Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective:To explore the strategy of prostate biopsy in patients with prostate specific antigen(PSA)gray zone based on prostate imaging reporting and data system (PI-RADS).Methods:The clinical data of 427 patients who underwent transperineal prostate biopsy in the First Affiliated Hospital of Nanjing Medical University from January 2020 to December 2022 were retrospectively analyzed. The median age was 66 (61, 72) years old. The median PSA was 6.62 (5.46, 8.19) ng/ml. The median PSA density (PSAD) was 0.15 (0.11, 0.21) ng/ml 2. The median prostate volume (PV) was 43.68 (31.12, 56.82) ml. PSA velocity (PSAV) data were available in 65 patients with negative MRI examination(PI-RADS <3), and the median PSAV was 1.40 (0.69, 2.89) ng/(ml· year). Among the patients with positive MRI(PI-RADS≥3), there were 174 patients with only 1 lesion and 83 patients with ≥2 lesions. A total of 170 patients with negative MRI underwent systematic biopsy, and 257 patients with positive MRI underwent systematic combined targeted biopsy. The PI-RADS score, regions of interest(ROI), PSAD, f/tPSA and PSAV were analyzed to explore the biopsy strategy for patients with PSA gray area based on bpMRI imaging. Results:Of the 427 patients included in the study, 194 were positive and 233 were negative. Among the patients with positive biopsy pathology, 140 cases were clinically significant prostate cancer (CsPCa). Among the MRI-negative patients, there were 33 cases with PSAV ≥1.4 ng/(ml·year), and 10 cases of prostate cancer and 6 cases of CsPCa were detected by systematic biopsy.In 32 cases with PSAV <1.4 ng/(ml·year), 3 cases of prostate cancer and 0 case of CsPCa were detected by systematic biopsy. The sensitivity of systematic biopsy for the diagnosis of prostate cancer and CsPCa in patients with PSAV≥1.4 ng/(ml·year) were 76.9% (10/13) and 100.0% (6/6) respectively, the specificity were 55.8% (29/52) and 54.2% (32/59) respectively, the negative predictive value were 90.6% (29/32) and 100.0% (32/32) respectively, and the positive predictive value were 30.3% (10/33) and 18.2% (6/33) respectively. In MRI-positive patients with PI-RADS 3, the prostate cancer detection rates of targeted biopsy combined with systematic biopsy, systematic biopsy and targeted biopsy were 41.7% (45/108), 32.4% (35/108) and 35.2% (38/108), respectively ( P=0.349). The detection rates of CsPCa were 27.8% (30/108), 21.3% (23/108) and 25.0% (27/108), respectively ( P=0.541). In patients with PI-RADS 4-5 and PSAD > 0.15 ng/ml 2, the detection rates of CsPCa in targeted biopsy combined with systematic biopsy, systematic biopsy and targeted biopsy were 67.8% (61/90), 58.9% (53/90) and 67.8% (61/90), respectively ( P=0.354). Conclusions:For MRI-negative patients, all CsPCa could be detected by perineal systematic biopsy when PSAV ≥1.4 ng/(ml·year), and active observation could be performed when PSAV <1.4 ng/(ml·year). For MRI-positive patients, targeted combined systemic biopsy was required when PI-RADS score was 3, and targeted biopsy only could be performed when PI-RADS score ≥4 and PSAD >0.15 ng/ml 2, otherwise targeted combined systemic biopsy was required.

Objective:To investigate the value of bi-parameter magnetic resonance imaging (bpMRI) in diagnosis and treatment of hematospermia.Methods:The clinical data and bpMRI of 182 patients with hematospermia (hematospermia group) and 51 patients without urinary system diseases (control group) were retrospectively analyzed. Both the control group and the hematospermia group underwent semen quality analysis, blood routine, urine routine, coagulation function, serum PSA test, and bpMRI examination before treatment. There were no significant differences in age [40(33, 50)years vs. 39(31, 53) years, Z=-0.77, P=0.43], body mass index [23.9(22.0, 25.7)kg/m2 vs. 24.5(22.3, 26.1) kg/m 2, Z=-0.50, P=0.62], smoking rate [24.7%(45/182) vs. 27.5%(14/51), χ2=0.16, P=0.69], alcohol consumption rate [29.1%(53/182) vs. 29.4%(15/51), χ2=0.002, P=0.97], and comorbid hypertension [20.9%(38/182) vs. 17.6%(9/51), χ2=0.26, P=0.61] between the hematospermia group and the control group. There was a statistically significant difference in PSA levels between the hematospermia group and the control group [2.82(2.08, 3.68)ng/ml vs 1.59(0.88, 2.28) ng/ml, Z=6.08, P=0.03].The median duration of illness in the hematospermia group was 10(5, 15) months, the median number of red blood cells reported in semen analysis was 17(10, 23)/HP, 59(32.4%) cases had infections in urine routine results, 15(8.2%) cases had infections in blood routine results, and 19(10.4%) cases had coagulation abnormalities. Hematospermia patients can be divided into five categories based on their causes: 105 cases of infection and inflammation, 42 cases of obstruction, 19 cases of tumors, 8 cases of systemic diseases, and 8 cases of iatrogenic factors and trauma. The treatment option was based on etiology: ①Infections, Inflammation, Systemic Diseases, Iatrogenic Factors, and Trauma: Remove the underlying cause and observe or watchful waiting. ②Recurrence of Systemic Diseases, Infections, and Inflammation: Treat the underlying cause with appropriate medication, including nonsteroidal anti-inflammatory drugs (NSAIDs), α-receptor blockers, etc. If there is an infection, administer oral antibiotics for 1-2 weeks. ③Obstruction and Tumors: Perform seminal vesiculoscopy surgery or radical prostatectomy. The efficacy evaluation was porfeomed after 12 months of treatment. Cure: Hematospermia symptoms disappear, with no recurrence. Effective: Symptoms significantly improve, no visible hematospermia, semen analysis shows marked improvement in red blood cells, and neither clinical symptoms nor semen analysis worsen. Not Cured: Visible hematospermia persists, and semen analysis shows no change in red blood cells compared to before treatment. Recurrence: Clinical symptoms improve but significant visible hematospermia reappears, and semen analysis shows red blood cell count >5/HP. Results:The proportion of patients with PI-RADS scores ≥ 3 in the hematospermia group was higher than that in the control group [29.1%(53/182)vs. 13.7%(7/51), χ2=4.94, P=0.03], and the difference was statistically significant. Comparing the imaging characteristics and related parameters of two groups of bpMRI, the results showed that the length and width of the left and right seminal vesicles in the hematospermia group were greater than those in the control group. The length of the left seminal vesicle was [29.9(25.9, 33.4)mm vs. 23.0(21.2, 25.4)mm, Z=7.30, P<0.01], the width of the left seminal vesicle was[20.4(17.8, 23.5)mm vs. 17.2(15.1, 18.5)mm, Z=5.85, P<0.01], the length of the right seminal vesicle was [28.9(24.8, 32.4)mm vs. 23.4(21.5, 28.1)mm, Z=4.68, P<0.01], and the width of the right seminal vesicle was[19.8(17.7, 23.1)mm vs. 17.2(15.1, 18.6)mm, Z=5.45, P<0.01]. The differences were statistically significant. After 12 months of follow-up, 152(83.5%) cases were cured, 21(11.5%) cases were defined as effective, 4(2.2%) cases were not cured, and 5(2.7%) cases had recurrence. Conclusions:The bpMRI examination can clearly identify the location of the hematospermia lesion and the timing of the bleeding. Based on the results of bpMRI, determining the cause and selecting the appropriate treatment strategy is reliable, convenient, and effective.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.

Objective:To investigate the value of bi-parameter magnetic resonance imaging (bpMRI) in diagnosis and treatment of hematospermia.Methods:The clinical data and bpMRI of 182 patients with hematospermia (hematospermia group) and 51 patients without urinary system diseases (control group) were retrospectively analyzed. Both the control group and the hematospermia group underwent semen quality analysis, blood routine, urine routine, coagulation function, serum PSA test, and bpMRI examination before treatment. There were no significant differences in age [40(33, 50)years vs. 39(31, 53) years, Z=-0.77, P=0.43], body mass index [23.9(22.0, 25.7)kg/m2 vs. 24.5(22.3, 26.1) kg/m 2, Z=-0.50, P=0.62], smoking rate [24.7%(45/182) vs. 27.5%(14/51), χ2=0.16, P=0.69], alcohol consumption rate [29.1%(53/182) vs. 29.4%(15/51), χ2=0.002, P=0.97], and comorbid hypertension [20.9%(38/182) vs. 17.6%(9/51), χ2=0.26, P=0.61] between the hematospermia group and the control group. There was a statistically significant difference in PSA levels between the hematospermia group and the control group [2.82(2.08, 3.68)ng/ml vs 1.59(0.88, 2.28) ng/ml, Z=6.08, P=0.03].The median duration of illness in the hematospermia group was 10(5, 15) months, the median number of red blood cells reported in semen analysis was 17(10, 23)/HP, 59(32.4%) cases had infections in urine routine results, 15(8.2%) cases had infections in blood routine results, and 19(10.4%) cases had coagulation abnormalities. Hematospermia patients can be divided into five categories based on their causes: 105 cases of infection and inflammation, 42 cases of obstruction, 19 cases of tumors, 8 cases of systemic diseases, and 8 cases of iatrogenic factors and trauma. The treatment option was based on etiology: ①Infections, Inflammation, Systemic Diseases, Iatrogenic Factors, and Trauma: Remove the underlying cause and observe or watchful waiting. ②Recurrence of Systemic Diseases, Infections, and Inflammation: Treat the underlying cause with appropriate medication, including nonsteroidal anti-inflammatory drugs (NSAIDs), α-receptor blockers, etc. If there is an infection, administer oral antibiotics for 1-2 weeks. ③Obstruction and Tumors: Perform seminal vesiculoscopy surgery or radical prostatectomy. The efficacy evaluation was porfeomed after 12 months of treatment. Cure: Hematospermia symptoms disappear, with no recurrence. Effective: Symptoms significantly improve, no visible hematospermia, semen analysis shows marked improvement in red blood cells, and neither clinical symptoms nor semen analysis worsen. Not Cured: Visible hematospermia persists, and semen analysis shows no change in red blood cells compared to before treatment. Recurrence: Clinical symptoms improve but significant visible hematospermia reappears, and semen analysis shows red blood cell count >5/HP. Results:The proportion of patients with PI-RADS scores ≥ 3 in the hematospermia group was higher than that in the control group [29.1%(53/182)vs. 13.7%(7/51), χ2=4.94, P=0.03], and the difference was statistically significant. Comparing the imaging characteristics and related parameters of two groups of bpMRI, the results showed that the length and width of the left and right seminal vesicles in the hematospermia group were greater than those in the control group. The length of the left seminal vesicle was [29.9(25.9, 33.4)mm vs. 23.0(21.2, 25.4)mm, Z=7.30, P<0.01], the width of the left seminal vesicle was[20.4(17.8, 23.5)mm vs. 17.2(15.1, 18.5)mm, Z=5.85, P<0.01], the length of the right seminal vesicle was [28.9(24.8, 32.4)mm vs. 23.4(21.5, 28.1)mm, Z=4.68, P<0.01], and the width of the right seminal vesicle was[19.8(17.7, 23.1)mm vs. 17.2(15.1, 18.6)mm, Z=5.45, P<0.01]. The differences were statistically significant. After 12 months of follow-up, 152(83.5%) cases were cured, 21(11.5%) cases were defined as effective, 4(2.2%) cases were not cured, and 5(2.7%) cases had recurrence. Conclusions:The bpMRI examination can clearly identify the location of the hematospermia lesion and the timing of the bleeding. Based on the results of bpMRI, determining the cause and selecting the appropriate treatment strategy is reliable, convenient, and effective.

Objective:To explore the optimal keV value of the virtual monoenergetic image (VMI) for displaying the osteosarcoma by using the dual-layer spectral detector CT and to evaluate its application value in determining the extent of intramedullary invasion of osteosarcoma.Methods:From August 2021 to August 2022, 57 patients with conventional osteosarcoma of long bone confirmed by biopsy in Beijing Jishuitan Hospital, Capital Medical University were retrospectively analyzed. All patients completed dual-layer spectral CT enhanced examination before limb salvage surgery, and tumor segment resection specimens were obtained after surgery. Conventional 120 kVp image and VMI of 40, 50, 60, 70 and 80 keV were obtained by spectral CT examination, and the CT values of tumors, image noise were measured and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the corresponding images were calculated. The objective evaluation among the six groups of images were assessed with the Friedman test, and then determined the optimal keV value. The maximum distance between the intramedullary boundary of osteosarcoma and the adjacent articular surfaces was measured on the best keV VMI and the tumor segment resection specimens. The Wilcoxon signed rank test was used to find the differences and the Spearman correlation analysis was used to evaluate the correlation between the distance measured from the best keV VMI and the specimens.Results:There were significant differences in CT value, image noise, SNR and CNR between 40-80 keV VMI and 120 kVp conventional CT images ( P<0.05). The CT value, SNR and CNR of 40 and 50 keV VMI were better than 120 kVp ( P<0.001). The 50 keV VMI was chosen as the best keV VMI to measure the intramedullary extent of osteosarcoma. The distance measured from 50 keV VMI was 103.9 (80.4, 131.4) mm, while the distance measured from specimens was 113.5 (94.0, 142.0) mm, and the difference was statistically significant ( Z=-5.76, P<0.001). The 50 keV VMI measurements in 51 patients were smaller than the gross specimens, which underestimated the tumor intramedullary extent, with the difference was 11.1 (6.6, 13.8) mm. The Spearman correlation analysis demonstrated a high positive correlation of distance measured on gross specimens with the 50 keV VMI ( r s=0.960, P<0.001). Conclusions:Dual-layer spectral detector CT with 50 keV VMI is the best image to show the limb osteosarcoma. Compared with gross specimens, the distance measured from CT underestimated the intramedullary invasion range of limb osteosarcoma about 10 mm, but the two show a good correlation.

Objective To explore the effects of Valpar Component Work Sample on Parkinson's disease (PD). Methods From June, 2015 to June, 2017, 40 patients with PD were randomly divided into control group (n=20) and observation group (n=20). Both groups received routine treatment and occupational therapy, while the observation group accepted rehabilitation with Valpar Component Work Sample in addition, for eight weeks. They were assessed with Unified Parkinson's Disease Rating Score II and III (UPDRSII and UPDRSIII), Mini-Mental State Examination (MMSE), Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA) and the Parkinson's Disease Questionnaire-39 (PDQ-39) before and after treatment. Results The scores of UPDRSII, UPDRSIII, MMSE, HAMD, and HAMA, and Summary Index of PDQ-39 improved in both groups (t>2.864, P<0.05) after treatment, and improved more in the observation group than in the control group (t>2.237, P<0.05). Conclusion Combined with Valpar Component Work Sample may further improve the activities of daily living, motor, cognitive function, depression and anxiety, and then quality of life in patients with PD.

Programmed cell death 4 (PDCD4) is a RNA-binding protein that acts as a tumor suppressor in many cancer types, including colorectal cancer (CRC). During CRC carcinogenesis, PDCD4 protein levels remarkably decrease, but the underlying molecular mechanism for decreased PDCD4 expression is not fully understood. In this study, we performed bioinformatics analysis to identify miRNAs that potentially target PDCD4. We demonstrated miR-181b as a direct regulator of PDCD4. We further showed that activation of IL6/STAT3 signaling pathway increased miR-181b expression and consequently resulted in downregulation of PDCD4 in CRC cells. In addition, we investigated the biological effects of PDCD4 inhibition by miR-181b both in vitro and in vivo and found that miR-181b could promote cell proliferation and migration and suppress apoptosis in CRC cells and accelerate tumor growth in xenograft mice, potentially through targeting PDCD4. Taken together, this study highlights an oncomiR role for miR-181b in regulating PDCD4 in CRC and suggests that miR-181b may be a novel molecular therapeutic target for CRC.
Animals ; Apoptosis Regulatory Proteins ; genetics ; metabolism ; Caco-2 Cells ; Cell Proliferation ; Colorectal Neoplasms ; genetics ; metabolism ; pathology ; Heterografts ; Humans ; Male ; Mice ; Mice, Nude ; Mice, SCID ; MicroRNAs ; genetics ; metabolism ; Neoplasm Proteins ; genetics ; metabolism ; Neoplasm Transplantation ; RNA, Neoplasm ; genetics ; metabolism ; RNA-Binding Proteins ; genetics ; metabolism