and renal small cell carcinoma had the least favorable outcomes. Contrary to the common belief that extrapulmonary small cell carcinomas have better prognoses, our research indicates that the prognosis of some primary sites may be better (e.g., esophagus, bile duct, intestine), worse (e.g., liver, pancreas, stomach), or similar to that of small cell lung cancer (SCLC) .( Supplementary Fig. 3). Aligned with extant literature and clinical paradigms, our analysis also corroborated that patients with distant metastases (categorized as 'DISTANT' in SEER staging) experienced the gravest prognosis, whereas those with localized manifestations of the disease had the most favorable outcomes (refer to Fig. 4D for SEER staging categories: Distant, Regional, Localized). Additionally, our findings suggest that an integrated approach of surgery coupled with radiotherapy and chemotherapy conferred a survival benefit across all stages.( Supplementary Fig. 2) Notably, patients who underwent primary site surgery exhibited a significantly enhanced prognosis compared to their non-surgical counterparts, likely attributable to earlier disease detection and intervention. Crucially, the amalgamation of local surgical excision, whether supplemented with systemic adjuvant radiotherapy and chemotherapy or not, appears to augment overall survival (OS), a notion gaining traction in contemporary clinical practice. Discussion Firstly, our analysis of cosmic mutation data revealed that high-frequency mutation genes vary across different primary sites. Regardless of being extrapulmonary or EPSCC, there is a higher incidence of TP53 and RB1 mutations, consistent with previous studies. For instance, research by Nikolas G. Balanis et al. indicated that the loss and/or inactivating mutations of TP53 and RB1 are enriched in prostatic neuroendocrine carcinoma. [ 16 ]Similar gene alterations in TP53 and RB1 are observed in small cell carcinoma of the esophagus, bladder,[ 17 ] and prostate. Notably, TP53 and RB1 are well-known tumor suppressor genes. TP53 plays a pivotal role in maintaining genomic stability, inhibiting cell proliferation, and inducing apoptosis in damaged cells. Mutations or inactivation of TP53 can lead to defective p53 function, impairing the cell's appropriate response to DNA damage and increasing susceptibility to cancer development. [ 18 ] The RB1 gene, encoding the pRB protein, regulates cell cycle progression to inhibit cell proliferation. Mutations or inactivation of RB1 can cause aberrations in the cell cycle, leading to uncontrolled cell proliferation and promoting cancer cell overgrowth.[ 19 ] The frequent mutations in these two genes may result in excessive cell proliferation, leading to an imbalance in the nuclear-cytoplasmic ratio, which could explain why most cells in small cell carcinoma tissues have an increased nucleus-to-cytoplasm ratio without forming distinct structures. Our study demonstrates that high-frequency mutations in bladder, lung, and prostate small cell carcinomas involve cell cycle signaling pathways. Chemotherapy, typically more toxic to rapidly proliferating cancer cells, could partly explain its efficacy in treating small cell carcinomas.[ 20 , 21 ] Additionally, we discovered high-frequency mutations in PIK3CA and PIK3CD in prostate and uterine small cell carcinomas, suggesting a potential role for PI3K inhibitors in their treatment. However, our further GDSC drug sensitivity analysis did not show a therapeutic advantage of PI3K inhibitors in cervical small cell carcinoma, indicating a need for more samples for validation. Furthermore, our research indicates that, irrespective of being extrapulmonary or pulmonary, small cell carcinomas have a higher prevalence in male patients. Previous reports have suggested that estrogen may act as a protective factor, reducing the risk of lung, colorectal, bladder, stomach, kidney, and pancreatic tumors, which might imply its protective role in small cell carcinoma patients as well[ 22 – 24 ]. Moreover, our analysis suggests that Caucasians have a worse prognosis in small cell carcinoma compared to other ethnicities. However, this contrasts with previous reports of a higher risk of advanced cancer in Black individuals than in Caucasians,[ 25 – 27 ] necessitating further research to understand the underlying reasons for these ethnic disparities in small cell carcinoma prognosis. In the realm of our detailed mutation analysis, several critical insights emerged. Predominantly, it was observed that small cell lung carcinomas are characterized by an elevated frequency of mutations in areas crucial for genomic integrity, cell cycle regulation, and PTK signaling pathways. Turning our attention to EPSCC (Extrapulmonary Small Cell Carcinoma), there is a noteworthy observation of a substantial mutation prevalence in genes such as TB53, RB1, and TTN. This finding becomes particularly interesting when juxtaposed with small cell lung carcinomas. Here, EPSCC demonstrated a slightly augmented mutation frequency in SMAECA4, PI3KCA, FAT1, ERBB2, CREBBP, KMT2A, KMT2D, ARID1A, and KDM6A. It merits special attention that the mutation rate of TTN in pulmonary small cell carcinoma reaches as high as 20%, whereas in EPSCC, this rate declines to just 7%, with a considerable majority of these mutations predominantly found in bile duct small cell carcinoma, where the TTN mutation frequency soars to a remarkable 45%. Echoing the mutation profile of small cell lung carcinoma, the mutational signaling pathways in EPSCC were primarily concentrated on genomic integrity and the cell cycle. Yet, a significant and intriguing divergence is noted in EPSCC, which exhibits more pronounced mutations in chromosomal signaling pathways, marking a distinct contrast from its pulmonary counterpart. Small cell lung carcinoma exhibits a high mutation frequency in genomic integrity, cell cycle, and PTK signaling pathways. In EPSCC, high mutation rates of TB53, RB1, TTN are observed, but compared to small cell lung carcinoma, SMAECA4, PI3KCA, FAT1, ERBB2, CREBBP, KMT2A, KMT2D, ARID1A, KDM6A show slightly higher mutation frequencies in EPSCC. TTN mutations, accounting for 20% in small cell lung carcinoma, drop to 7% in EPSCC, with the majority concentrated in bile duct small cell carcinoma, reaching up to 45%. Like small cell lung carcinoma, EPSCC mutations are focused on genomic integrity and the cell cycle, but differ in the prominence of chromosomal signaling pathway mutations. In recent years, immunotherapy has become a hotspot in cancer treatment. The 2018 IMpower133 study by Roche, the first clinical study where an immunotherapy checkpoint inhibitor (anti-PD-L1 atezolizumab combined with chemotherapy) was used in first-line treatment of SCLC, established the standard treatment in ES-SCLC at the 2018 WCLC conference. Subsequently, to further improve the survival of small cell lung carcinoma, new immunotherapy combination strategies are continuously being explored.[ 28 – 33 ] Due to the rarity of extrapulmonary small cell carcinoma, there are currently no clinical studies on immunotherapy for this cancer type. Recent reports suggest that co-mutations of TP53 and TTN in lung adenocarcinoma patients are associated with higher TMB levels and better immunotherapy response, potentially making them promising biomarkers for assessing immunotherapy effectiveness in lung adenocarcinoma.[ 34 ] A study on the use of Apatinib combined with Camrelizumab in patients with advanced melanoma, using an open-label, single-arm Phase 2 trial design, found that patients with TTN mutations had better progression-free survival (PFS) and overall survival (OS).[ 35 ] Our analysis also reveals that cancer types with a better prognosis than small cell lung carcinoma do not have high-frequency TTN mutations, while those with a worse prognosis, such as bile duct small cell carcinoma and small cell lung carcinoma, have high-frequency TTN mutations. Immunotherapy indeed extends the survival of small cell lung carcinoma patients, with samples containing TTN mutations showing significantly higher tumor mutation burdens, consistent with previous reports. A 2020 study also indicated that spontaneous mutations in the TTN gene represent a high tumor mutation burden, and the TTN mutation spectrum can serve as a predictive factor for MSI-H.[ 36 ] Considering these studies and findings, it is necessary to explore how small cell carcinoma with high-frequency TTN mutations responds to immunotherapy. For cancer types with a worse prognosis than small cell lung carcinoma, specific data on gene mutation conditions are not available, requiring more information for further analysis. In bladder small cell carcinoma, high-frequency mutations include an additional KMT2D gene compared to prostate small cell carcinoma. KMT2 genes might be necessary for repairing DNA damage caused by carcinogen exposure (such as excessive smoking). Cells with KMT2 gene mutations are unable to repair these DNA damages, leading to an accumulation of mutations in the genome. Researchers have identified KMT2C and KMT2D mutations as urgent biomarkers for guiding PARP inhibitor treatment in non-small cell lung carcinoma.[ 37 ] Combined with our earlier survival analysis, the prognosis of bladder small cell carcinoma appears better than prostate small cell carcinoma. The KMT2D mutation should be more detrimental to cancer cell development, indicating a worse prognosis. However, the prognosis might appear better due to earlier diagnosis and treatment of bladder cancer. Conclusion Through comprehensive data analysis and studies on drug sensitivity, we have unveiled the genetic characteristics and variations in drug response of small cell carcinoma across different primary sites. The drug sensitivity analysis indicates a general poor response of small cell carcinoma to alkylating agents, while targeted therapies focusing on the cell cycle, BCL2 apoptosis regulation, and the EGFR signaling pathway demonstrate greater potential. Specifically, in small cell carcinomas of the lung, stomach, and cervix, drugs targeting BCL2 and EGFR exhibited lower IC50 values, suggesting their suitability for treating these types of small cell carcinoma. Additionally, the high frequency of mutations in the MAPK signaling pathway in extrapulmonary small cell carcinoma suggests limited efficacy of targeted therapies for this cancer type. Regarding gene mutations, the high frequency of TP53 and RB1 mutations has been a hallmark of small cell carcinoma, potentially leading to excessive cell proliferation and cell cycle anomalies, thereby explaining the increased nucleus-to-cytoplasm ratio in small cell carcinoma cells. Moreover, mutations in TTN and KMT2D may involve potential targets for small cell carcinoma treatment. Mutations in TTN are associated with the response to immunotherapy and could serve as biomarkers for immunotherapy effectiveness, while KMT2D mutations may impact DNA damage repair capabilities, providing biomarkers for PARP inhibitor treatment. In conclusion, our observations of significant prognostic differences in small cell carcinoma across different ethnicities and primary sites underscore the necessity of personalized treatment. These in-depth analyses offer critical insights for the future precision treatment of small cell carcinoma, where specific targeted therapies and immunotherapies may become key areas in the management of small cell carcinoma. Limitations Despite certain advancements made in this study regarding Extrapulmonary Small Cell Carcinoma (EPSCC), there are several notable limitations. Firstly, the study inevitably faced the challenge of limited sample sizes for EPSCC, restricting our ability to conduct an in-depth investigation of this rare cancer type. The low incidence of EPSCC makes it difficult to gather a sufficiently large patient sample, which could impact the comprehensive understanding of the full spectrum of this tumor. Secondly, this study was not based on a singular database for holistic analysis but rather relied on data compiled from multiple databases. There are potential differences between these databases, including data collection methods and patient selection criteria, which might introduce heterogeneity and affect our accuracy in grasping the overall situation. Furthermore, we noted that the analysis for certain cancer types was limited by small sample sizes, constraining our capacity for a more profound investigation of these cancers. Larger-scale sample data are needed for these specific types of small cell carcinoma to more reliably reveal their unique characteristics and treatment responses. The lack of sufficient drug sensitivity data also represents a significant shortcoming of this study. Drug treatment impacts on tumors are a crucial aspect of therapeutic research; however, the absence of adequate drug sensitivity data means that our understanding of different types of small cell carcinoma responses to specific treatment regimens is incomplete. Additionally, our research did not conduct a thorough differential analysis of other aspects of the genome, nor did it delve into analyses based on mRNA levels. These areas represent directions for further research to more comprehensively and profoundly understand the pathobiological mechanisms of EPSCC. In summary, although this study has achieved some preliminary results, more extensive and in-depth research is required to address these limitations and advance our understanding and treatment capabilities for EPSCC. Declarations Author Contributions Statement Yang Chunqian (Y Chunqian): Responsible for the research design, data collection, data analysis, and manuscript writing. As the first author, Yang Chunqian participated in all stages of the research, from conceptualization to the preparation of the final manuscript. Wei Ting (W Ting): Provided guidance on the research direction, was responsible for obtaining funding for the project, reviewed the manuscript, and approved the final version. As the corresponding author, Wei Ting played a key role in the design of the study, interpretation of the results, and ensuring the accuracy of the data. Ethical Approval and Consent to Participate: Not applicable. Our study did not involve human participants, human data, or human tissue, and therefore did not require ethical approval. Consent for Publication: Not applicable. This manuscript does not contain any personal data in any form. Availability of Data and Materials: The data supporting the findings of this study are available from the corresponding author upon reasonable request. The authors are committed to ensuring the availability of data in accordance with the principles of data sharing. Competing Interests: The authors declare that they have no competing interests. This statement is made to confirm the absence of any financial, personal, or professional conflict that could be construed to influence the work reported in this manuscript. Funding: This research was supported by the National Natural Science Foundation of China (General Program; Key Program; Major Program), grant number 81772457. The funding body supported the design of the study, analysis, and interpretation of data, and in writing the manuscript. References Thompson L: World Health Organization classification of tumours: pathology and genetics of head and neck tumours . Ear, nose, & throat journal 2006, 85 (2):74. Solcia E, Klöppel G, Sobin LH: Histological typing of endocrine tumours : Springer Science & Business Media; 2012. Garrow GC, Greco FA, Hainsworth JD: Poorly differentiated neuroendocrine carcinoma of unknown primary tumor site . Semin Oncol 1993, 20 (3):287-291. Levenson RM, Ihde DC, Matthews MJ, Cohen MH, Gazdar AF, Bunn PA, Minna JD: Small cell carcinoma presenting as an extrapulmonary neoplasm: sites of origin and response to chemotherapy . J Natl Cancer Inst 1981, 67 (3):607-612. Remick SC, Ruckdeschel JC: Extrapulmonary and pulmonary small-cell carcinoma: tumor biology, therapy, and outcome . Med Pediatr Oncol 1992, 20 (2):89-99. Wong YNS, Jack RH, Mak V, Henrik M, Davies EA: The epidemiology and survival of extrapulmonary small cell carcinoma in South East England, 1970-2004 . BMC Cancer 2009, 9 :209. Sengoz M, Abacioglu U, Salepci T, Eren F, Yumuk F, Turhal S: Extrapulmonary small cell carcinoma: multimodality treatment results . Tumori 2003, 89 (3):274-277. Lin Y-L, Chung C-Y, Chang C-S, Wu J-S, Kuo K-T, Kuo S-H, Cheng A-L: Prognostic factors in extrapulmonary small cell carcinomas. A large retrospective study . Oncology 2007, 72 (3-4):181-187. Brennan SM, Gregory DL, Stillie A, Herschtal A, Mac Manus M, Ball DL: Should extrapulmonary small cell cancer be managed like small cell lung cancer? Cancer 2010, 116 (4):888-895. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP: Maftools: efficient and comprehensive analysis of somatic variants in cancer . Genome research 2018, 28 (11):1747-1756. Wilkinson L: ggplot2: Elegant Graphics for Data Analysis by WICKHAM, H . Biometrics 2011, 67 (2):678-679. Therneau TM, Grambsch PM: Modeling Survival Data: Extending the Cox Model : Modeling Survival Data: Extending the Cox Model; 2013. Kassambara A: Drawing Survival Curves using 'ggplot2' [R package survminer version 0.2.0] . 2017. Poon SL, McPherson JR, Tan P, Teh BT, Rozen SG: Mutation signatures of carcinogen exposure: genome-wide detection and new opportunities for cancer prevention . Genome Medicine 2014, 6 (3):24. Yao Z, Lin A, Yi Y, Shen W, Zhang J, Luo P: THSD7B Mutation Induces Platinum Resistance in Small Cell Lung Cancer Patients . Drug design, development and therapy 2022, 16 :1679-1695. Balanis NG, Sheu KM, Esedebe FN, Patel SJ, Smith BA, Park JW, Alhani S, Gomperts BN, Huang J, Witte ON et al : Pan-cancer Convergence to a Small-Cell Neuroendocrine Phenotype that Shares Susceptibilities with Hematological Malignancies . Cancer cell 2019, 36 (1):17-34.e17. Chang MT, Penson A, Desai NB, Socci ND, Shen R, Seshan VE, Kundra R, Abeshouse A, Viale A, Cha EK et al : Small-Cell Carcinomas of the Bladder and Lung Are Characterized by a Convergent but Distinct Pathogenesis . Clinical cancer research : an official journal of the American Association for Cancer Research 2018, 24 (8):1965-1973. Levine AJ, Oren M: The first 30 years of p53: growing ever more complex . Nature reviews Cancer 2009, 9 (10):749-758. Sherr CJ: Principles of tumor suppression . Cell 2004, 116 (2):235-246. Lee SS, Lee JL, Ryu MH, Chang HM, Kim TW, Kim WK, Lee JS, Jang SJ, Khang SK, Kang YK: Extrapulmonary small cell carcinoma: single center experience with 61 patients . Acta oncologica (Stockholm, Sweden) 2007, 46 (6):846-851. Zaffuto E, Pompe R, Zanaty M, Bondarenko HD, Leyh-Bannurah SR, Moschini M, Dell'Oglio P, Gandaglia G, Fossati N, Stabile A et al : Contemporary Incidence and Cancer Control Outcomes of Primary Neuroendocrine Prostate Cancer: A SEER Database Analysis . Clinical genitourinary cancer 2017, 15 (5):e793-e800. Chen C, Gong X, Yang X, Shang X, Du Q, Liao Q, Xie R, Chen Y, Xu J: The roles of estrogen and estrogen receptors in gastrointestinal disease . Oncology letters 2019, 18 (6):5673-5680. Al-Khyatt W, Tufarelli C, Khan R, Iftikhar SY: Selective oestrogen receptor antagonists inhibit oesophageal cancer cell proliferation in vitro . BMC Cancer 2018, 18 (1):121. Costa AR, Lança de Oliveira M, Cruz I, Gonçalves I, Cascalheira JF, Santos CRA: The Sex Bias of Cancer . Trends in endocrinology and metabolism: TEM 2020, 31 (10):785-799. Erhunmwunsee L, Joshi MB, Conlon DH, Harpole DH, Jr.: Neighborhood-level socioeconomic determinants impact outcomes in nonsmall cell lung cancer patients in the Southeastern United States . Cancer 2012, 118 (20):5117-5123. Ebner PJ, Ding L, Kim AW, Atay SM, Yao MJ, Toubat O, McFadden PM, Balekian AA, David EA: The Effect of Socioeconomic Status on Treatment and Mortality in Non-Small Cell Lung Cancer Patients . The Annals of thoracic surgery 2020, 109 (1):225-232. Tannenbaum SL, Koru-Sengul T, Zhao W, Miao F, Byrne MM: Survival disparities in non-small cell lung cancer by race, ethnicity, and socioeconomic status . Cancer journal (Sudbury, Mass) 2014, 20 (4):237-245. Bavetsias V, Linardopoulos S: Aurora Kinase Inhibitors: Current Status and Outlook . Frontiers in oncology 2015, 5 :278. Rolfo C, Russo A: In search of lost biomarker for immunotherapy in small-cell lung cancer . Clinical cancer research : an official journal of the American Association for Cancer Research 2023. Ahn MJ, Cho BC, Felip E, Korantzis I, Ohashi K, Majem M, Juan-Vidal O, Handzhiev S, Izumi H, Lee JS et al : Tarlatamab for Patients with Previously Treated Small-Cell Lung Cancer . The New England journal of medicine 2023, 389 (22):2063-2075. Cheng Y, Han L, Wu L, Chen J, Sun H, Wen G, Ji Y, Dvorkin M, Shi J, Pan Z et al : Effect of First-Line Serplulimab vs Placebo Added to Chemotherapy on Survival in Patients With Extensive-Stage Small Cell Lung Cancer: The ASTRUM-005 Randomized Clinical Trial . Jama 2022, 328 (12):1223-1232. Wang J, Zhou C, Yao W, Wang Q, Min X, Chen G, Xu X, Li X, Xu F, Fang Y et al : Adebrelimab or placebo plus carboplatin and etoposide as first-line treatment for extensive-stage small-cell lung cancer (CAPSTONE-1): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial . The Lancet Oncology 2022, 23 (6):739-747. Rudin CM, Balli D, Lai WV, Richards AL, Nguyen E, Egger JV, Choudhury NJ, Sen T, Chow A, Poirier JT et al : Clinical Benefit From Immunotherapy in Patients With SCLC Is Associated With Tumor Capacity for Antigen Presentation . Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2023, 18 (9):1222-1232. Ying K, Zou L, Wang D, Wang R, Qian J: Co-mutation of TP53 and TTN is Correlated with the Efficacy of Immunotherapy in Lung Squamous Cell Carcinoma . Combinatorial chemistry & high throughput screening 2023. Wang X, Wu X, Yang Y, Xu W, Tian H, Lian B, Chi Z, Si L, Sheng X, Kong Y et al : Apatinib combined with camrelizumab in advanced acral melanoma patients: An open-label, single-arm phase 2 trial . European journal of cancer (Oxford, England : 1990) 2023, 182 :57-65. Oh JH, Jang SJ, Kim J, Sohn I, Lee JY, Cho EJ, Chun SM, Sung CO: Spontaneous mutations in the single TTN gene represent high tumor mutation burden . NPJ genomic medicine 2020, 5 :33. Chang A, Liu L, Ashby JM, Wu D, Chen Y, O'Neill SS, Huang S, Wang J, Wang G, Cheng D et al : Recruitment of KMT2C/MLL3 to DNA Damage Sites Mediates DNA Damage Responses and Regulates PARP Inhibitor Sensitivity in Cancer . Cancer research 2021, 81 (12):3358-3373. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3914949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271804388,"identity":"8e1252a5-1d1c-440a-84f1-1d6879f572c5","order_by":0,"name":"Chunqian Yang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunqian","middleName":"","lastName":"Yang","suffix":""},{"id":271804389,"identity":"169178de-97c8-4f9e-90df-46d55caad338","order_by":1,"name":"Ting Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfvnDBw7//ffPjrG9gUgtkjPYEh/wsB1IZu45QKQWgxs8xgZALYztMxKIddnttjQJCZ47zLwzH2+8wVBjE01QB+Ocw8ckDCSe8UnOTiu2YDiWlttASAszQ1qaRIIBM7Ph7BwzCcaGw4S1sDEAVR5IYGbcf/MMkVp4JHKMDRsOHGZsnMFDpBYJnmOJjxkb0pIZe4B+SSDGL/bHm4FWNNgAo/LwxhsfamwIa0EGBhIJpCiHaCFVxygYBaNgFIwMAAD5qEHeMDWx+QAAAABJRU5ErkJggg==","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-01-31 19:49:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3914949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3914949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51024968,"identity":"8edd09f1-7632-4b12-b162-f70481b6ce0e","added_by":"auto","created_at":"2024-02-12 21:41:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":769240,"visible":true,"origin":"","legend":"\u003cp\u003eThe Entire Research Design and Data Collection Process\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3914949/v1/bb207ef15b746c5e12b41bda.jpg"},{"id":51024967,"identity":"bbab0287-dced-48ce-971c-2b77e351253a","added_by":"auto","created_at":"2024-02-12 21:41:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1978214,"visible":true,"origin":"","legend":"\u003cp\u003eMutation Profiles in Small Cell Lung Cancer\u003c/p\u003e\n\u003cp\u003eA: High-frequency mutations in small cell lung cancer as reported in the COSMIC database, featuring TP53 (54%), RB1 (30%), TTN (20%), LRP1B (14%), CSMD3 (13%), USH2A (12%), RYR2 (12%), MUC16 (11%), ZFHX4 (11%), KMT2D (10%).\u003c/p\u003e\n\u003cp\u003eB: Prevalent mutations identified in the small cell lung cancer cohort from Pearl River Hospital, showing TP53 (83%), TTN (72%), MUC16 (52%), RYR2 (45%), CSMD3 (43%), USH2A (43%), OBSCN (36%), HMCN1 (34%), SYNE1 (34%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC: Variation in the mutation landscape of small cell lung cancer based on the primary site, highlighting different high-frequency genes and signaling pathways.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3914949/v1/3f23681d41d5f063c1932ab5.jpg"},{"id":51024970,"identity":"2c1e4ab4-a75c-4f20-a16e-161626321456","added_by":"auto","created_at":"2024-02-12 21:41:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1160598,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of Small Cell Carcinoma to Chemotherapy and Targeted Drugs\u003c/p\u003e\n\u003cp\u003eA: Small cell carcinoma demonstrates resistance to alkylating agents including cyclophosphamide and cytarabine. Cisplatin, a commonly used chemotherapy drug, shows better efficacy in small cell lung cancer but limited effectiveness in gastric and cervical small cell carcinomas. Cervical small cell carcinoma exhibits increased sensitivity to gemcitabine and Mitoxantrone (an antimetabolite of cytarabine) compared to other cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: Small cell carcinoma shows resistance to targeted therapies affecting SMO and ERK/MAPK pathways. In contrast, drugs targeting BCL2 apoptosis regulation and EGFR signaling pathways display lower IC50 values, indicating potential higher efficacy in small cell carcinoma treatment. Note: ECC10 and ECC12 represent gastric small cell carcinomas, TC-YIK is a cervical small cell carcinoma, and the remaining samples are small cell lung carcinomas.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3914949/v1/e2459def4fdb07a20f620923.jpg"},{"id":51024971,"identity":"ceae29fa-7831-4477-8192-88847aedbd34","added_by":"auto","created_at":"2024-02-12 21:41:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2761365,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Prognostic Factors and Survival Outcomes in Small Cell Carcinoma\u003c/p\u003e\n\u003cp\u003eA/B: Multivariable and univariable regression analyses identify gender, ethnicity, surgical status, chemotherapy/radiotherapy, SEER stage, and marital status as significant prognostic factors influencing overall survival (OS) in patients with small cell carcinoma.\u003c/p\u003e\n\u003cp\u003eC/D/E: Survival outcomes vary significantly based on the primary site of the carcinoma. Respiratory and endocrine systems show poorer survival prognosis, while small cell carcinoma of the head and neck region demonstrates a relatively better prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD: Prognosis varies across different stages of the disease.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3914949/v1/7a3e0457d1b2d569c80ccc5d.jpg"},{"id":51296927,"identity":"a2e8fee7-e3bc-47e3-8bb2-a05d18ea0aa6","added_by":"auto","created_at":"2024-02-19 05:02:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1893044,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3914949/v1/61627872-77f8-4a82-9211-f3cf20a478f8.pdf"},{"id":51024969,"identity":"2b4b36c7-187b-4a54-b7f6-a47e19191d3d","added_by":"auto","created_at":"2024-02-12 21:41:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":788284,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-3914949/v1/b7c3501736ef6bf21d3825f3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survival and mutational analysis of small cell carcinoma in pan-cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe World Health Organization classifies neuroendocrine cancers into three categories: highly differentiated (true carcinoid), moderately differentiated (atypical carcinoid), and poorly differentiated tumors. Small cell carcinoma is considered to be a poorly differentiated neuroendocrine cancer.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Small cell carcinomas, predominantly originating in the pulmonary system, have been increasingly documented in various non-pulmonary sites as medical research advances. In the realm of oncology, small cell carcinomas are most frequently observed originating within the pulmonary system. However, with the continuous evolution of medical research, the incidence of extrapulmonary small cell carcinomas (EPSCC) has been increasingly reported.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] These EPSCC cases have been identified in a variety of organs, and they represent approximately 2\u0026ndash;4% of the total small cell carcinoma diagnoses, as indicated by recent studies. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] In the largest documented series of 1618 extrapulmonary small cell carcinoma (EPSCC) cases, the esophagus is most frequently affected (18%), followed by other gastrointestinal sites (15%), the genitourinary system (20%), head and neck (11%), and breast (10%).[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOwing to the rarity and varied presentation of EPSCC, limited data from randomized trials exist, leading to a lack of consensus regarding effective treatment. Current recommendations are based on retrospective studies and experiences with small-scale, single-institution treatments.[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] In fact, EPSCC is distinct from both small cell lung carcinoma (SCLC) and the common malignancies of its respective sites of occurrence, possessing unique clinical and pathological characteristics. Unfortunately, due to its rarity in clinical settings, no prospective clinical studies have been conducted, thus precluding the provision of high-level evidence-based medical guidance.\u003c/p\u003e \u003cp\u003eTo delineate the distinctions between EPSCC and SCLC and to broaden the clinical understanding of this relatively rare tumor for enhanced therapeutic guidance, this investigation compiled and analyzed extant genomic mutation data of small cell carcinoma from the Cosmic database. Our analysis focused on identifying genomic variances and congruities in small cell carcinomas emanating from diverse anatomical origins. The objective is to elucidate the differential characteristics of small cell carcinomas contingent upon their site of origin. Furthermore, this study integrates these genomic variations with relevant GDSC drug sensitivity data, thereby facilitating the formulation of personalized treatment strategies for small cell carcinoma based on their primary location.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eGenomic mutation data were meticulously collated from the Cosmic database, employing CosmicMutantExport and CosmicSample datasets. Targeted retrieval was executed using the keyword \u0026apos;Small_cell_carcinoma\u0026apos; to filter pertinent data pertaining to small cell carcinoma. This endeavor resulted in the acquisition of mutational profiles for 2,223 lung, 148 bladder, 126 prostate, 102 ovarian, 84 uterine, 40 esophageal, 36 bile duct, and 10 thymic small cell carcinoma cases. Furthermore, an in-depth genomic exploration was conducted on 87 patient samples from our medical institution via Whole Exome Sequencing (WES). Subsequent mutation spectrum analyses were rigorously performed utilizing R studio.\u003c/p\u003e\n\u003cp\u003eConcomitantly, comprehensive patient data spanning from 2001 to 2018 were extracted from the SEER database, adhering to the specific coding criteria designated for small cell carcinoma. The delineation of primary tumor sites was based on the SEER database\u0026rsquo;s explicit annotations. The inclusion criteria were meticulously defined, excluding non-primary tumors and cases lacking complete follow-up information. This stringent selection process culminated in the inclusion of 53,806 intrapulmonary and 8,674 extrapulmonary small cell carcinoma cases. The data collation adhered scrupulously to the coding and staging guidelines outlined in the SEER database manual. The methodology encompassed in this research, alongside the data collection paradigm, is systematically illustrated in the ensuing schematic representation(Fig. 1).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe comprehensive bioinformatic analyses were executed utilizing R version 3.6.3. For delineating the genetic mutation landscape, the \u0026apos;maftools\u0026apos; package in R was employed, which facilitated the generation of mutation landscape plots that elucidate the genomic alterations across various genes[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Visualization of drug sensitivity data, particularly highlighting the potential drug susceptibilities in small cell carcinomas of diverse primary origins, was accomplished using the \u0026apos;ggplot2\u0026apos; package, known for its robust graphical capabilities[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The \u0026apos;forest\u0026apos; package was adeptly applied to construct forest plots for univariate Cox proportional hazards regression analysis, offering insights into variable-specific hazard ratios. Survival curves and their respective statistical interpretations were elegantly presented through the utilization of the \u0026apos;survival\u0026apos; and \u0026apos;survminer\u0026apos; packages in R, ensuring a comprehensive and visually appealing survival analysis.[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]Significance in the context of statistical testing was determined by a threshold p-value of less than 0.05, adhering to the convention of using two-tailed tests for all statistical inferences.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eHigher Mutation Frequency of TP53, RB1, and TTN in Intrapulmonary Small Cell Carcinoma and Diverse Mutational Landscapes Across Ethnicities\u003c/p\u003e \u003cp\u003eThe overall mutation pattern in small cell carcinoma predominantly comprises missense mutations. Specifically, mutations in TP53, RB1, and TTN were observed at frequencies of 53%, 31%, and 17%, respectively, in small cell carcinoma. Notably, the replacement of Cytosine (C) with Adenine (A) and the substitution of Cytosine (C) with Thymine (T) were significantly more prevalent than other types of mutations. This mutation pattern may be associated with exposure to specific carcinogens, such as ultraviolet radiation and chemical carcinogens[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Due to the large volume of intrapulmonary samples, there is a potential dilution effect on the mutational characteristics of extrapulmonary small cell carcinoma. Therefore, the study subdivided small cell carcinoma into each primary site for corresponding mutation analysis. It was found that intrapulmonary small cell carcinoma exhibited high-frequency mutations in TP53 and RB1. The top 10 genes with the highest mutation frequency in intrapulmonary cases were TP53 (54%), RB1 (30%), TTN (20%), LRP1B (14%), CSMD3 (13%), USH2A (12%), RYR2 (12%), MUC16 (11%), ZFHX4 (11%), and KMT2D (10%), as shown in Fig.\u0026nbsp;2A. These mutations predominantly cluster in pathways related to genomic integrity, cell cycle, and RTK signaling. Local data from patients with small cell lung carcinoma were also collected for similar mutational analysis (Fig.\u0026nbsp;2B), revealing that the top 10 mutated genes were TP53 (83%), TTN (72%), MUC16 (52%), RYR2 (45%), CSMD3 (43%), USH2A (43%), OBSCN (36%), HMCN1 (34%), and SYNE1 (34%), primarily impacting pathways associated with genomic integrity. Comparing our local data with the information from the Cosmic database, we observed potential ethnic variations in mutation patterns. Our local cohort from Zhujiang Hospital predominantly comprises Asian patients,[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] while the Cosmic database mainly contains data from Caucasian individuals. This indicates that genetic research should thoroughly consider ethnic differences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiverse Mutation Spectra in Extrapulmonary Small Cell Carcinoma Across Different Primary Sites\u003c/p\u003e \u003cp\u003eExtrapulmonary small cell carcinomas (EPSCCs) also exhibit high mutation frequencies in TP53 and RB1 genes. Notably, in EPSCC, the top 10 genes with the highest mutation frequencies include TP53 (52%), RB1 (34%), SMARCA4 (14%), PIK3CA (13%), FAT1 (12%), ERBB2 (10%), CREBBP (9%), KMT2A (9%), KMT2D (9%), and ARID1A (9%). Similar to intrapulmonary small cell carcinomas, these genes are predominantly involved in pathways related to genomic integrity and the cell cycle signaling. However, a distinguishing feature of EPSCC is the enrichment of high-frequency mutations in pathways associated with chromosomal alterations. The mutation profiles of small cell carcinomas originating from different primary sites are illustrated in Figs.\u0026nbsp;2C and 2D.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSmall Cell Carcinoma of the Bladder\u003c/h2\u003e \u003cp\u003eThe top ten genes with the highest mutation frequencies in small cell carcinoma of the bladder are TP53 (80%), RB1 (69%), ARID1A (24%), FAT1 (24%), KMT2D (24%), ERBB2 (22%), CREBBP (20%), KDM6A (20%), KMT2A (16%), and PIK3CA (16%). Intriguingly, in bladder small cell carcinoma, mutations in the chromatin remodeling complex pathway account for 39% of the alterations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSmall Cell Carcinoma of the Prostate\u003c/h2\u003e \u003cp\u003eIn small cell carcinoma of the prostate, the top ten genes exhibiting the highest mutation frequencies are TP53 (45%), RB1 (29%), FOXA1 (26%), PIK3CD (26%), BRCA2 (19%), KMT2A (19%), GRIN2A (16%), APC (13%), ARID1B (13%), and CREBBP (13%). The three most significantly mutated pathways include genomic integrity (61%), cell cycle (29%), and transcription factor-related pathways (29%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSmall Cell Carcinoma of the Biliary Tract\u003c/h2\u003e \u003cp\u003eFor biliary tract small cell carcinoma, the genes exhibiting elevated mutation frequencies are distinctively TP53 (55%), TTN (45%), HMCN1 (35%), MUC4 (35%), VPS13D (35%), HRNR (30%), NACAD (30%), OBSCN (30%), PKD1L1 (30%), and ZNF208 (30%). The mutational landscape in these cases primarily revolves around pathways involving genomic integrity, transcriptional regulation, and chromatin remodeling complexes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSmall Cell Carcinoma of the Uterus\u003c/h2\u003e \u003cp\u003eIn uterine small cell carcinoma, the genes most frequently mutated include PIK3CA (34%), TP53 (23%), KRAS (20%), PIEZO2 (11%), ERBB4 (9%), FLNC (9%), GCN1 (9%), PLEC (9%), VRNP200 (9%), and ISC2 (9%). The mutation patterns are primarily concentrated in the PI3K pathway (43%), genomic integrity (32%), and the MAPK signaling pathway (25%). There appears to be a mutational exclusivity between PIK3CA and TP53 mutations as opposed to KRAS mutations, while the rest exhibit a tendency for co-occurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSmall Cell Carcinoma of the Ovary\u003c/h2\u003e \u003cp\u003eOvarian small cell carcinoma exhibits a relatively singular mutation pattern, with the top ten mutated genes identified as SMARCA4 (90%), ASXL1 (3%), JAK3 (3%), KMT2A (3%), MPL (3%), NOTCH2 (3%), PTPRT (3%), TP53 (3%), and WT1 (3%). The primary alterations in signaling pathways are observed in chromatin histone modification (3%), other aspects of chromatin (3%), and genomic integrity (3%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSmall Cell Carcinoma of the Thymus\u003c/h2\u003e \u003cp\u003eThe top ten mutated genes identified in thymic small cell carcinoma are ABHD2 (33%), YRX (33%), CDH26 (33%), CTNNB1 (33%), EEF2KMT (33%), NPIPB15 (33%), TP53 (33%), and ZNF814 (33%). In summary, the frequency of mutated genes varies depending on the primary site of origin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCancer Treatment Strategy Analysis with Limited Samples: Differences Between Chemotherapy and Targeted Therapy\u003c/h2\u003e \u003cp\u003eGiven the variation in mutation spectra across different primary sites of cancer, an individualized and targeted approach is imperative in devising cancer treatment strategies. Within our limited dataset, we gathered drug sensitivity data from two gastric small cell carcinoma cell lines, ECC10 and ECC12, and one cervical small cell carcinoma cell line, TC-YIK, from the GDSC database. Combining this with drug sensitivity information from five commonly used small cell carcinoma cell lines, we further analyzed the sensitivity of cells from different primary sites to existing conventional drugs (Fig.\u0026nbsp;3). We observed that in terms of chemotherapy: whether it be lung, gastric, or cervical small cell carcinoma cell lines, all exhibited resistance (high IC50) to DNA-damaging alkylating agents, cyclophosphamide (an alkylating agent), and cytarabine. The commonly used chemotherapy drug cisplatin might be more effective for lung small cell carcinoma, but it may not be the optimal choice for gastric or cervical small cell carcinomas. Notably, the cervical small cell carcinoma showed higher sensitivity to gemcitabine and Mitoxantrone compared to other cell lines.\u003c/p\u003e \u003cp\u003eRegarding targeted therapy, drugs targeting the SMO and ERK/MAPK signaling pathways showed high IC50 values, while those targeting BCL2 apoptosis regulation and the EGFR signaling pathway had lower IC50 values, suggesting that drugs targeting BCL2 apoptosis regulation and the EGFR pathway could be more effective in treating small cell carcinoma. Among various targeted therapies, cervical small cell carcinoma seems to be more sensitive to Axitinib, Crizotinib, and Talazoparib. Compared to gastric small cell carcinoma, Osimertinib and Trametinib showed stronger responses in cervical small cell carcinoma. Furthermore, our analysis indicated a high frequency of mutations in the MAPK signaling pathway in colorectal small cell carcinoma, and GDSC drug sensitivity data suggested that MAPK pathway inhibitors are not very effective in targeted therapy of small cell carcinoma, whereas PI3K pathway inhibitors showed slightly better efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurvival Analysis\u003c/p\u003e \u003cp\u003eAs elucidated previously, small cell carcinomas from varied primary origins manifest distinct mutational profiles. In the current era of rapidly advancing scientific technologies and the burgeoning field of multi-omics, a growing corpus of literature indicates that disparate mutational signatures may prognosticate divergent outcomes. In light of this, an extensive survival analysis was undertaken using the SEER database, encompassing the comprehensive categorization of small cell carcinoma. The dataset, spanning from 2000 to 2018, was rigorously examined. The findings delineate a consistent downward trajectory in incidence rates over the years. Moreover, a pronounced disparity in incidence rates was observed between genders, with males exhibiting a higher rate than females and a more precipitous decline. This phenomenon may be indicative of an increasing public health consciousness and alterations in lifestyle factors, exerting a salutary effect on cancer incidence. The collected clinical baseline data of the cohort is systematically tabulated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, we have gathered data pertaining to the variations in the incidence rates of small cell lung cancer spanning the years 1980 to 2018. This dataset reveals an annual escalation in incidence rates prior to 1990. Post-1990, however, there is a noticeable decrement in the prevalence of small cell lung cancer. Notably, the incidence rates among males consistently surpass those in females.( Supplementary Fig.\u0026nbsp;3)\u003c/p\u003e \u003cp\u003e \u003cem\u003ePrimary Sites of Small Cell Carcinoma: Predominantly in the Lung, Followed by the Urinary, Digestive, and Female Reproductive Systems\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOur analysis of data extracted from the SEER database, spanning 2000 to 2018, revealed a total of 122,218 small cell carcinoma cases. Notably, 53,806 of these were primary intrapulmonary small cell carcinomas, while 8,674 cases were classified as extrapulmonary (EPSCC). This data highlights a higher incidence of intrapulmonary small cell carcinoma compared to EPSCC. In the subset of EPSCC, the urinary system was the most prevalent primary site, succeeded by the digestive system and the female reproductive system, as illustrated in Supplementary Fig.\u0026nbsp;1.. A significant observation was the greater proportion of positive lymph nodes found in intrapulmonary small cell carcinoma patients compared to EPSCC, suggesting a less extensive metastatic pattern in EPSCC(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDemographic analysis indicated that small cell carcinoma affects more males than females, with a notably higher incidence among Caucasians. Intrapulmonary cases often presented with a larger number of positive lymph nodes, implying a higher tendency for metastasis to peripheral lymph nodes and a greater malignancy degree. Regarding treatment, chemotherapy alone or in combination with radiotherapy was the predominant approach. More than half of both intrapulmonary and extrapulmonary small cell carcinoma patients underwent surgery at the primary site, with a higher prevalence of surgery-only treatments observed in EPSCC patients. The majority of patients, irrespective of the carcinoma location, received external beam radiation therapy as part of their treatment regimen(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003cp\u003e115978\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003cp\u003e2069\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale Rep\u003c/p\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003cp\u003e1247\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHead and neck region\u003c/p\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003cp\u003e344\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrinary System\u003c/p\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003cp\u003e2548\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndocrine system\u003c/p\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57779\u003c/p\u003e \u003cp\u003e58199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e901\u003c/p\u003e \u003cp\u003e1168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1247\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e454\u003c/p\u003e \u003cp\u003e2194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003cp\u003e61\u0026ndash;80\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e481\u003c/p\u003e \u003cp\u003e27911\u003c/p\u003e \u003cp\u003e75247\u003c/p\u003e \u003cp\u003e12337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e592\u003c/p\u003e \u003cp\u003e1071\u003c/p\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e307\u003c/p\u003e \u003cp\u003e425\u003c/p\u003e \u003cp\u003e418\u003c/p\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e89\u003c/p\u003e \u003cp\u003e175\u003c/p\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e359\u003c/p\u003e \u003cp\u003e1549\u003c/p\u003e \u003cp\u003e726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003cp\u003eMarried\u003c/p\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003cp\u003eSingle\u003c/p\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003cp\u003eUnmarried/Domestic Partner\u003c/p\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15984\u003c/p\u003e \u003cp\u003e56910\u003c/p\u003e \u003cp\u003e1338\u003c/p\u003e \u003cp\u003e14723\u003c/p\u003e \u003cp\u003e21840\u003c/p\u003e \u003cp\u003e167\u003c/p\u003e \u003cp\u003e5016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221\u003c/p\u003e \u003cp\u003e1102\u003c/p\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e297\u003c/p\u003e \u003cp\u003e316\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129\u003c/p\u003e \u003cp\u003e529\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e333\u003c/p\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9954\u003c/p\u003e \u003cp\u003e4487\u003c/p\u003e \u003cp\u003e4487\u003c/p\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252\u003c/p\u003e \u003cp\u003e1661\u003c/p\u003e \u003cp\u003e152\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189\u003c/p\u003e \u003cp\u003e916\u003c/p\u003e \u003cp\u003e137\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e314\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e178\u003c/p\u003e \u003cp\u003e2336\u003c/p\u003e \u003cp\u003e131\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.1cm\u003c/p\u003e \u003cp\u003e0.2\u0026thinsp;~\u0026thinsp;98.8cm\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;98.8cm\u003c/p\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003cp\u003e12685\u003c/p\u003e \u003cp\u003e4349\u003c/p\u003e \u003cp\u003e98808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e277\u003c/p\u003e \u003cp\u003e138\u003c/p\u003e \u003cp\u003e1652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e173\u003c/p\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e1004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e294\u003c/p\u003e \u003cp\u003e353\u003c/p\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9195\u003c/p\u003e \u003cp\u003e16090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1593\u003c/p\u003e \u003cp\u003e2958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e764\u003c/p\u003e \u003cp\u003e4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e315\u003c/p\u003e \u003cp\u003e1505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e470\u003c/p\u003e \u003cp\u003e6241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003cp\u003eRadiation\u0026thinsp;+\u0026thinsp;Chemotherapy\u003c/p\u003e \u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;Radiation\u003c/p\u003e \u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;Chemotherapy\u003c/p\u003e \u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;Chemoradiotherapy\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e854\u003c/p\u003e \u003cp\u003e6678\u003c/p\u003e \u003cp\u003e33697\u003c/p\u003e \u003cp\u003e42511\u003c/p\u003e \u003cp\u003e110\u003c/p\u003e \u003cp\u003e916\u003c/p\u003e \u003cp\u003e1063\u003c/p\u003e \u003cp\u003e30149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e607\u003c/p\u003e \u003cp\u003e375\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e164\u003c/p\u003e \u003cp\u003e107\u003c/p\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112\u003c/p\u003e \u003cp\u003e50\u003c/p\u003e \u003cp\u003e149\u003c/p\u003e \u003cp\u003e262\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e287\u003c/p\u003e \u003cp\u003e218\u003c/p\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e36\u003c/p\u003e \u003cp\u003e110\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e73\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e647\u003c/p\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e252\u003c/p\u003e \u003cp\u003e156\u003c/p\u003e \u003cp\u003e104\u003c/p\u003e \u003cp\u003e759\u003c/p\u003e \u003cp\u003e382\u003c/p\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethods of radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal irradiation\u003c/p\u003e \u003cp\u003eInternal exposure\u003c/p\u003e \u003cp\u003eExternal\u0026thinsp;+\u0026thinsp;internal exposure\u003c/p\u003e \u003cp\u003eDilemma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49287\u003c/p\u003e \u003cp\u003e88\u003c/p\u003e \u003cp\u003e142\u003c/p\u003e \u003cp\u003e66461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e539\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e387\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e119\u003c/p\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e224\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e675\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeer stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003cp\u003eRegional\u003c/p\u003e \u003cp\u003eDistant\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4328\u003c/p\u003e \u003cp\u003e18525\u003c/p\u003e \u003cp\u003e65339\u003c/p\u003e \u003cp\u003e27786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003cp\u003e337\u003c/p\u003e \u003cp\u003e1077\u003c/p\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003cp\u003e332\u003c/p\u003e \u003cp\u003e410\u003c/p\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e124\u003c/p\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e885\u003c/p\u003e \u003cp\u003e511\u003c/p\u003e \u003cp\u003e845\u003c/p\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eGender, Ethnicity, Surgical Status, Chemotherapy/Radiotherapy, SEER Stage, and Marital Status as Prognostic Factors for Overall Survival in Small Cell Carcinoma Patients\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIncorporating variables such as gender, ethnicity, and treatment modality, a comprehensive univariate and multivariate Cox proportional hazards model was developed (Fig.\u0026nbsp;4A). The analysis yielded statistically significant findings regarding the impact of gender on survival time, with male patients demonstrating poorer prognoses. Furthermore, the influence of ethnicity on survival time was significant, with Caucasian patients experiencing different outcomes compared to other ethnic groups. Surgical intervention at the primary site was associated with a reduced risk of death compared to those who did not undergo surgery. Both radiotherapy and chemotherapy significantly affected survival time. Interestingly, marital status also had a significant impact on survival time. Patients with partners showed better prognoses compared to those who were single or widowed, which may be attributed to the enhanced medical support provided by stable family relationships.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSite- and Stage-Specific Prognoses in Small Cell Carcinoma: SEER Data Insights\u003c/h2\u003e \u003cp\u003eUtilizing the Surveillance, Epidemiology, and End Results (SEER) database, we amassed survival data for small cell carcinoma (SCLC) originating from diverse anatomical locations including the respiratory, digestive, endocrine, female reproductive, and urinary systems, as well as the head and neck region. Kaplan-Meier survival analysis yielded significant insights. Specifically, in localized SCLC, the most favorable prognosis was observed in neoplasms of the female reproductive system, followed sequentially by those in the head and neck region. Conversely, the respiratory and urinary systems demonstrated notably poorer prognoses.\u003c/p\u003e \u003cp\u003eIn instances of regional and distant SCLC, patients with neoplasms in the head and neck or female reproductive system manifested the most advantageous prognostic outcomes within our dataset. In stark contrast, SCLC of the respiratory system exhibited the most adverse prognosis, with urinary and digestive system SCLCs occupying an intermediate prognostic position (as depicted in Fig.\u0026nbsp;4C).\u003c/p\u003e \u003cp\u003eTo investigate the survival disparities across different primary sites of small cell carcinoma (SCC), this study conducted a detailed division of SCCs within each system and further analyzed survival data. The findings revealed significant prognostic variations even within the same system. For instance, within the reproductive system, breast cancer showed the most favorable prognosis, followed by ovarian and uterine cancers with intermediate outcomes, and vaginal small cell carcinoma with the least favorable prognosis. In the urinary system, bladder carcinoma exhibited the best prognosis, prostate carcinoma showed an intermediate prognosis, and renal small cell carcinoma had the least favorable outcomes. Contrary to the common belief that extrapulmonary small cell carcinomas have better prognoses, our research indicates that the prognosis of some primary sites may be better (e.g., esophagus, bile duct, intestine), worse (e.g., liver, pancreas, stomach), or similar to that of small cell lung cancer (SCLC) .( Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eAligned with extant literature and clinical paradigms, our analysis also corroborated that patients with distant metastases (categorized as 'DISTANT' in SEER staging) experienced the gravest prognosis, whereas those with localized manifestations of the disease had the most favorable outcomes (refer to Fig.\u0026nbsp;4D for SEER staging categories: Distant, Regional, Localized). Additionally, our findings suggest that an integrated approach of surgery coupled with radiotherapy and chemotherapy conferred a survival benefit across all stages.( Supplementary Fig.\u0026nbsp;2) Notably, patients who underwent primary site surgery exhibited a significantly enhanced prognosis compared to their non-surgical counterparts, likely attributable to earlier disease detection and intervention. Crucially, the amalgamation of local surgical excision, whether supplemented with systemic adjuvant radiotherapy and chemotherapy or not, appears to augment overall survival (OS), a notion gaining traction in contemporary clinical practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFirstly, our analysis of cosmic mutation data revealed that high-frequency mutation genes vary across different primary sites. Regardless of being extrapulmonary or EPSCC, there is a higher incidence of TP53 and RB1 mutations, consistent with previous studies. For instance, research by Nikolas G. Balanis et al. indicated that the loss and/or inactivating mutations of TP53 and RB1 are enriched in prostatic neuroendocrine carcinoma. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]Similar gene alterations in TP53 and RB1 are observed in small cell carcinoma of the esophagus, bladder,[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and prostate. Notably, TP53 and RB1 are well-known tumor suppressor genes. TP53 plays a pivotal role in maintaining genomic stability, inhibiting cell proliferation, and inducing apoptosis in damaged cells. Mutations or inactivation of TP53 can lead to defective p53 function, impairing the cell's appropriate response to DNA damage and increasing susceptibility to cancer development. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] The RB1 gene, encoding the pRB protein, regulates cell cycle progression to inhibit cell proliferation. Mutations or inactivation of RB1 can cause aberrations in the cell cycle, leading to uncontrolled cell proliferation and promoting cancer cell overgrowth.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The frequent mutations in these two genes may result in excessive cell proliferation, leading to an imbalance in the nuclear-cytoplasmic ratio, which could explain why most cells in small cell carcinoma tissues have an increased nucleus-to-cytoplasm ratio without forming distinct structures.\u003c/p\u003e \u003cp\u003eOur study demonstrates that high-frequency mutations in bladder, lung, and prostate small cell carcinomas involve cell cycle signaling pathways. Chemotherapy, typically more toxic to rapidly proliferating cancer cells, could partly explain its efficacy in treating small cell carcinomas.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Additionally, we discovered high-frequency mutations in PIK3CA and PIK3CD in prostate and uterine small cell carcinomas, suggesting a potential role for PI3K inhibitors in their treatment. However, our further GDSC drug sensitivity analysis did not show a therapeutic advantage of PI3K inhibitors in cervical small cell carcinoma, indicating a need for more samples for validation.\u003c/p\u003e \u003cp\u003eFurthermore, our research indicates that, irrespective of being extrapulmonary or pulmonary, small cell carcinomas have a higher prevalence in male patients. Previous reports have suggested that estrogen may act as a protective factor, reducing the risk of lung, colorectal, bladder, stomach, kidney, and pancreatic tumors, which might imply its protective role in small cell carcinoma patients as well[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, our analysis suggests that Caucasians have a worse prognosis in small cell carcinoma compared to other ethnicities. However, this contrasts with previous reports of a higher risk of advanced cancer in Black individuals than in Caucasians,[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] necessitating further research to understand the underlying reasons for these ethnic disparities in small cell carcinoma prognosis.\u003c/p\u003e \u003cp\u003eIn the realm of our detailed mutation analysis, several critical insights emerged. Predominantly, it was observed that small cell lung carcinomas are characterized by an elevated frequency of mutations in areas crucial for genomic integrity, cell cycle regulation, and PTK signaling pathways. Turning our attention to EPSCC (Extrapulmonary Small Cell Carcinoma), there is a noteworthy observation of a substantial mutation prevalence in genes such as TB53, RB1, and TTN. This finding becomes particularly interesting when juxtaposed with small cell lung carcinomas. Here, EPSCC demonstrated a slightly augmented mutation frequency in SMAECA4, PI3KCA, FAT1, ERBB2, CREBBP, KMT2A, KMT2D, ARID1A, and KDM6A. It merits special attention that the mutation rate of TTN in pulmonary small cell carcinoma reaches as high as 20%, whereas in EPSCC, this rate declines to just 7%, with a considerable majority of these mutations predominantly found in bile duct small cell carcinoma, where the TTN mutation frequency soars to a remarkable 45%. Echoing the mutation profile of small cell lung carcinoma, the mutational signaling pathways in EPSCC were primarily concentrated on genomic integrity and the cell cycle. Yet, a significant and intriguing divergence is noted in EPSCC, which exhibits more pronounced mutations in chromosomal signaling pathways, marking a distinct contrast from its pulmonary counterpart.\u003c/p\u003e \u003cp\u003eSmall cell lung carcinoma exhibits a high mutation frequency in genomic integrity, cell cycle, and PTK signaling pathways. In EPSCC, high mutation rates of TB53, RB1, TTN are observed, but compared to small cell lung carcinoma, SMAECA4, PI3KCA, FAT1, ERBB2, CREBBP, KMT2A, KMT2D, ARID1A, KDM6A show slightly higher mutation frequencies in EPSCC. TTN mutations, accounting for 20% in small cell lung carcinoma, drop to 7% in EPSCC, with the majority concentrated in bile duct small cell carcinoma, reaching up to 45%. Like small cell lung carcinoma, EPSCC mutations are focused on genomic integrity and the cell cycle, but differ in the prominence of chromosomal signaling pathway mutations.\u003c/p\u003e \u003cp\u003eIn recent years, immunotherapy has become a hotspot in cancer treatment. The 2018 IMpower133 study by Roche, the first clinical study where an immunotherapy checkpoint inhibitor (anti-PD-L1 atezolizumab combined with chemotherapy) was used in first-line treatment of SCLC, established the standard treatment in ES-SCLC at the 2018 WCLC conference. Subsequently, to further improve the survival of small cell lung carcinoma, new immunotherapy combination strategies are continuously being explored.[\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Due to the rarity of extrapulmonary small cell carcinoma, there are currently no clinical studies on immunotherapy for this cancer type. Recent reports suggest that co-mutations of TP53 and TTN in lung adenocarcinoma patients are associated with higher TMB levels and better immunotherapy response, potentially making them promising biomarkers for assessing immunotherapy effectiveness in lung adenocarcinoma.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] A study on the use of Apatinib combined with Camrelizumab in patients with advanced melanoma, using an open-label, single-arm Phase 2 trial design, found that patients with TTN mutations had better progression-free survival (PFS) and overall survival (OS).[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Our analysis also reveals that cancer types with a better prognosis than small cell lung carcinoma do not have high-frequency TTN mutations, while those with a worse prognosis, such as bile duct small cell carcinoma and small cell lung carcinoma, have high-frequency TTN mutations. Immunotherapy indeed extends the survival of small cell lung carcinoma patients, with samples containing TTN mutations showing significantly higher tumor mutation burdens, consistent with previous reports. A 2020 study also indicated that spontaneous mutations in the TTN gene represent a high tumor mutation burden, and the TTN mutation spectrum can serve as a predictive factor for MSI-H.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Considering these studies and findings, it is necessary to explore how small cell carcinoma with high-frequency TTN mutations responds to immunotherapy. For cancer types with a worse prognosis than small cell lung carcinoma, specific data on gene mutation conditions are not available, requiring more information for further analysis.\u003c/p\u003e \u003cp\u003eIn bladder small cell carcinoma, high-frequency mutations include an additional KMT2D gene compared to prostate small cell carcinoma. KMT2 genes might be necessary for repairing DNA damage caused by carcinogen exposure (such as excessive smoking). Cells with KMT2 gene mutations are unable to repair these DNA damages, leading to an accumulation of mutations in the genome. Researchers have identified KMT2C and KMT2D mutations as urgent biomarkers for guiding PARP inhibitor treatment in non-small cell lung carcinoma.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Combined with our earlier survival analysis, the prognosis of bladder small cell carcinoma appears better than prostate small cell carcinoma. The KMT2D mutation should be more detrimental to cancer cell development, indicating a worse prognosis. However, the prognosis might appear better due to earlier diagnosis and treatment of bladder cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough comprehensive data analysis and studies on drug sensitivity, we have unveiled the genetic characteristics and variations in drug response of small cell carcinoma across different primary sites. The drug sensitivity analysis indicates a general poor response of small cell carcinoma to alkylating agents, while targeted therapies focusing on the cell cycle, BCL2 apoptosis regulation, and the EGFR signaling pathway demonstrate greater potential. Specifically, in small cell carcinomas of the lung, stomach, and cervix, drugs targeting BCL2 and EGFR exhibited lower IC50 values, suggesting their suitability for treating these types of small cell carcinoma. Additionally, the high frequency of mutations in the MAPK signaling pathway in extrapulmonary small cell carcinoma suggests limited efficacy of targeted therapies for this cancer type.\u003c/p\u003e \u003cp\u003eRegarding gene mutations, the high frequency of TP53 and RB1 mutations has been a hallmark of small cell carcinoma, potentially leading to excessive cell proliferation and cell cycle anomalies, thereby explaining the increased nucleus-to-cytoplasm ratio in small cell carcinoma cells. Moreover, mutations in TTN and KMT2D may involve potential targets for small cell carcinoma treatment. Mutations in TTN are associated with the response to immunotherapy and could serve as biomarkers for immunotherapy effectiveness, while KMT2D mutations may impact DNA damage repair capabilities, providing biomarkers for PARP inhibitor treatment.\u003c/p\u003e \u003cp\u003eIn conclusion, our observations of significant prognostic differences in small cell carcinoma across different ethnicities and primary sites underscore the necessity of personalized treatment. These in-depth analyses offer critical insights for the future precision treatment of small cell carcinoma, where specific targeted therapies and immunotherapies may become key areas in the management of small cell carcinoma.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eDespite certain advancements made in this study regarding Extrapulmonary Small Cell Carcinoma (EPSCC), there are several notable limitations. Firstly, the study inevitably faced the challenge of limited sample sizes for EPSCC, restricting our ability to conduct an in-depth investigation of this rare cancer type. The low incidence of EPSCC makes it difficult to gather a sufficiently large patient sample, which could impact the comprehensive understanding of the full spectrum of this tumor.\u003c/p\u003e \u003cp\u003eSecondly, this study was not based on a singular database for holistic analysis but rather relied on data compiled from multiple databases. There are potential differences between these databases, including data collection methods and patient selection criteria, which might introduce heterogeneity and affect our accuracy in grasping the overall situation.\u003c/p\u003e \u003cp\u003eFurthermore, we noted that the analysis for certain cancer types was limited by small sample sizes, constraining our capacity for a more profound investigation of these cancers. Larger-scale sample data are needed for these specific types of small cell carcinoma to more reliably reveal their unique characteristics and treatment responses.\u003c/p\u003e \u003cp\u003eThe lack of sufficient drug sensitivity data also represents a significant shortcoming of this study. Drug treatment impacts on tumors are a crucial aspect of therapeutic research; however, the absence of adequate drug sensitivity data means that our understanding of different types of small cell carcinoma responses to specific treatment regimens is incomplete.\u003c/p\u003e \u003cp\u003eAdditionally, our research did not conduct a thorough differential analysis of other aspects of the genome, nor did it delve into analyses based on mRNA levels. These areas represent directions for further research to more comprehensively and profoundly understand the pathobiological mechanisms of EPSCC.\u003c/p\u003e \u003cp\u003eIn summary, although this study has achieved some preliminary results, more extensive and in-depth research is required to address these limitations and advance our understanding and treatment capabilities for EPSCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Chunqian (Y Chunqian): Responsible for the research design, data collection, data analysis, and manuscript writing. As the first author, Yang Chunqian participated in all stages of the research, from conceptualization to the preparation of the final manuscript.\u003c/p\u003e\n\u003cp\u003eWei Ting (W Ting): Provided guidance on the research direction, was responsible for obtaining funding for the project, reviewed the manuscript, and approved the final version. As the corresponding author, Wei Ting played a key role in the design of the study, interpretation of the results, and ensuring the accuracy of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate:\u003c/strong\u003e Not applicable. Our study did not involve human participants, human data, or human tissue, and therefore did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e Not applicable. This manuscript does not contain any personal data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e The data supporting the findings of this study are available from the corresponding author upon reasonable request. The authors are committed to ensuring the availability of data in accordance with the principles of data sharing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that they have no competing interests. This statement is made to confirm the absence of any financial, personal, or professional conflict that could be construed to influence the work reported in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was supported by the National Natural Science Foundation of China (General Program; Key Program; Major Program), grant number 81772457. The funding body supported the design of the study, analysis, and interpretation of data, and in writing the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThompson L: \u003cstrong\u003eWorld Health Organization classification of tumours: pathology and genetics of head and neck tumours\u003c/strong\u003e. \u003cem\u003eEar, nose, \u0026amp; throat journal \u003c/em\u003e2006, \u003cstrong\u003e85\u003c/strong\u003e(2):74.\u003c/li\u003e\n\u003cli\u003eSolcia E, Kl\u0026ouml;ppel G, Sobin LH: \u003cstrong\u003eHistological typing of endocrine tumours\u003c/strong\u003e: Springer Science \u0026amp; Business Media; 2012.\u003c/li\u003e\n\u003cli\u003eGarrow GC, Greco FA, Hainsworth JD: \u003cstrong\u003ePoorly differentiated neuroendocrine carcinoma of unknown primary tumor site\u003c/strong\u003e. \u003cem\u003eSemin Oncol \u003c/em\u003e1993, \u003cstrong\u003e20\u003c/strong\u003e(3):287-291.\u003c/li\u003e\n\u003cli\u003eLevenson RM, Ihde DC, Matthews MJ, Cohen MH, Gazdar AF, Bunn PA, Minna JD: \u003cstrong\u003eSmall cell carcinoma presenting as an extrapulmonary neoplasm: sites of origin and response to chemotherapy\u003c/strong\u003e. \u003cem\u003eJ Natl Cancer Inst \u003c/em\u003e1981, \u003cstrong\u003e67\u003c/strong\u003e(3):607-612.\u003c/li\u003e\n\u003cli\u003eRemick SC, Ruckdeschel JC: \u003cstrong\u003eExtrapulmonary and pulmonary small-cell carcinoma: tumor biology, therapy, and outcome\u003c/strong\u003e. \u003cem\u003eMed Pediatr Oncol \u003c/em\u003e1992, \u003cstrong\u003e20\u003c/strong\u003e(2):89-99.\u003c/li\u003e\n\u003cli\u003eWong YNS, Jack RH, Mak V, Henrik M, Davies EA: \u003cstrong\u003eThe epidemiology and survival of extrapulmonary small cell carcinoma in South East England, 1970-2004\u003c/strong\u003e. \u003cem\u003eBMC Cancer \u003c/em\u003e2009, \u003cstrong\u003e9\u003c/strong\u003e:209.\u003c/li\u003e\n\u003cli\u003eSengoz M, Abacioglu U, Salepci T, Eren F, Yumuk F, Turhal S: \u003cstrong\u003eExtrapulmonary small cell carcinoma: multimodality treatment results\u003c/strong\u003e. \u003cem\u003eTumori \u003c/em\u003e2003, \u003cstrong\u003e89\u003c/strong\u003e(3):274-277.\u003c/li\u003e\n\u003cli\u003eLin Y-L, Chung C-Y, Chang C-S, Wu J-S, Kuo K-T, Kuo S-H, Cheng A-L: \u003cstrong\u003ePrognostic factors in extrapulmonary small cell carcinomas. 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D\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRecruitment of KMT2C/MLL3 to DNA Damage Sites Mediates DNA Damage Responses and Regulates PARP Inhibitor Sensitivity in Cancer\u003c/strong\u003e. \u003cem\u003eCancer research \u003c/em\u003e2021, \u003cstrong\u003e81\u003c/strong\u003e(12):3358-3373.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Small Cell Carcinoma, Pan-Cancer Research, Extrapulmonary Small Cell Carcinoma, Genomic Mutation, Drug Sensitivity, Cosmic Database, SEER Database, GDSC Database","lastPublishedDoi":"10.21203/rs.3.rs-3914949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3914949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThis study aims to delve into the differences and commonalities among small cell carcinomas (SCC) originating from different sites, including extrapulmonary small cell carcinoma (EPSCC) and small cell lung carcinoma (SCLC). We focus on understanding the trends in incidence, genomic characteristics, and treatment strategies for these subtypes, addressing the gaps in our knowledge of these rare and heterogeneous diseases.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA comprehensive approach was employed using data from Cosmic, SEER, and GDSC databases. Epidemiological data were obtained from the SEER database, genomic mutation information from the Cosmic database, and drug sensitivity data from the GDSC database. Statistical tests were applied to analyze the data, revealing epidemiological variations in SCC across different populations and regions and identifying genomic variations.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAnalysis indicates a significant difference in the incidence rates of EPSCC and SCLC, with EPSCC currently accounting for 2% \u0026minus;\u0026thinsp;4% of all SCC diagnoses. Genomic analysis unveils both shared and unique mutational landscapes between these two subtypes, guiding future therapeutic strategies. Tailored treatment plans were formulated based on the site of origin, and analysis of the SEER database highlighted epidemiological variations in SCC, emphasizing key factors associated with survival rates.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study provides in-depth insights into the differences and commonalities among small cell carcinomas originating from different sites, offering crucial clues for precision treatment strategies. The rising incidence of EPSCC underscores its clinical significance. These findings not only expand our understanding of SCC biology but also have profound implications for improving clinical treatment outcomes for patients..\u003c/p\u003e","manuscriptTitle":"Survival and mutational analysis of small cell carcinoma in pan-cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 21:41:40","doi":"10.21203/rs.3.rs-3914949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7156afaf-fcae-4559-9d8b-1848ca96d6f1","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-08T20:02:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-12 21:41:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3914949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3914949","identity":"rs-3914949","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}