Exploring the Role of KIR2DS4 and HLA-A*02:07 in Predicting Chemotherapy Sensitivity and Erythrocytopenia in Nasopharyngeal Carcinoma

Exploring the Role of KIR2DS4 and HLA-A*02:07 in Predicting Chemotherapy Sensitivity and Erythrocytopenia in Nasopharyngeal Carcinoma | 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 Exploring the Role of KIR2DS4 and HLA-A*02:07 in Predicting Chemotherapy Sensitivity and Erythrocytopenia in Nasopharyngeal Carcinoma Jie-Mei Ye, Hao-Lin Ma, Xue-Meng Jiang, Wei Zhao, Peng Yu, Wen-Yang Wei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5963730/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: Nasopharyngeal carcinoma (NPC) is common in Southeast Asia, with most patients diagnosed with locally advanced disease. Radiotherapy alone is often ineffective, so platinum-based chemotherapy is combined for better outcomes. However, chemotherapy response and side effects vary among patients. Genetic markers, particularly human leukocyte antigen (HLA) and killer-cell immunoglobulin-like receptors (KIR), have been implicated in modulating chemotherapy sensitivity and toxicity. Identifying these markers could facilitate personalized treatment strategies for NPC patients. Methods: This study included 204 NPC patients between April 2020 and October 2021, and performed KIR and HLA-A allele typing. The control group consisted of 201 healthy individuals, matched by gender and age, who underwent routine health check-ups at the hospital. Among the cases, 110 nasopharyngeal carcinoma patients who received platinum based chemotherapy were analyzed for the relationship between KIR and HLA genotype characteristics and chemotherapy sensitivity, as well as the occurrence of chemotherapy induced side effects. Results: NPC patients exhibited higher expression of activating KIR2DS4 (97.55% vs 91.54%, OR = 3.677, 95% CI = 1.320 ~ 10.168, P = 0.008) and inhibitory KIR3DL1 (97.55% vs 93.03%, OR = 2.980, 95% CI = 1.053 ~ 8.434, P = 0.032), suggesting their involvement in the disease. The BB haplotype, a particular KIR gene combination, was less frequent in NPC patients, hinting at a protective effect (4.90% vs 11.44%, OR = 0.399, 95% CI = 0.185 ~ 0.861, P = 0.016). The detection frequency of HLA-A*11:01 in the NPC case group was significantly lower than that in the healthy control group (23.53% vs 30.71%, OR = 0.694, 95% CI = 0.505 ~ 0.955, P = 0.024), and the detection frequency of HLA-A*02:07 was significantly higher than that in the healthy control group (17.16% vs 8.70%, OR = 2.175, 95% CI = 1.394 ~ 3.392, P < 0.001). Notably, HLA-A*02:07 was associated with increased chemotherapy sensitivity (51.35% vs 21.91%, OR = 3.760, 95% CI = 1.552 ~ 8.648, P = 0.002). Additionally, the KIR2DS4*003 allele was linked to a reduced incidence of chemotherapy-induced erythrocytopenia (2.63% vs 97.37% in non-carriers, OR = 0.135, 95% CI = 0.017 ~ 1.082, P = 0.032). Conclusions: Our findings suggest that HLA-A*02:07 and KIR2DS4 are promising genetic markers for predicting chemotherapy sensitivity and the risk of erythrocytopenia in NPC patients. These results support the potential for personalized chemotherapy regimens based on genetic profiling, helping to reduce side effects and improve treatment efficacy. Nasopharyngeal carcinoma chemotherapy sensitivity KIR HLA erythrocytopenia genetic markers personalized treatment. 1 Introduction Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial lining of the nasopharynx, with a high prevalence in Southeast Asia, particularly in China, where it is considered a major health concern (Chen et al. 2025 ; Yu et al. 2022 ; Zhang et al. 2024 ). The incidence of NPC is notably high among individuals of Southern Chinese descent, with genetic and environmental factors contributing to its pathogenesis. Epstein-Barr Virus (EBV) infection is considered a critical etiological factor in the development of NPC (Chen et al. 2022 ; Liu et al. 2022 ; Yang et al. 2015 ; Zhang et al. 2017 ; Zheng et al. 2020 ). While advances in diagnostic techniques and treatment protocols, including chemotherapy, radiotherapy, and immunotherapy, have improved survival rates, the clinical outcomes of NPC patients remain highly variable. This variability is largely due to the complex interplay between genetic factors, tumor biology, and individual patient characteristics. Therefore, identifying genetic markers that predict chemotherapy sensitivity and susceptibility to side effects, such as erythrocytopenia, is vital for tailoring personalized treatment strategies and improving clinical outcomes. Platinum-based chemotherapy, especially regimens containing cisplatin or carboplatin, remains the cornerstone of treatment for advanced NPC(Gharib and Elkady 2024 ; Guo et al. 2017 ; Lam and Chan 2018 ; Ma et al. 2020 ; Xiao et al. 2013 ). Despite its efficacy in controlling tumor growth, chemotherapy is associated with significant side effects, including hematological toxicity, particularly erythrocytopenia, which can lead to treatment delays or discontinuation. Various genetic factors have been implicated in the variation of chemotherapy response and toxicity, with human leukocyte antigen (HLA) and killer-cell immunoglobulin-like receptors (KIR) being among one of studied (Abed et al. 2022 ; Araz et al. 2015 ; De Re et al. 2014 ; Kandilarova et al. 2016 ; Leone et al. 2017 ; Mezquita et al. 2017 ; Saraiva et al. 2018 ; Urrutia-Maldonado et al. 2023 ; Varbanova et al. 2016 ; Venstrom et al. 2009 ; Yu et al. 2017 ). The HLA system plays a crucial role in immune response by presenting antigens to T cells, which are involved in the recognition and elimination of tumor cells. Genetic variations in both HLA and KIR loci can influence the immune response to chemotherapy, potentially affecting the efficacy of treatment and the severity of side effects (De Re et al. 2014 ; Leone et al. 2017 ; Mezquita et al. 2017 ; Urrutia-Maldonado et al. 2023 ; Varbanova et al. 2016 ). However, the specific genetic markers that predict chemotherapy sensitivity and toxicity in NPC patients remain poorly understood, requiring further investigation. Recent studies have explored the relationship between KIR and HLA genetic polymorphisms and their impact on NPC outcomes, but the findings remain inconclusive (Agostini et al. 2018 ; Douik et al. 2016 ; Geng et al. 2016 ; Huisman et al. 2022 ; Mokni-Baizig et al. 2017 ; Ren et al. 2016 ; Tsao et al. 2017 ; Wang et al. 2016 ; Wang et al. 2024 ; Wong et al. 2018 ). Some studies suggest that certain HLA alleles, such as HLA-A*02, are associated with enhanced chemotherapy sensitivity, while others show that particular KIR alleles may influence the occurrence of chemotherapy-induced side effects, including anemia and neutropenia (Larson et al. 2024 ; Okita et al. 2019 ; Rivas-Fuentes et al. 2018 ; Rosenbaum et al. 2020a ; Rosenbaum et al. 2020b ; Scarabel et al. 2022 ; Sinn et al. 2018 ; Tangamornsuksan et al. 2020 ; Wu et al. 2018 ; Zhang et al. 2022 ). Additionally, variations in these genetic markers may modulate the immune microenvironment of the tumor, further influencing treatment efficacy and toxicity. This study aims to investigate the role of KIR and HLA-A genotyping in predicting chemotherapy sensitivity and the risk of erythrocytopenia in NPC patients. Understanding the genetic factors that contribute to chemotherapy outcomes in NPC may pave the way for personalized treatment regimens, minimizing side effects while maximizing therapeutic efficacy. 2 Methods 2.1 Patients This study included 204 patients diagnosed with NPC at the Red Cross Hospital, Wuzhou, between April 2020 and October 2021. Of these, 110 patients (92 males, 18 females; median age 50 years, range 28–78) who received induction chemotherapy with platinum-based drugs followed by concurrent chemoradiotherapy were included for further analysis. Patients with severe cardiovascular, neurological, or liver/kidney dysfunction were excluded. The control group consisted of 201 healthy individuals, matched by gender and age, who underwent routine health check-ups at the hospital. All NPC patients were staged according to the 8th edition of the UICC/AJCC TNM system (stage III-IVa)(Huang and O'Sullivan 2017; Pan et al. 2019). 2.2 Ethical Statement The study was approved by the Ethics Committee of the Red Cross Hospital, Wuzhou (Approval No. 2018-7), and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants for their data to be used in the study. 2.3 Gene Genotyping Methodology 2.3.1 DNA Extraction Genomic DNA was extracted from peripheral blood samples (5 mL, EDTA-K anticoagulated) using the MagCore HF16 plus DNA extraction system (MagCore HF16 plus, MagCore, Taiwan), following the manufacturer's protocol. The DNA concentration and purity were assessed using the Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), with the A260/A280 ratio required to be between 1.8 and 2.0. 2.3.2 KIR Genotyping KIR gene genotyping was performed using SYBR Green I Real-Time PCR and PCR-SSP methods. A total of 30 KIR gene-specific primers were synthesized (Invitrogen, USA) for 15 pairs, including primers for activating KIR2DS1 and other KIR genes. For PCR-SSP, the KAPA2G Robust HotStart PCR Kit (KAPA Biosystems, USA) was used. The PCR reaction mixture contained: 5 μL SYBR Green I Master Mix, 2 μL of 10 µM primer solution, 0.2 μL of 50 ng/µL genomic DNA, ddH 2 O to make up to 10 μL per reaction. For the KIR2DS4 gene, sequencing was performed using the PCR-SBT method. The KIR2DS4 PCR primers and reaction conditions were adapted from the method described by Deng et al (Shenzhen Blood Center, China). The PCR products were purified and analyzed by sequencing with the ABI 3730XL (Applied Biosystems, USA). 2.3.3 HLA-A Genotyping HLA-A genotyping was performed using PCR-SBT, focusing on exons 2 and 3, which are highly polymorphic. PCR amplification was done using the KAPA2G Robust HotStart PCR Kit (KAPA Biosystems). The amplification conditions were similar to those used for KIR genotyping. The PCR products were purified, and sequencing was carried out using the ABI 3730XL DNA Analyzer. The sequencing results were analyzed using Assign 1.0.2.45 SBT software, with allele identification based on the reference database. 2.3.4 Genotyping Analysis After amplification, PCR products were analyzed using 1.2% agarose gel electrophoresis (GelDoc, Bio-Rad Laboratories, USA). KIR2DS4 and other KIR alleles were identified by the presence of specific bands. The sequencing data were analyzed using Assign 1.0.2.45 SBT software to detect errors and identify alleles. 2.4 Treatment Among case group, 110 patients received either the GP regimen (gemcitabine 80 mg/m², cisplatin 80 mg/m²) or the TP regimen (docetaxel 75 mg/m², cisplatin 75 mg/m²) as induction chemotherapy, followed by concurrent chemoradiotherapy with cisplatin (80-100 mg/m²). Patients were closely monitored for hematological toxicity, particularly erythrocytopenia, and other adverse effects such as nausea and vomiting. Chemotherapy responses were assessed according to the RECIST 1.1 criteria, categorizing patients as chemotherapy-sensitive (complete or partial remission) or chemotherapy-resistant (stable or progressive disease). Chemotherapy-induced side effects were monitored, with special attention to erythrocytopenia and other hematological toxicities, graded according to WHO standards. 2.5 Follow-up Patients were followed up for 6 to 18 months after completing chemotherapy, with imaging (CT or MRI) to assess tumor progression and clinical examination for recurrence. Hematological tests were conducted regularly to monitor the occurrence of erythrocytopenia and other toxicities. 2.6 Statistical Analysis Data were analyzed using SPSS version 26.0. Frequencies of KIR genes, HLA alleles, and KIR/HLA genotypes were compared between the case and control groups using Chi-square or Fisher's exact test. Odds ratios (OR) with 95% confidence intervals (CI) were calculated to assess the association between genetic markers and chemotherapy response or toxicity. Statistical significance was set at P<0.05. 3 Results 3.1 KIR Gene Variants and Their Association with NPC Susceptibility In this study, we compared the frequency of KIR gene expression between NPC patients and healthy controls to identify potential genetic risk factors for NPC. The analysis revealed that the activating KIR2DS4 and inhibitory KIR3DL1 alleles were significantly more frequent in the NPC group compared to the control group. Specifically, the frequency of KIR2DS4 in the NPC group was 97.55%, significantly higher than the 91.54% seen in the control group (OR = 3.677, 95% CI = 1.320-10.168, P = 0.008). Similarly, KIR3DL1 was expressed more frequently in the NPC group (97.55%) than in the controls (93.03%), with a significant difference (OR = 2.98, 95% CI = 1.053-8.434, P = 0.032)( Table 1-1). These findings suggest that both KIR2DS4 and KIR3DL1 may contribute to NPC susceptibility, making them potential biomarkers for predicting cancer risk in individuals with genetic predispositions. Additionally, when examining the KIR genotype combinations, we found that the BB genotype was significantly less common in the NPC group (4.90%) compared to the control group (11.44%), with a P-value of 0.016. The BB genotype, which is considered protective, was associated with a decreased risk of developing NPC (OR = 0.399, 95% CI = 0.185-0.861). Conversely, the AA and AB genotypes, which were present in similar frequencies in both groups, did not show any significant association with NPC susceptibility ( P = 0.314 and P = 0.796, respectively) (Table 1-2). Further analysis focused on the 2DS4 allele revealed no significant differences in the individual allele frequencies between the NPC and control groups. The 2DS4*003 allele, which had a frequency of 12.06% in the NPC group and 8.7% in the control group, did not reach statistical significance ( P = 0.282). However, the combination of the 2DS4-Normal and 2DS4-Deleted alleles showed a significantly higher frequency of the 2DS4-Deleted/Deleted genotype in the NPC group (20.6%) compared to the control group (11.96%, P = 0.023) (Tables 1-3 and 1-4). This suggests that the 2DS4-Deleted/Deleted combination may be associated with increased susceptibility to NPC. Overall, our results emphasize the importance of KIR and HLA genetic variations in NPC susceptibility. The significant associations found with KIR2DS4 and KIR3DL1, particularly their higher frequencies in NPC patients, highlight the potential of these genes as biomarkers for NPC risk. The finding that specific KIR genotypes and 2DS4 allele combinations can influence NPC susceptibility provides new insights into the genetic mechanisms underlying this disease. These results warrant further investigation into the use of genetic profiling for early detection and personalized treatment strategies in NPC patients. Table 1-1 Comparison of KIR Gene Detection Frequencies between Case and Control Groups KIR gene Case Group（n=204） Control Group（n=201） OR 95%CI P value Count(n) Frequency（%） Count(n) Frequency（%） 2DL1 204 100.00 199 99.00 - - - 2DL2 58 28.43 59 29.35 0.956 0.622~1.469 0.838 2DL3 199 97.55 198 98.51 0.603 0.142~2.557 0.724 2DL4 △ 204 100.00 201 100.00 - - - 2DS2 59 28.92 60 29.85 0.956 0.623~1.466 0.837 2DS3 54 26.47 58 28.86 0.888 0.574~1.372 0.592 2DS4* 199 97.55 184 91.54 3.677 1.320~10.168 0.008 2DS5 40 19.61 43 21.39 0.896 0.553~1.452 0.656 3DL1* 199 97.55 187 93.03 2.98 1.053~8.434 0.032 3DS1 79 38.73 78 38.81 0.997 0.668~1.486 0.987 3DL2 △ 204 100.00 201 100.00 - - - 3DL3 △ 204 100 201 100.00 - - - 2DL5 87 42.65 88 43.78 0.955 0.644~1.415 0.818 2DP1 204 100.00 200 99.50 - - - 2DS1 71 34.80 63 31.34 1.169 0.773~1.770 0.459 Note: △ denotes framework genes. Table 1-2 Comparison of KIR Genotype Detection Frequencies Between Case and Control Groups KIR Genotype Case Group (n=204） Control Group (n=201) OR 95%CI P value Count(n) Frequency（%） Count(n) Frequency（%） AA 97 47.55 85 42.29 1.223 0.827~1.809 0.314 Bx 107 52.45 116 57.71 AB 97 47.55 93 46.27 1.053 0.713~1.555 0.796 BB* 10 4.90 23 11.44 0.399 0.185~0.861 0.016 Note: KIR Bx refers to the combination of KIR AB and KIR BB haplotypes. Table 1-3 Comparison of 2DS4 Alleles and Their Detection Frequencies in NPC Case and Healthy Control Groups Allele Case Group （n=199） Control Group（n=184） OR 95%CI P value Count(n) Frequency（%） Count(n) Frequency（%） 2DS4*00101 155 77.89 145 78.8 0.947 0.582~1.542 0.828 2DS4*003 24 12.06 16 8.7 1.44 0.739~2.086 0.282 2DS4*004 32 16.08 37 20.11 0.761 0.451~1.284 0.305 2DS4*010 73 36.68 66 35.87 1.036 0.683~1.572 0.869 2DS4*014 - - 1 0.54 - - - Table 1-4 Comparison of 2DS4-Normal and 2DS4-Deleted Combinations in Case and Control Groups KIR2DS4 Combination Case Group（n=199） Control Group（n=184） OR 95%CI P value Count(n) Frequency（%） Count(n) Frequency（%） 2DS4-Normal/Deleted 75 37.69 86 46.74 0.689 0.459~1.036 0.073 2DS4-Normal/Normal 83 41.71 76 41.3 1.017 0.677~1.527 0.935 2DS4-Deleted/Deleted* 41 20.6 22 11.96 1.911 1.089~3.353 0.023 3.2 HLA-A Allele Frequencies and Their Association with NPC Susceptibility In this study, we analyzed the HLA-A genotypes in a cohort of 204 NPC patients and 184 healthy controls to further explore the role of KIR/HLA interactions in NPC susceptibility. The analysis revealed significant differences in the frequencies of certain HLA-A alleles between the two groups. Notably, the frequency of HLA-A11:01 was significantly lower in the NPC group compared to the control group (23.53% vs. 30.71%, OR=0.694, 95% CI=0.505–0.955, P =0.024), indicating that HLA-A11:01 may act as a protective factor against NPC. In contrast, the frequency of HLA-A02:07 was significantly higher in the NPC group (17.16% vs. 8.70%, OR=2.175, 95% CI=1.394–3.392, P <0.001), suggesting that HLA-A02:07 may be a susceptibility factor for NPC. These results suggest that the presence of HLA-A11:01 may decrease the risk of NPC, whereas HLA-A02:07 may increase the risk, further emphasizing their potential roles in NPC pathogenesis (Table 2). Additionally, we examined other HLA-A alleles, such as A33:03, A24:02, and A02:03, which did not show any statistically significant differences between the case and control groups ( P >0.05). These findings underscore the importance of HLA-A genotyping in understanding the genetic factors underlying NPC susceptibility. The strong association between HLA-A02:07 and NPC highlights the potential of this allele as a marker for identifying individuals at higher risk for the disease. Moreover, the protective role of HLA-A11:01 suggests that it may have therapeutic implications in terms of understanding immune responses to NPC and the development of targeted therapies. Future research should further investigate these associations and explore how KIR/HLA interactions influence immune evasion in NPC. Table 2 Comparative Analysis of Detection Frequencies of HLA-A Alleles Between Case and Control Groups HLA Allele Case Group（2n=408） Case Group（2n=368） OR 95%CI P value Count(n) Frequency（%） Count(n) Frequency（%） A*11:01* 96 23.53 113 30.71 0.694 0.505~0.955 0.024 A*02:07* 70 17.16 32 8.70 2.175 1.394~3.392 0.000 A*33:03 63 15.44 53 14.40 1.085 0.730~1.613 0.685 A*24:02 55 13.48 48 13.04 1.039 0.685~1.574 0.858 A*02:03 46 11.27 44 11.96 0.936 0.603~1.452 0.764 A*02:01 32 7.84 19 5.16 1.563 0.870~2.809 0.132 A*02:06 19 4.66 11 2.99 1.585 0.744~3.777 0.229 A*11:02 13 3.19 13 3.53 0.899 0.411~1.965 0.789 A*03:01 3 0.74 6 1.63 0.447 0.111~1.800 0.321 A*26:01 3 0.74 3 0.82 - - 1.000 A*24:07 2 0.49 0 0.00 - - - A*33:01 2 0.49 0 0.00 - - - A*29:01 1 0.25 3 0.82 - - - A*31:01 1 0.25 6 1.63 - - - A*32:01 1 0.25 0 0.00 - - - A*33:19 1 0.25 0 0.00 - - - A*11:10 0 0.00 1 0.27 - - - A*24:03 0 0.00 1 0.27 - - - A*30:01 0 0.00 4 1.09 - - - A*24:10 0 0.00 1 0.27 - - - A*30:04 0 0.00 1 0.27 - - - A*33:10 0 0.00 1 0.27 - - - A*33:11 0 0.00 1 0.27 - - - A*68:01 0 0.00 1 0.27 - - - A*74:01 0 0.00 1 0.27 - - - A*74:05 0 0.00 1 0.27 - - - A*01:01 0 0.00 1 0.27 - - - A*02:10 0 0.00 1 0.27 - - - A*23:01 0 0.00 2 0.54 - - - 3.3 KIR and HLA Interactions in NPC Susceptibility In this study, we analyzed the interaction between KIR and HLA receptor-ligand combinations to assess their association with NPC susceptibility. Our findings revealed a significant difference in the detection frequency of specific KIR+HLA combinations between the NPC patient group and the control group. Among the combinations tested, the group of patients carrying HLA-A11:01 but not KIR2DS4 showed a significantly lower detection rate compared to the control group (0.94% vs. 4.35%, OR=0.108, 95% CI=0.013–0.875, P =0.015). This result supports the notion that HLA-A11:01 has a protective effect against NPC development. Additionally, we found that the absence of both KIR2DS2 and HLA-A11:01 was associated with an increased susceptibility to NPC (42.65% vs. 32.07%, OR=1.575, 95% CI=1.040–2.387, P =0.032). Conversely, the presence of KIR2DS2 with HLA-A11:01 provided a protective effect against NPC, as evidenced by a lower frequency of NPC in this combination (28.43% vs. 38.59%, OR=0.632, 95% CI=0.413–0.967, P =0.034) (Table 3). These findings suggest that HLA-A*11:01 may have a protective role in NPC, and the presence of KIR2DS2 interacts with it to modulate NPC susceptibility. Moreover, the analysis of the KIR2DS4+A11 receptor-ligand combination revealed that although the combination was found in both the case and control groups, there was no significant difference in its frequency (50% vs. 42.39%, P =0.133). However, the absence of the 2DS4+A11 combination, which was found in only 0.49% of the NPC patients, was significantly lower than in the control group (4.35%, OR=0.108, 95% CI=0.013–0.875, P =0.015), further supporting the protective effect of HLA-A*11:01 in NPC. This highlights the importance of understanding the complex interactions between KIR genes and HLA alleles in determining susceptibility to NPC and suggests the potential of using these genetic markers for early detection and personalized treatment strategies(Table 3). In conclusion, our study highlights the significant role of specific KIR and HLA genotypes and their combinations in influencing NPC susceptibility. The protective effect of HLA-A*11:01, particularly in combination with specific KIR alleles, may provide insights into NPC pathogenesis and aid in developing more personalized treatment approaches. Further research is needed to validate these findings and better understand the immune mechanisms underlying NPC susceptibility. Table 3 Comparative Analysis of Detection Frequencies of 2DS4+A11 and 3DL2+A11 Receptor-Ligand Combinations in Case and Control Groups KIR+HLA Receptor-Ligand Combination Case Group（n=204） Case Group（n=184） OR 95%CI P value Count(n) Frequency（%） Count(n) Frequency（%） active 2DS4+A11 2DS4+A11- 102 50.00 78 42.39 1.359 0.910~2.029 0.133 2DS4+A11+ 97 47.55 93 50.54 0.887 0.595~1.322 0.566 2DS4-A11- 4 1.96 5 2.72 0.716 0.189~2.708 0.621 2DS4-A11+* 1 0.49 8 4.35 0.108 0.013~0.875 0.015 2DS4+A*11:01- 111 54.41 85 46.20 1.390 0.932~2.074 0.106 2DS4+A*11:01+ 88 43.14 86 46.74 0.864 0.579~1.291 0.476 2DS4-A*11:01- 4 1.96 5 2.72 0.716 0.189~2.708 0.741 2DS4-A*11:01+** 1 0.49 8 4.35 0.108 0.013~0.875 0.015 2DS4+A*11:02- 186 91.18 159 86.41 1.625 0.855~3.087 0.136 2DS4+A*11:02+ 13 6.37 12 6.52 0.976 0.423~2.196 0.952 2DS4-A*11:02- 5 2.45 12 6.52 2.286 0.438~11.930 0.453 2DS4-A*11:02+ - - 1 0.54 - - - active 2DS2+A*11：01 2DS2+A*11:01- 28 13.73 31 16.85 0.785 0.451~1.368 0.392 2DS2+A*11:01+ 31 15.20 23 12.50 1.254 0.702~2.241 0.444 2DS2-A*11:01-* 87 42.65 59 32.07 1.575 1.040~2.387 0.032 2DS2-A*11:01+* 58 28.43 71 38.59 0.632 0.413~0.967 0.034 3.4 HLA-A Genotype and Chemotherapy Sensitivity in NPC In this study, we examined the relationship between HLA-A genotypes and chemotherapy sensitivity in 110 NPC patients who underwent platinum-based chemotherapy(Table 4-1, 4-2). We observed a significant association between the presence of HLA-A02:07 and increased chemotherapy sensitivity. Specifically, patients carrying the HLA-A02:07 allele demonstrated a 3.76-fold higher response rate to chemotherapy (51.35% vs. 21.92%, OR=3.760, 95% CI=1.607–8.801, P=0.002) compared to those who did not carry this allele. This result underscores the importance of HLA-A*02:07 as a potential biomarker for predicting chemotherapy sensitivity in NPC patients (Table 4-4). In contrast, other HLA-A genotypes, such as HLA-A11:01, showed no significant association with chemotherapy sensitivity (40.54% vs. 59.46%, OR=0.740, 95% CI=0.332–1.649, P=0.461).(Table 4-4). These findings suggest that while certain HLA-A alleles may influence treatment outcomes, not all HLA genotypes contribute significantly to chemotherapy sensitivity. We also explored the role of KIR genes and their interaction with HLA-A alleles. However, our analysis revealed no significant correlation between KIR genotypes and chemotherapy sensitivity. Despite this, the strong association between HLA-A*02:07 and enhanced chemotherapy response warrants further investigation into how specific HLA genotypes can be leveraged in clinical practice for more personalized treatment regimens. Further, when comparing the chemotherapy sensitivity across different demographic and clinical factors such as age, gender, and the chemotherapy regimen (GP vs. TP), no significant differences were found, suggesting that these factors do not significantly influence chemotherapy response in this cohort ( P > 0.05) (Table 4-3). Thus, genetic factors such as HLA-A*02:07 appear to be more critical in determining chemotherapy sensitivity than demographic or clinical characteristics. The findings suggest that HLA-A genotyping could serve as an important tool in personalizing chemotherapy treatment for NPC, although further studies are needed to fully understand the genetic interactions at play. Table 4-1 Clinical Data of 110 Patients with NPC Clinical Data Number(n) Frequency（%） Sex Male 92 83.64 Female 18 16.36 Age ≥50 56 50.91 <50 54 49.09 T Stage T0 1 0.90 T2 30 27.27 T3 29 26.36 T4 50 45.45 N Stage N0 1 0.90 N1 15 13.64 N2 63 57.27 N3 31 28.18 Clinical Stage Ⅲ 38 34.55 Ⅳa 72 64.45 Induction Chemotherapy Regimen GP (Gemcitabine + Cisplatin) 87 79.09 TP (Docetaxel + Cisplatin) 23 20.91 Table 4-2 The Sensitivity of Chemotherapy in Patients with NPC Recent Efficacy Number (n) Frequency (%) CR 6 5.45 PR 31 28.18 SD 72 65.45 PD 1 0.91 Table 4-3 Relationship between the Sensitivity of Chemotherapy and Clinical Data Basic Data Chemotherapy Sensitivity OR 95%CI P value PR+CR（%） SD+PD（%） Gender Male 29（31.52） 63（68.48） 0.575 0.206~1.609 0.289 Female 8（44.44） 10（55.56） Age ≥50 20（35.71） 36（64.29） 1.209 0.547~2.672 0.639 <50 17（31.48） 37（68.52） Treatment GP 28（32.18） 59（67.82） 0.738 0.285~1.910 0.531 TP 9（39.13） 14（60.87） Table 4-4 Correlation Analysis between HLA-A Genotype and Platinum Chemotherapy Sensitivity HLA-A Allele Chemotherapy Sensitivity OR 95%CI P value PR+CR（%） SD+PD（%） HLA-A*1101+ 15（40.54） 35（47.95） 0.740 0.332~1.649 0.461 HLA-A*1101- 22（59.46） 38（52.06） HLA-A*0207+ 19（51.35） 16（21.92） 3.760 1.607~8.801 0.002 HLA-A*0207-* 18（48.65） 57（78.08） 3.5 KIR2DS4 Allele and Its Association with Erythrocytopenia in NPC Chemotherapy In the analysis of the association between KIR2DS4 alleles and chemotherapy-induced side effects, particularly erythrocytopenia, we observed a significant finding regarding the KIR2DS4*003 allele. While the majority of KIR alleles did not show a significant correlation with the incidence of erythrocytopenia, the presence of the KIR2DS4*003 allele was notably associated with a lower incidence of this side effect. Specifically, patients with the KIR2DS4*003 allele had a significantly lower rate of erythrocytopenia (2.63%) compared to those without the allele (93.67%), with an odds ratio (OR) of 0.135 (95% CI = 0.017-1.082, P = 0.032) (Table 5). This suggests that KIR2DS4003 may play a protective role in reducing the risk of erythrocytopenia during platinum-based chemotherapy in NPC patients. These findings highlight the potential role of KIR2DS4*003 in reducing chemotherapy-induced side effects, specifically erythrocytopenia, which could help improve the management of NPC patients undergoing platinum-based chemotherapy. Further studies are necessary to confirm these results and to explore the underlying mechanisms by which KIR alleles influence chemotherapy toxicity. Table 5 Correlation Analysis between KIR2DS4 Allele and Erythrocytopenia KIR2DS4 Allele Erythrocytopenia（n=38） OR 95%CI P value Occurrence (%) Non-occurrence (%) 2DS4*00101 30（78.95） 59（81.94） 0.826 0.309~2.211 0.704 2DS4*003* 1（2.63） 12（16.67） 0.135 0.017~1.082 0.032 2DS4*004 7（18.42） 6（8.33） 2.484 0.770~8.011 0.132 2DS4*010 14（36.84） 25（34.72） 1.097 0.484~2.486 0.825 4 Discussion In this study, we investigated the role of genetic markers, specifically HLA-A and KIR2DS4 alleles, in predicting chemotherapy sensitivity and chemotherapy-induced erythrocytopenia in NPC patients. Our major findings revealed that the presence of HLA-A02:07 was significantly associated with increased chemotherapy sensitivity, with a 3.76-fold higher response rate compared to those without the allele ( P =0.002). Additionally, HLA-A11:01 was identified as a protective factor, with a lower frequency observed in chemotherapy-sensitive patients. In terms of chemotherapy-induced side effects, particularly erythrocytopenia, the KIR2DS4*003 allele was associated with a significantly lower incidence of erythrocytopenia ( P =0.032), suggesting a protective role in reducing hematological toxicity. These findings provide insights into the potential of using genetic testing for predicting treatment efficacy and managing side effects, supporting the idea of personalized chemotherapy regimens for NPC patients. Our findings align with several previous studies that have highlighted the role of HLA polymorphisms in influencing chemotherapy response(Abed et al. 2024; Araz et al. 2015; Gravett et al. 2018; Larson et al. 2022; Shehata et al. 2009; Wei et al. 2017). For instance, HLA-A has been reported to be associated with enhanced chemotherapy sensitivity in in certain types of cancers, where it was found to improve the efficacy of platinum-based chemotherapies(Korentzelos et al. 2022; Okita et al. 2019; Shehata et al. 2009; Wu et al. 2018). Similarly, our results confirm the protective effect of HLA-A11:01, which is consistent with studies showing that this allele is linked to a reduced risk of NPC development(He et al. 2022; Tang et al. 2012a; Tang et al. 2012b). However, our study diverges from others in its exploration of KIR2DS4 alleles. While a few studies have suggested that certain KIR alleles, particularly KIR2DS4, can influence chemotherapy outcomes(De Re et al. 2014), the protective association between KIR2DS4*003 and erythrocytopenia in our cohort is a novel finding. Previous studies on KIR2DS4 alleles in NPC have focused more on their role in immune evasion and cancer progression, rather than chemotherapy toxicity(Lin et al. 2023; Zhu et al. 2023). The absence of significant associations between other KIR2DS4 alleles and chemotherapy toxicity in our study further distinguishes it from some prior research, which has shown more pronounced effects of these alleles on immune responses and treatment outcomes. These differences highlight the need for further studies to clarify the role of KIR alleles in chemotherapy toxicity and their potential as biomarkers for predicting adverse reactions. This study employed a comprehensive approach to investigate the genetic factors influencing both chemotherapy sensitivity and side effects in NPC patients by combining high-resolution HLA-A genotyping and KIR gene profiling. The use of high-resolution gene sequencing for HLA-A genotyping allowed for the accurate identification of allele-specific variations, such as HLA-A02:07, which demonstrated a strong association with chemotherapy sensitivity in our cohort ( P =0.002). The inclusion of KIR2DS4 genotyping further enriched our study, providing insights into the role of immune-related genetic factors in chemotherapy-induced side effects, particularly erythrocytopenia. This method is unique as it not only explores genetic predictors of treatment efficacy but also examines the genetic risk factors for chemotherapy toxicity, which has been less frequently investigated in NPC. Our finding that KIR2DS4003 carriers were less likely to experience erythrocytopenia ( P =0.032) adds a novel dimension to the understanding of KIR-HLA interactions in chemotherapy-induced hematological toxicity. Previous studies have primarily focused on immune evasion in cancer progression, whereas our research emphasizes how these interactions can affect treatment outcomes and adverse effects(Li et al. 2024; Lin et al. 2023; Zhu et al. 2023). The combined analysis of both HLA and KIR genotypes in this study provides a more holistic view of the genetic factors that can guide chemotherapy regimens, suggesting that genetic testing could serve as a valuable tool for personalized treatment strategies in NPC. While this study offers valuable insights into the genetic factors influencing chemotherapy sensitivity and side effects in NPC patients, there are several limitations. The relatively small sample size of 110 patients may limit the generalizability of the results, and the single-center design could introduce selection bias. Additionally, potential confounding factors, such as lifestyle and environmental influences, were not accounted for, which may interact with genetic markers and affect treatment outcomes. The study also focused on specific HLA and KIR alleles, leaving out the potential role of other genetic factors that could provide a broader understanding of chemotherapy sensitivity and toxicity. Finally, the relatively short follow-up period did not allow for a long-term analysis of survival, recurrence, or late-onset side effects. Future research should aim to increase the sample size and include multi-center cohorts to enhance the generalizability of findings. A broader genetic analysis, incorporating other immune-related genes, would provide more comprehensive insights into the genetic determinants of chemotherapy response and toxicity. Additionally, studies should explore the interaction between genetic factors and environmental influences, such as EBV infection, diet, and smoking. Long-term studies are needed to assess the impact of genetic markers on survival, recurrence, and late-onset side effects. Employing advanced genomic techniques like next-generation sequencing could further broaden the scope of research and help identify novel biomarkers that may improve personalized treatment strategies for NPC patients. 5 Conclusion In conclusion, this study highlights the potential of genetic markers, specifically HLA-A and KIR alleles, in predicting chemotherapy sensitivity and side effects in NPC patients. Identifying these markers could pave the way for more personalized treatment strategies, reducing adverse effects such as erythrocytopenia and improving chemotherapy efficacy. However, the study also emphasizes the complexity of chemotherapy responses, suggesting that additional genetic factors, as well as environmental and lifestyle influences, may contribute to variations in treatment outcomes. The limitations of this study, including its small sample size and short follow-up period, suggest the need for further research with larger, multi-center cohorts to validate the results and explore the role of other genetic variations. Future studies should also incorporate more comprehensive genomic approaches, such as next-generation sequencing, to identify novel biomarkers for chemotherapy sensitivity and toxicity. Moreover, long-term follow-up studies are essential to assess the impact of these genetic markers on patient survival, recurrence, and late-onset toxicities. Overall, the integration of genetic testing into clinical practice for NPC could improve personalized treatment regimens, potentially enhancing patient outcomes and quality of life. 5 Conclusion In conclusion, this study highlights the potential of genetic markers, specifically HLA-A and KIR alleles, in predicting chemotherapy sensitivity and side effects in NPC patients. Identifying these markers could pave the way for more personalized treatment strategies, reducing adverse effects such as erythrocytopenia and improving chemotherapy efficacy. However, the study also emphasizes the complexity of chemotherapy responses, suggesting that additional genetic factors, as well as environmental and lifestyle influences, may contribute to variations in treatment outcomes. The limitations of this study, including its small sample size and short follow-up period, suggest the need for further research with larger, multi-center cohorts to validate the results and explore the role of other genetic variations. Future studies should also incorporate more comprehensive genomic approaches, such as next-generation sequencing, to identify novel biomarkers for chemotherapy sensitivity and toxicity. Moreover, long-term follow-up studies are essential to assess the impact of these genetic markers on patient survival, recurrence, and late-onset toxicities. Overall, the integration of genetic testing into clinical practice for NPC could improve personalized treatment regimens, potentially enhancing patient outcomes and quality of life. Abbreviations NPC – Nasopharyngeal Carcinoma HLA – Human Leukocyte Antigen KIR – Killer-cell Immunoglobulin-like Receptor EBV – Epstein-Barr Virus IMRT – Intensity-Modulated Radiation Therapy OR – Odds Ratio CI – Confidence Interval RECIST – Response Evaluation Criteria in Solid Tumors TP – Docetaxel and Cisplatin Chemotherapy Regimen GP – Gemcitabine and Cisplatin Chemotherapy Regimen Declarations Data Availability Statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. All relevant data are included in the manuscript and its supplementary files. Ethical Statement This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Red Cross Hospital, Wuzhou, Guangxi (approval number: 2018-7). All participants provided written informed consent for the collection and use of their clinical data for research purposes. Patient confidentiality was strictly maintained, and all data were anonymized before analysis. Author Contributions Jie-Mei Ye, Hao-Lin Ma, and Xue-Meng Jiang: Designed assays and performed sequence analysis, coauthored and edited the manuscript. Peng Yu, Wen-Yang Wei, and Xin-Yun Peng: Performed sequence analysis. Yong-Lin Luo, Bin Zhang, and Wei Zhao: Contributed patient material. Min-Zhong Tang: Designed and initiated the study and edited the manuscript. All the authors approved the final version. Acknowledgments The authors thank Shenzhen Blood Center for technical assistance. Funding This research was funded by the Guangxi Natural Science Foundation (Grant No. 2022JJA140620), the Wuzhou Health Commission Project (Project No. WZWS-H2023019), and the Guangxi Key Laboratory for Early Prevention and Treatment of Regional High-Incidence Tumors (Grant No. GKE-KF202207). Conflict of Interest The authors have declared no conflicting interests. References Abed A, Law N, Calapre L, Lo J, Bhat V, Bowyer S, Millward M, Gray ES (2022) Human leucocyte antigen genotype association with the development of immune-related adverse events in patients with non-small cell lung cancer treated with single agent immunotherapy. Eur J Cancer 172: 98-106. doi: 10.1016/j.ejca.2022.05.021 Abed A, Reid A, Law N, Millward M, Gray ES (2024) HLA-A01 and HLA-B27 Supertypes, but Not HLA Homozygocity, Correlate with Clinical Outcome among Patients with Non-Small Cell Lung Cancer Treated with Pembrolizumab in Combination with Chemotherapy. 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Journal of Clinical Oncology 40: 11502-11502. doi: 10.1200/JCO.2022.40.16_suppl.11502 Zhang Y, Gu S, Deng H, Shen Z (2024) Global epidemiological profile in nasopharyngeal carcinoma: a prediction study. BMJ Open 14: e091087. doi: 10.1136/bmjopen-2024-091087 Zheng MQ, Wang TM, Liao Y, Xue WQ, He YQ, Wu ZY, Yang DW, Li DH, Deng CM, Jia YJ, Yuan LL, Zhang WL, Luo LT, Tong XT, Wu YX, Zhou T, Li XZ, Tang LL, Zhang JB, Xia YF, Mu J, Jia WH (2020) Nasopharyngeal Epstein-Barr virus DNA loads in high-risk nasopharyngeal carcinoma families: Familial aggregation and host heritability. J Med Virol 92: 3717-3725. doi: 10.1002/jmv.26198 Zhu DQ, Su C, Li JJ, Li AW, Luv Y, Fan Q (2023) Update on Radiotherapy Changes of Nasopharyngeal Carcinoma Tumor Microenvironment. World J Oncol 14: 350-357. doi: 10.14740/wjon1645 Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Hao-Lin","middleName":"","lastName":"Ma","suffix":""},{"id":412556701,"identity":"94a33466-e420-4db8-91af-63a6bc013967","order_by":2,"name":"Xue-Meng Jiang","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue-Meng","middleName":"","lastName":"Jiang","suffix":""},{"id":412556702,"identity":"7c47e562-f863-430e-9c3c-9560badbd38a","order_by":3,"name":"Wei Zhao","email":"","orcid":"","institution":"Wuzhou Workers' Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhao","suffix":""},{"id":412556703,"identity":"6b36850f-bfb1-4d08-9075-5a8d0fb9ec66","order_by":4,"name":"Peng Yu","email":"","orcid":"","institution":"Guilin Medical 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11:23:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17379,"visible":true,"origin":"","legend":"","description":"","filename":"Primers.docx","url":"https://assets-eu.researchsquare.com/files/rs-5963730/v1/ac989cf691af0b8de5486701.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Role of KIR2DS4 and HLA-A*02:07 in Predicting Chemotherapy Sensitivity and Erythrocytopenia in Nasopharyngeal Carcinoma","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial lining of the nasopharynx, with a high prevalence in Southeast Asia, particularly in China, where it is considered a major health concern (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The incidence of NPC is notably high among individuals of Southern Chinese descent, with genetic and environmental factors contributing to its pathogenesis. Epstein-Barr Virus (EBV) infection is considered a critical etiological factor in the development of NPC (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While advances in diagnostic techniques and treatment protocols, including chemotherapy, radiotherapy, and immunotherapy, have improved survival rates, the clinical outcomes of NPC patients remain highly variable. This variability is largely due to the complex interplay between genetic factors, tumor biology, and individual patient characteristics. Therefore, identifying genetic markers that predict chemotherapy sensitivity and susceptibility to side effects, such as erythrocytopenia, is vital for tailoring personalized treatment strategies and improving clinical outcomes.\u003c/p\u003e \u003cp\u003ePlatinum-based chemotherapy, especially regimens containing cisplatin or carboplatin, remains the cornerstone of treatment for advanced NPC(Gharib and Elkady \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lam and Chan \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xiao et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Despite its efficacy in controlling tumor growth, chemotherapy is associated with significant side effects, including hematological toxicity, particularly erythrocytopenia, which can lead to treatment delays or discontinuation. Various genetic factors have been implicated in the variation of chemotherapy response and toxicity, with human leukocyte antigen (HLA) and killer-cell immunoglobulin-like receptors (KIR) being among one of studied (Abed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Araz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; De Re et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kandilarova et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Leone et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mezquita et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saraiva et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Urrutia-Maldonado et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Varbanova et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Venstrom et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The HLA system plays a crucial role in immune response by presenting antigens to T cells, which are involved in the recognition and elimination of tumor cells. Genetic variations in both HLA and KIR loci can influence the immune response to chemotherapy, potentially affecting the efficacy of treatment and the severity of side effects (De Re et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leone et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mezquita et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Urrutia-Maldonado et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Varbanova et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the specific genetic markers that predict chemotherapy sensitivity and toxicity in NPC patients remain poorly understood, requiring further investigation.\u003c/p\u003e \u003cp\u003eRecent studies have explored the relationship between KIR and HLA genetic polymorphisms and their impact on NPC outcomes, but the findings remain inconclusive (Agostini et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Douik et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Geng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huisman et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mokni-Baizig et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ren et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tsao et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wong et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some studies suggest that certain HLA alleles, such as HLA-A*02, are associated with enhanced chemotherapy sensitivity, while others show that particular KIR alleles may influence the occurrence of chemotherapy-induced side effects, including anemia and neutropenia (Larson et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Okita et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rivas-Fuentes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rosenbaum et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Rosenbaum et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Scarabel et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sinn et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tangamornsuksan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, variations in these genetic markers may modulate the immune microenvironment of the tumor, further influencing treatment efficacy and toxicity. This study aims to investigate the role of KIR and HLA-A genotyping in predicting chemotherapy sensitivity and the risk of erythrocytopenia in NPC patients. Understanding the genetic factors that contribute to chemotherapy outcomes in NPC may pave the way for personalized treatment regimens, minimizing side effects while maximizing therapeutic efficacy.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e2.1 Patients\u003c/p\u003e\n\u003cp\u003eThis study included 204 patients diagnosed with NPC at the Red Cross Hospital, Wuzhou, between April 2020 and October 2021. Of these, 110 patients (92 males, 18 females; median age 50 years, range 28–78) who received induction chemotherapy with platinum-based drugs followed by concurrent chemoradiotherapy were included for further analysis. Patients with severe cardiovascular, neurological, or liver/kidney dysfunction were excluded. The control group consisted of 201 healthy individuals, matched by gender and age, who underwent routine health check-ups at the hospital. All NPC patients were staged according to the 8th edition of the UICC/AJCC TNM system (stage III-IVa)(Huang and O'Sullivan 2017; Pan et al. 2019).\u003c/p\u003e\n\u003cp\u003e2.2 Ethical Statement\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Red Cross Hospital, Wuzhou (Approval No. 2018-7), and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants for their data to be used in the study.\u003c/p\u003e\n\u003cp\u003e2.3 Gene Genotyping Methodology\u003c/p\u003e\n\u003cp\u003e2.3.1 DNA Extraction\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from peripheral blood samples (5 mL, EDTA-K anticoagulated) using the MagCore HF16 plus DNA extraction system (MagCore HF16 plus, MagCore, Taiwan), following the manufacturer's protocol. The DNA concentration and purity were assessed using the Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), with the A260/A280 ratio required to be between 1.8 and 2.0.\u003c/p\u003e\n\u003cp\u003e2.3.2 KIR Genotyping\u003c/p\u003e\n\u003cp\u003eKIR gene genotyping was performed using SYBR Green I Real-Time PCR and PCR-SSP methods. A total of 30 KIR gene-specific primers were synthesized (Invitrogen, USA) for 15 pairs, including primers for activating KIR2DS1 and other KIR genes. For PCR-SSP, the KAPA2G Robust HotStart PCR Kit (KAPA Biosystems, USA) was used. The PCR reaction mixture contained:\u0026nbsp;5 μL SYBR Green I Master Mix,\u0026nbsp;2 μL of 10 µM primer solution,\u0026nbsp;0.2 μL of 50 ng/µL genomic DNA,\u0026nbsp;ddH\u003csub\u003e2\u003c/sub\u003eO to make up to 10 μL per reaction.\u0026nbsp;For the KIR2DS4 gene, sequencing was performed using the PCR-SBT method. The KIR2DS4 PCR primers and reaction conditions were adapted from the method described by Deng et al (Shenzhen Blood Center, China). The PCR products were purified and analyzed by sequencing with the ABI 3730XL (Applied Biosystems, USA).\u003c/p\u003e\n\u003cp\u003e2.3.3 HLA-A Genotyping\u003c/p\u003e\n\u003cp\u003eHLA-A genotyping was performed using PCR-SBT, focusing on exons 2 and 3, which are highly polymorphic. PCR amplification was done using the KAPA2G Robust HotStart PCR Kit (KAPA Biosystems). The amplification conditions were similar to those used for KIR genotyping. The PCR products were purified, and sequencing was carried out using the ABI 3730XL DNA Analyzer. The sequencing results were analyzed using Assign 1.0.2.45 SBT software, with allele identification based on the reference database.\u003c/p\u003e\n\u003cp\u003e2.3.4 Genotyping Analysis\u003c/p\u003e\n\u003cp\u003eAfter amplification, PCR products were analyzed using 1.2% agarose gel electrophoresis (GelDoc, Bio-Rad Laboratories, USA). KIR2DS4 and other KIR alleles were identified by the presence of specific bands. The sequencing data were analyzed using Assign 1.0.2.45 SBT software to detect errors and identify alleles.\u003c/p\u003e\n\u003cp\u003e2.4 Treatment\u003c/p\u003e\n\u003cp\u003eAmong case group, 110 patients received either the GP regimen (gemcitabine 80 mg/m², cisplatin 80 mg/m²) or the TP regimen (docetaxel 75 mg/m², cisplatin 75 mg/m²) as induction chemotherapy, followed by concurrent chemoradiotherapy with cisplatin (80-100 mg/m²). Patients were closely monitored for hematological toxicity, particularly erythrocytopenia, and other adverse effects such as nausea and vomiting. Chemotherapy responses were assessed according to the RECIST 1.1 criteria, categorizing patients as chemotherapy-sensitive (complete or partial remission) or chemotherapy-resistant (stable or progressive disease). Chemotherapy-induced side effects were monitored, with special attention to erythrocytopenia and other hematological toxicities, graded according to WHO standards.\u003c/p\u003e\n\u003cp\u003e2.5 Follow-up\u003c/p\u003e\n\u003cp\u003ePatients were followed up for 6 to 18 months after completing chemotherapy, with imaging (CT or MRI) to assess tumor progression and clinical examination for recurrence. Hematological tests were conducted regularly to monitor the occurrence of erythrocytopenia and other toxicities.\u003c/p\u003e\n\u003cp\u003e2.6 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS version 26.0. Frequencies of KIR genes, HLA alleles, and KIR/HLA genotypes were compared between the case and control groups using Chi-square or Fisher's exact test. Odds ratios (OR) with 95% confidence intervals (CI) were calculated to assess the association between genetic markers and chemotherapy response or toxicity. Statistical significance was set at P\u0026lt;0.05.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 KIR Gene Variants and Their Association with NPC Susceptibility\u003c/p\u003e\n\u003cp\u003eIn this study, we compared the frequency of KIR gene expression between NPC patients and healthy controls to identify potential genetic risk factors for NPC. The analysis revealed that the activating KIR2DS4 and inhibitory KIR3DL1 alleles were significantly more frequent in the NPC group compared to the control group. Specifically, the frequency of KIR2DS4 in the NPC group was 97.55%, significantly higher than the 91.54% seen in the control group (OR = 3.677, 95% CI = 1.320-10.168, P = 0.008). Similarly, KIR3DL1 was expressed more frequently in the NPC group (97.55%) than in the controls (93.03%), with a significant difference (OR = 2.98, 95% CI = 1.053-8.434, P = 0.032)( Table 1-1). These findings suggest that both KIR2DS4 and KIR3DL1 may contribute to NPC susceptibility, making them potential biomarkers for predicting cancer risk in individuals with genetic predispositions.\u003c/p\u003e\n\u003cp\u003eAdditionally, when examining the KIR genotype combinations, we found that the BB genotype was significantly less common in the NPC group (4.90%) compared to the control group (11.44%), with a P-value of 0.016. The BB genotype, which is considered protective, was associated with a decreased risk of developing NPC (OR = 0.399, 95% CI = 0.185-0.861). Conversely, the AA and AB genotypes, which were present in similar frequencies in both groups, did not show any significant association with NPC susceptibility (\u003cem\u003eP\u003c/em\u003e = 0.314 and \u003cem\u003eP\u003c/em\u003e = 0.796, respectively) (Table 1-2).\u003c/p\u003e\n\u003cp\u003eFurther analysis focused on the 2DS4 allele revealed no significant differences in the individual allele frequencies between the NPC and control groups. The 2DS4*003 allele, which had a frequency of 12.06% in the NPC group and 8.7% in the control group, did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e = 0.282). However, the combination of the 2DS4-Normal and 2DS4-Deleted alleles showed a significantly higher frequency of the 2DS4-Deleted/Deleted genotype in the NPC group (20.6%) compared to the control group (11.96%, P = 0.023) (Tables 1-3 and 1-4). This suggests that the 2DS4-Deleted/Deleted combination may be associated with increased susceptibility to NPC.\u003c/p\u003e\n\u003cp\u003eOverall, our results emphasize the importance of KIR and HLA genetic variations in NPC susceptibility. The significant associations found with KIR2DS4 and KIR3DL1, particularly their higher frequencies in NPC patients, highlight the potential of these genes as biomarkers for NPC risk. The finding that specific KIR genotypes and 2DS4 allele combinations can influence NPC susceptibility provides new insights into the genetic mechanisms underlying this disease. These results warrant further investigation into the use of genetic profiling for early detection and personalized treatment strategies in NPC patients.\u003c/p\u003e\n\u003cp\u003eTable 1-1 Comparison of KIR Gene Detection Frequencies between Case and Control Groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"687\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eKIR\u0026nbsp;\u003c/em\u003egene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCase Group（n=204）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 170px;\"\u003e\n \u003cp\u003eControl Group（n=201）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e28.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e29.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.622~1.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e97.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e98.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.142~2.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DL4\u003c/em\u003e△\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e28.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e29.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.623~1.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e26.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e28.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.574~1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e97.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e91.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.320~10.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.553~1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e3DL1*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e97.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e93.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.053~8.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e3DS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e38.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.668~1.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e3DL2\u003c/em\u003e△\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e3DL3\u003c/em\u003e△\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DL5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e43.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.644~1.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e99.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e34.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e31.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.773~1.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: △ denotes framework genes.\u003c/p\u003e\n\u003cp\u003eTable 1-2 Comparison of KIR Genotype Detection Frequencies Between Case and Control Groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"598\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eKIR Genotype\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCase Group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n=204）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003eControl Group (n=201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 114px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e47.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e42.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.827~1.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e52.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e57.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e47.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e46.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.713~1.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBB*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.185~0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: KIR Bx refers to the combination of KIR AB and KIR BB haplotypes.\u003c/p\u003e\n\u003cp\u003eTable 1-3 Comparison of 2DS4 Alleles and Their Detection Frequencies in NPC Case and Healthy Control Groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eCase Group\u003c/p\u003e\n \u003cp\u003e（n=199）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eControl Group（n=184）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*00101\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e77.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e78.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.582~1.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*003\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e12.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.739~2.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*004\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e16.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e20.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.451~1.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*010\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e36.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e35.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.683~1.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*014\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1-4 Comparison of 2DS4-Normal and 2DS4-Deleted Combinations in Case and Control Groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cem\u003eKIR2DS4 Combination\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCase Group（n=199）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003eControl Group（n=184）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 55px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4-Normal/Deleted\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e37.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.459~1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4-Normal/Normal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e41.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.677~1.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4-Deleted/Deleted*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.089~3.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2 HLA-A Allele Frequencies and Their Association with NPC Susceptibility\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed the HLA-A genotypes in a cohort of 204 NPC patients and 184 healthy controls to further explore the role of KIR/HLA interactions in NPC susceptibility. The analysis revealed significant differences in the frequencies of certain HLA-A alleles between the two groups. Notably, the frequency of HLA-A11:01 was significantly lower in the NPC group compared to the control group (23.53% vs. 30.71%, OR=0.694, 95% CI=0.505\u0026ndash;0.955, \u003cem\u003eP\u003c/em\u003e=0.024), indicating that HLA-A11:01 may act as a protective factor against NPC. In contrast, the frequency of HLA-A02:07 was significantly higher in the NPC group (17.16% vs. 8.70%, OR=2.175, 95% CI=1.394\u0026ndash;3.392, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), suggesting that HLA-A02:07 may be a susceptibility factor for NPC. These results suggest that the presence of HLA-A11:01 may decrease the risk of NPC, whereas HLA-A02:07 may increase the risk, further emphasizing their potential roles in NPC pathogenesis (Table 2).\u003c/p\u003e\n\u003cp\u003eAdditionally, we examined other HLA-A alleles, such as A33:03, A24:02, and A02:03, which did not show any statistically significant differences between the case and control groups (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). These findings underscore the importance of HLA-A genotyping in understanding the genetic factors underlying NPC susceptibility. The strong association between HLA-A02:07 and NPC highlights the potential of this allele as a marker for identifying individuals at higher risk for the disease. Moreover, the protective role of HLA-A11:01 suggests that it may have therapeutic implications in terms of understanding immune responses to NPC and the development of targeted therapies. Future research should further investigate these associations and explore how KIR/HLA interactions influence immune evasion in NPC.\u003c/p\u003e\n\u003cp\u003eTable 2 Comparative Analysis of Detection Frequencies of HLA-A Alleles Between Case and Control Groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003eHLA Allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003eCase Group（2n=408）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 165px;\"\u003e\n \u003cp\u003eCase Group（2n=368）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*11:01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e23.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e30.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.505~0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*02:07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e17.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.394~3.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*33:03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e15.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.730~1.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*24:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.685~1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*02:03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.603~1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.870~2.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*02:06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.744~3.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*11:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.411~1.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.111~1.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*26:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*24:07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*33:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*29:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*31:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*32:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*33:19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*11:10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*24:03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*30:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*24:10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*30:04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*33:10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*33:11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*74:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*74:05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*02:10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eA*23:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.3 KIR and HLA Interactions in NPC Susceptibility\u003c/p\u003e\n\u003cp\u003eIn this study, we analyzed the interaction between KIR and HLA receptor-ligand combinations to assess their association with NPC susceptibility. Our findings revealed a significant difference in the detection frequency of specific KIR+HLA combinations between the NPC patient group and the control group. Among the combinations tested, the group of patients carrying HLA-A11:01 but not KIR2DS4 showed a significantly lower detection rate compared to the control group (0.94% vs. 4.35%, OR=0.108, 95% CI=0.013\u0026ndash;0.875, \u003cem\u003eP\u003c/em\u003e=0.015). This result supports the notion that HLA-A11:01 has a protective effect against NPC development. Additionally, we found that the absence of both KIR2DS2 and HLA-A11:01 was associated with an increased susceptibility to NPC (42.65% vs. 32.07%, OR=1.575, 95% CI=1.040\u0026ndash;2.387, \u003cem\u003eP\u003c/em\u003e=0.032). Conversely, the presence of KIR2DS2 with HLA-A11:01 provided a protective effect against NPC, as evidenced by a lower frequency of NPC in this combination (28.43% vs. 38.59%, OR=0.632, 95% CI=0.413\u0026ndash;0.967, \u003cem\u003eP\u003c/em\u003e=0.034) (Table 3). These findings suggest that HLA-A*11:01 may have a protective role in NPC, and the presence of KIR2DS2 interacts with it to modulate NPC susceptibility.\u003c/p\u003e\n\u003cp\u003eMoreover, the analysis of the KIR2DS4+A11 receptor-ligand combination revealed that although the combination was found in both the case and control groups, there was no significant difference in its frequency (50% vs. 42.39%, \u003cem\u003eP\u003c/em\u003e=0.133). However, the absence of the 2DS4+A11 combination, which was found in only 0.49% of the NPC patients, was significantly lower than in the control group (4.35%, OR=0.108, 95% CI=0.013\u0026ndash;0.875, \u003cem\u003eP\u003c/em\u003e=0.015), further supporting the protective effect of HLA-A*11:01 in NPC. This highlights the importance of understanding the complex interactions between KIR genes and HLA alleles in determining susceptibility to NPC and suggests the potential of using these genetic markers for early detection and personalized treatment strategies(Table 3).\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study highlights the significant role of specific KIR and HLA genotypes and their combinations in influencing NPC susceptibility. The protective effect of HLA-A*11:01, particularly in combination with specific KIR alleles, may provide insights into NPC pathogenesis and aid in developing more personalized treatment approaches. Further research is needed to validate these findings and better understand the immune mechanisms underlying NPC susceptibility.\u003c/p\u003e\n\u003cp\u003eTable 3 Comparative Analysis of Detection Frequencies of 2DS4+A11 and 3DL2+A11 Receptor-Ligand Combinations in Case and Control Groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"723\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKIR+HLA Receptor-Ligand Combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 172px;\"\u003e\n \u003cp\u003eCase Group（n=204）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003eCase Group（n=184）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 103px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eCount(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eactive 2DS4+A11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4+A11-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e42.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.910~2.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4+A11+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e47.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e50.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.595~1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4-A11-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.189~2.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4-A11+*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.013~0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4+A*11:01-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e54.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e46.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.932~2.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4+A*11:01+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e43.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.579~1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4-A*11:01-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.189~2.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4-A*11:01+**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.013~0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4+A*11:02-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e91.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e86.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.855~3.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4+A*11:02+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.423~2.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4-A*11:02-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.438~11.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS4-A*11:02+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eactive 2DS2+A*11：01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS2+A*11:01-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e16.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.451~1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS2+A*11:01+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e15.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.702~2.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS2-A*11:01-*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e42.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e32.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.040~2.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2DS2-A*11:01+*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e28.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e38.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.413~0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.4 HLA-A Genotype and Chemotherapy Sensitivity in NPC\u003c/p\u003e\n\u003cp\u003eIn this study, we examined the relationship between HLA-A genotypes and chemotherapy sensitivity in 110 NPC patients who underwent platinum-based chemotherapy(Table 4-1,\u0026nbsp;4-2). We observed a significant association between the presence of HLA-A02:07 and increased chemotherapy sensitivity. Specifically, patients carrying the HLA-A02:07 allele demonstrated a 3.76-fold higher response rate to chemotherapy (51.35% vs. 21.92%, OR=3.760, 95% CI=1.607\u0026ndash;8.801, P=0.002) compared to those who did not carry this allele. This result underscores the importance of HLA-A*02:07 as a potential biomarker for predicting chemotherapy sensitivity in NPC patients (Table 4-4). In contrast, other HLA-A genotypes, such as HLA-A11:01, showed no significant association with chemotherapy sensitivity (40.54% vs. 59.46%, OR=0.740, 95% CI=0.332\u0026ndash;1.649, P=0.461).(Table 4-4). These findings suggest that while certain HLA-A alleles may influence treatment outcomes, not all HLA genotypes contribute significantly to chemotherapy sensitivity.\u003c/p\u003e\n\u003cp\u003eWe also explored the role of KIR genes and their interaction with HLA-A alleles. However, our analysis revealed no significant correlation between KIR genotypes and chemotherapy sensitivity. Despite this, the strong association between HLA-A*02:07 and enhanced chemotherapy response warrants further investigation into how specific HLA genotypes can be leveraged in clinical practice for more personalized treatment regimens. Further, when comparing the chemotherapy sensitivity across different demographic and clinical factors such as age, gender, and the chemotherapy regimen (GP vs. TP), no significant differences were found, suggesting that these factors do not significantly influence chemotherapy response in this cohort (\u003cem\u003eP\u003c/em\u003e\u0026gt; 0.05) (Table 4-3).\u003c/p\u003e\n\u003cp\u003eThus, genetic factors such as HLA-A*02:07 appear to be more critical in determining chemotherapy sensitivity than demographic or clinical characteristics. The findings suggest that HLA-A genotyping could serve as an important tool in personalizing chemotherapy treatment for NPC, although further studies are needed to fully understand the genetic interactions at play.\u003c/p\u003e\n\u003cp\u003eTable 4-1 Clinical Data of 110 Patients with NPC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eClinical Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eNumber(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eFrequency（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e83.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e16.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e50.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e49.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e27.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e26.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e45.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eN Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e13.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e57.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e28.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eClinical Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e34.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eⅣa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e64.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eInduction Chemotherapy Regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eGP (Gemcitabine + Cisplatin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e79.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eTP (Docetaxel + Cisplatin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e20.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4-2 The Sensitivity of Chemotherapy in Patients with NPC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eRecent Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eNumber (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e28.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e65.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4-3 Relationship between the Sensitivity of Chemotherapy and Clinical Data\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"518\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBasic Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003eChemotherapy Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePR+CR（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSD+PD（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e29（31.52）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e63（68.48）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.206~1.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8（44.44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10（55.56）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e20（35.71）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36（64.29）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.547~2.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e17（31.48）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e37（68.52）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e28（32.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e59（67.82）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.285~1.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9（39.13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14（60.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4-4 Correlation Analysis between HLA-A Genotype and Platinum Chemotherapy Sensitivity\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003e\n \u003cp\u003eHLA-A\u0026nbsp;Allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 220px;\"\u003e\n \u003cp\u003eChemotherapy Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003ePR+CR（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSD+PD（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eHLA-A*1101+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e15（40.54）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e35（47.95）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.332~1.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eHLA-A*1101-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e22（59.46）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e38（52.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eHLA-A*0207+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e19（51.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16（21.92）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.607~8.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eHLA-A*0207-*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e18（48.65）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e57（78.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.5 KIR2DS4 Allele and Its Association with Erythrocytopenia in NPC Chemotherapy\u003c/p\u003e\n\u003cp\u003eIn the analysis of the association between KIR2DS4 alleles and chemotherapy-induced side effects, particularly erythrocytopenia, we observed a significant finding regarding the KIR2DS4*003 allele. While the majority of KIR alleles did not show a significant correlation with the incidence of erythrocytopenia, the presence of the KIR2DS4*003 allele was notably associated with a lower incidence of this side effect. Specifically, patients with the KIR2DS4*003 allele had a significantly lower rate of erythrocytopenia (2.63%) compared to those without the allele (93.67%), with an odds ratio (OR) of 0.135 (95% CI = 0.017-1.082, P = 0.032) (Table 5). This suggests that KIR2DS4003 may play a protective role in reducing the risk of erythrocytopenia during platinum-based chemotherapy in NPC patients.\u003c/p\u003e\n\u003cp\u003eThese findings highlight the potential role of KIR2DS4*003 in reducing chemotherapy-induced side effects, specifically erythrocytopenia, which could help improve the management of NPC patients undergoing platinum-based chemotherapy. Further studies are necessary to confirm these results and to explore the underlying mechanisms by which KIR alleles influence chemotherapy toxicity.\u003c/p\u003e\n\u003cp\u003eTable 5 Correlation Analysis between \u003cem\u003eKIR2DS4\u003c/em\u003e Allele and Erythrocytopenia\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eKIR2DS4 Allele\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 229px;\"\u003e\n \u003cp\u003eErythrocytopenia（n=38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eOccurrence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNon-occurrence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*00101\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e30（78.95）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e59（81.94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.309~2.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*003*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1（2.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12（16.67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.017~1.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*004\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7（18.42）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6（8.33）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.770~8.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e2DS4*010\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e14（36.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25（34.72）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.484~2.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we investigated the role of genetic markers, specifically HLA-A and KIR2DS4 alleles, in predicting chemotherapy sensitivity and chemotherapy-induced erythrocytopenia in NPC patients. Our major findings revealed that the presence of HLA-A02:07 was significantly associated with increased chemotherapy sensitivity, with a 3.76-fold higher response rate compared to those without the allele (\u003cem\u003eP\u003c/em\u003e=0.002). Additionally, HLA-A11:01 was identified as a protective factor, with a lower frequency observed in chemotherapy-sensitive patients. In terms of chemotherapy-induced side effects, particularly erythrocytopenia, the KIR2DS4*003 allele was associated with a significantly lower incidence of erythrocytopenia (\u003cem\u003eP\u003c/em\u003e=0.032), suggesting a protective role in reducing hematological toxicity. These findings provide insights into the potential of using genetic testing for predicting treatment efficacy and managing side effects, supporting the idea of personalized chemotherapy regimens for NPC patients.\u003c/p\u003e\n\u003cp\u003eOur findings align with several previous studies that have highlighted the role of HLA polymorphisms in influencing chemotherapy response(Abed et al. 2024; Araz et al. 2015; Gravett et al. 2018; Larson et al. 2022; Shehata et al. 2009; Wei et al. 2017). For instance, HLA-A has been reported to be associated with enhanced chemotherapy sensitivity in in certain types of cancers, where it was found to improve the efficacy of platinum-based chemotherapies(Korentzelos et al. 2022; Okita et al. 2019; Shehata et al. 2009; Wu et al. 2018). Similarly, our results confirm the protective effect of HLA-A11:01, which is consistent with studies showing that this allele is linked to a reduced risk of NPC development(He et al. 2022; Tang et al. 2012a; Tang et al. 2012b). However, our study diverges from others in its exploration of KIR2DS4 alleles. While a few studies have suggested that certain KIR alleles, particularly KIR2DS4, can influence chemotherapy outcomes(De Re et al. 2014), the protective association between KIR2DS4*003 and erythrocytopenia in our cohort is a novel finding. Previous studies on KIR2DS4 alleles in NPC have focused more on their role in immune evasion and cancer progression, rather than chemotherapy toxicity(Lin et al. 2023; Zhu et al. 2023). The absence of significant associations between other KIR2DS4 alleles and chemotherapy toxicity in our study further distinguishes it from some prior research, which has shown more pronounced effects of these alleles on immune responses and treatment outcomes. These differences highlight the need for further studies to clarify the role of KIR alleles in chemotherapy toxicity and their potential as biomarkers for predicting adverse reactions.\u003c/p\u003e\n\u003cp\u003eThis study employed a comprehensive approach to investigate the genetic factors influencing both chemotherapy sensitivity and side effects in NPC patients by combining high-resolution HLA-A genotyping and KIR gene profiling. The use of high-resolution gene sequencing for HLA-A genotyping allowed for the accurate identification of allele-specific variations, such as HLA-A02:07, which demonstrated a strong association with chemotherapy sensitivity in our cohort (\u003cem\u003eP\u003c/em\u003e=0.002). The inclusion of KIR2DS4 genotyping further enriched our study, providing insights into the role of immune-related genetic factors in chemotherapy-induced side effects, particularly erythrocytopenia. This method is unique as it not only explores genetic predictors of treatment efficacy but also examines the genetic risk factors for chemotherapy toxicity, which has been less frequently investigated in NPC. Our finding that KIR2DS4003 carriers were less likely to experience erythrocytopenia (\u003cem\u003eP\u003c/em\u003e=0.032) adds a novel dimension to the understanding of KIR-HLA interactions in chemotherapy-induced hematological toxicity. Previous studies have primarily focused on immune evasion in cancer progression, whereas our research emphasizes how these interactions can affect treatment outcomes and adverse effects(Li et al. 2024; Lin et al. 2023; Zhu et al. 2023). The combined analysis of both HLA and KIR genotypes in this study provides a more holistic view of the genetic factors that can guide chemotherapy regimens, suggesting that genetic testing could serve as a valuable tool for personalized treatment strategies in NPC.\u003c/p\u003e\n\u003cp\u003eWhile this study offers valuable insights into the genetic factors influencing chemotherapy sensitivity and side effects in NPC patients, there are several limitations. The relatively small sample size of 110 patients may limit the generalizability of the results, and the single-center design could introduce selection bias. Additionally, potential confounding factors, such as lifestyle and environmental influences, were not accounted for, which may interact with genetic markers and affect treatment outcomes. The study also focused on specific HLA and KIR alleles, leaving out the potential role of other genetic factors that could provide a broader understanding of chemotherapy sensitivity and toxicity. Finally, the relatively short follow-up period did not allow for a long-term analysis of survival, recurrence, or late-onset side effects.\u003c/p\u003e\n\u003cp\u003eFuture research should aim to increase the sample size and include multi-center cohorts to enhance the generalizability of findings. A broader genetic analysis, incorporating other immune-related genes, would provide more comprehensive insights into the genetic determinants of chemotherapy response and toxicity. Additionally, studies should explore the interaction between genetic factors and environmental influences, such as EBV infection, diet, and smoking. Long-term studies are needed to assess the impact of genetic markers on survival, recurrence, and late-onset side effects. Employing advanced genomic techniques like next-generation sequencing could further broaden the scope of research and help identify novel biomarkers that may improve personalized treatment strategies for NPC patients.\u003c/p\u003e\n\u003cp name=\"removable\"\u003e5 Conclusion\u003c/p\u003e\n\u003cp name=\"removable\"\u003eIn conclusion, this study highlights the potential of genetic markers, specifically HLA-A and KIR alleles, in predicting chemotherapy sensitivity and side effects in NPC patients. Identifying these markers could pave the way for more personalized treatment strategies, reducing adverse effects such as erythrocytopenia and improving chemotherapy efficacy. However, the study also emphasizes the complexity of chemotherapy responses, suggesting that additional genetic factors, as well as environmental and lifestyle influences, may contribute to variations in treatment outcomes. The limitations of this study, including its small sample size and short follow-up period, suggest the need for further research with larger, multi-center cohorts to validate the results and explore the role of other genetic variations. Future studies should also incorporate more comprehensive genomic approaches, such as next-generation sequencing, to identify novel biomarkers for chemotherapy sensitivity and toxicity. Moreover, long-term follow-up studies are essential to assess the impact of these genetic markers on patient survival, recurrence, and late-onset toxicities. Overall, the integration of genetic testing into clinical practice for NPC could improve personalized treatment regimens, potentially enhancing patient outcomes and quality of life.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the potential of genetic markers, specifically HLA-A and KIR alleles, in predicting chemotherapy sensitivity and side effects in NPC patients. Identifying these markers could pave the way for more personalized treatment strategies, reducing adverse effects such as erythrocytopenia and improving chemotherapy efficacy. However, the study also emphasizes the complexity of chemotherapy responses, suggesting that additional genetic factors, as well as environmental and lifestyle influences, may contribute to variations in treatment outcomes. The limitations of this study, including its small sample size and short follow-up period, suggest the need for further research with larger, multi-center cohorts to validate the results and explore the role of other genetic variations. Future studies should also incorporate more comprehensive genomic approaches, such as next-generation sequencing, to identify novel biomarkers for chemotherapy sensitivity and toxicity. Moreover, long-term follow-up studies are essential to assess the impact of these genetic markers on patient survival, recurrence, and late-onset toxicities. Overall, the integration of genetic testing into clinical practice for NPC could improve personalized treatment regimens, potentially enhancing patient outcomes and quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNPC \u0026ndash; Nasopharyngeal Carcinoma\u003c/p\u003e\n\u003cp\u003eHLA \u0026ndash; Human Leukocyte Antigen\u003c/p\u003e\n\u003cp\u003eKIR \u0026ndash; Killer-cell Immunoglobulin-like Receptor\u003c/p\u003e\n\u003cp\u003eEBV \u0026ndash; Epstein-Barr Virus\u003c/p\u003e\n\u003cp\u003eIMRT \u0026ndash; Intensity-Modulated Radiation Therapy\u003c/p\u003e\n\u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003eRECIST \u0026ndash; Response Evaluation Criteria in Solid Tumors\u003c/p\u003e\n\u003cp\u003eTP \u0026ndash; Docetaxel and Cisplatin Chemotherapy Regimen\u003c/p\u003e\n\u003cp\u003eGP \u0026ndash; Gemcitabine and Cisplatin Chemotherapy Regimen\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. All relevant data are included in the manuscript and its supplementary files.\u003c/p\u003e\n\u003cp\u003eEthical Statement\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Red Cross Hospital, Wuzhou, Guangxi (approval number: 2018-7). All participants provided written informed consent for the collection and use of their clinical data for research purposes. Patient confidentiality was strictly maintained, and all data were anonymized before analysis.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eJie-Mei Ye, Hao-Lin Ma, and Xue-Meng Jiang: Designed assays and performed sequence analysis, coauthored and edited the manuscript. Peng Yu, Wen-Yang Wei, and Xin-Yun Peng: Performed sequence analysis. Yong-Lin Luo, Bin Zhang, and Wei Zhao: Contributed patient material. Min-Zhong Tang: Designed and initiated the study and edited the manuscript. All the authors approved the final version.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank Shenzhen Blood Center for technical assistance.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Guangxi Natural Science Foundation (Grant No. 2022JJA140620), the Wuzhou Health Commission Project (Project No. WZWS-H2023019), and the Guangxi Key Laboratory for Early Prevention and Treatment of Regional High-Incidence Tumors (Grant No. GKE-KF202207).\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflicting interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbed A, Law N, Calapre L, Lo J, Bhat V, Bowyer S, Millward M, Gray ES (2022) Human leucocyte antigen genotype association with the development of immune-related adverse events in patients with non-small cell lung cancer treated with single agent immunotherapy. 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World J Oncol 14: 350-357. doi: 10.14740/wjon1645\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":"Nasopharyngeal carcinoma, chemotherapy sensitivity, KIR, HLA, erythrocytopenia, genetic markers, personalized treatment.","lastPublishedDoi":"10.21203/rs.3.rs-5963730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5963730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eNasopharyngeal carcinoma (NPC) is common in Southeast Asia, with most patients diagnosed with locally advanced disease. Radiotherapy alone is often ineffective, so platinum-based chemotherapy is combined for better outcomes. However, chemotherapy response and side effects vary among patients. Genetic markers, particularly human leukocyte antigen (HLA) and killer-cell immunoglobulin-like receptors (KIR), have been implicated in modulating chemotherapy sensitivity and toxicity. Identifying these markers could facilitate personalized treatment strategies for NPC patients.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis study included 204 NPC patients between April 2020 and October 2021, and performed KIR and HLA-A allele typing. The control group consisted of 201 healthy individuals, matched by gender and age, who underwent routine health check-ups at the hospital. Among the cases, 110 nasopharyngeal carcinoma patients who received platinum based chemotherapy were analyzed for the relationship between KIR and HLA genotype characteristics and chemotherapy sensitivity, as well as the occurrence of chemotherapy induced side effects.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eNPC patients exhibited higher expression of activating KIR2DS4 (97.55% vs 91.54%, OR\u0026thinsp;=\u0026thinsp;3.677, 95% CI\u0026thinsp;=\u0026thinsp;1.320\u0026thinsp;~\u0026thinsp;10.168, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and inhibitory KIR3DL1 (97.55% vs 93.03%, OR\u0026thinsp;=\u0026thinsp;2.980, 95% CI\u0026thinsp;=\u0026thinsp;1.053\u0026thinsp;~\u0026thinsp;8.434, P\u0026thinsp;=\u0026thinsp;0.032), suggesting their involvement in the disease. The BB haplotype, a particular KIR gene combination, was less frequent in NPC patients, hinting at a protective effect (4.90% vs 11.44%, OR\u0026thinsp;=\u0026thinsp;0.399, 95% CI\u0026thinsp;=\u0026thinsp;0.185\u0026thinsp;~\u0026thinsp;0.861, P\u0026thinsp;=\u0026thinsp;0.016). The detection frequency of HLA-A*11:01 in the NPC case group was significantly lower than that in the healthy control group (23.53% vs 30.71%, OR\u0026thinsp;=\u0026thinsp;0.694, 95% CI\u0026thinsp;=\u0026thinsp;0.505\u0026thinsp;~\u0026thinsp;0.955, P\u0026thinsp;=\u0026thinsp;0.024), and the detection frequency of HLA-A*02:07 was significantly higher than that in the healthy control group (17.16% vs 8.70%, OR\u0026thinsp;=\u0026thinsp;2.175, 95% CI\u0026thinsp;=\u0026thinsp;1.394\u0026thinsp;~\u0026thinsp;3.392, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, HLA-A*02:07 was associated with increased chemotherapy sensitivity (51.35% vs 21.91%, OR\u0026thinsp;=\u0026thinsp;3.760, 95% CI\u0026thinsp;=\u0026thinsp;1.552\u0026thinsp;~\u0026thinsp;8.648, P\u0026thinsp;=\u0026thinsp;0.002). Additionally, the KIR2DS4*003 allele was linked to a reduced incidence of chemotherapy-induced erythrocytopenia (2.63% vs 97.37% in non-carriers, OR\u0026thinsp;=\u0026thinsp;0.135, 95% CI\u0026thinsp;=\u0026thinsp;0.017\u0026thinsp;~\u0026thinsp;1.082, P\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eOur findings suggest that HLA-A*02:07 and KIR2DS4 are promising genetic markers for predicting chemotherapy sensitivity and the risk of erythrocytopenia in NPC patients. These results support the potential for personalized chemotherapy regimens based on genetic profiling, helping to reduce side effects and improve treatment efficacy.\u003c/p\u003e","manuscriptTitle":"Exploring the Role of KIR2DS4 and HLA-A*02:07 in Predicting Chemotherapy Sensitivity and Erythrocytopenia in Nasopharyngeal Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 11:15:10","doi":"10.21203/rs.3.rs-5963730/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":"497ec67c-4e5a-4055-98bc-61065cd3a134","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-12T06:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 11:15:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5963730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5963730","identity":"rs-5963730","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}