Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer.

BACKGROUND: TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). METHODS: A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. RESULTS: The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. CONCLUSION: The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

Neoadjuvant Chemotherapy With Oxaliplatin and Fluoropyrimidine Versus Upfront Surgery for Locally Advanced Colon Cancer: The Randomized, Phase III OPTICAL Trial.

PURPOSE: The role of neoadjuvant chemotherapy (NAC) in colon cancer remains unclear. This trial investigated whether 3 months of modified infusional fluorouracil, leucovorin, and oxaliplatin (mFOLFOX6) or capecitabine and oxaliplatin (CAPOX) as NAC could improve outcomes in patients with locally advanced colon cancer versus upfront surgery. PATIENTS AND METHODS: OPTICAL was a randomized, phase III trial in patients with clinically staged locally advanced colon cancer (T3 with extramural spread into the mesocolic fat ≥5 mm or T4). Patients were randomly assigned 1:1 to receive six preoperative cycles of mFOLFOX6 or four cycles of CAPOX, followed by surgery and adjuvant chemotherapy (NAC group), or immediate surgery and the physician's choice of adjuvant chemotherapy (upfront surgery group). The primary end point was 3-year disease-free survival (DFS) assessed in the modified intention-to-treat (mITT) population. RESULTS: Between January 2016 and April 2021, of the 752 patients enrolled, 744 patients were included in the mITT analysis (371 in the NAC group; 373 in the upfront surgery group). At a median follow-up of 48.0 months (IQR, 46.0-50.1), 3-year DFS rates were 82.1% in the NAC group and 77.5% in the upfront surgery group (stratified hazard ratio [HR], 0.74 [95% CI, 0.54 to 1.03]). The R0 resection was achieved in 98% of patients who underwent surgery in both groups. Compared with upfront surgery, NAC resulted in a 7% pathologic complete response rate (pCR), significantly lower rates of advanced tumor staging (pT3-4: 77% v 94%), lymph node metastasis (pN1-2: 31% v 46%), and potentially improved overall survival (stratified HR, 0.44 [95% CI, 0.25 to 0.77]). CONCLUSION: NAC with mFOLFOX6 or CAPOX did not show a significant DFS benefit. However, this neoadjuvant approach was safe, resulted in substantial pathologic downstaging, and appears to be a viable therapeutic option for locally advanced colon cancer.

Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC.

Objective.To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model.Approach.466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFFmodel (fused features from all MRI sequences), RADCmodel (ADC radiomics feature), StratifiedADCmodel (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADCmodel were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n= 337) and test set (n= 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy.Main results.Both the RFFand StratifiedADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADCmodel revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p <0.05). The integrated RFF-StratifiedADCmodel demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p <0.05).Significance.The RFF-StratifiedADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADCmodel and radiomics information from mpMRI-based RFFmodel, exhibits tremendous promise for identifying TNBC.

Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer.

Importance: The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer. Objective: To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides. Design, Setting, and Participants: This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023. Main Outcomes and Measures: The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated. Results: This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001). Conclusions and Relevance: These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.

Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.

BACKGROUND: Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. METHODS: Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. RESULTS: By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations. CONCLUSIONS: This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study.

BACKGROUND: This study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis. METHODS: In the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA). RESULTS: In total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients. CONCLUSION: Our prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists' diagnosis.

Objective: To investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively. Methods: 460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist's work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison. Results: During the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p < 0.05 except HR+ vs. HR-). Conclusion: The RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists' diagnosis for preoperative identification of molecular receptors of BC.

ADAMTS12 promotes oxaliplatin chemoresistance and angiogenesis in gastric cancer through VEGF upregulation.

BACKGROUND: While ADAMTS12 (A disintegrin and metalloproteinase with thrombospondin motifs 12) has been established as an important regulator of gastrointestinal tumor development and angiogenic activity, the precise mechanistic functions of ADAMTS12 have yet to be fully clarified in gastric cancer (GC). Accordingly, this study was developed to explore the molecular functions of ADAMTS12 in GC and to examine its utility as a biomarker associated with chemoresistance and prognostic outcomes in this cancer type. METHODS: The ability of ADAMTS12 to modulate the proliferative, migratory, invasive, chemoresistant, and tube formation activity of tumor cells was assessed in vivo and in vitro through gain- and loss-of-function approaches. Correlations between ADAMTS12, CD31, and VEGF expression levels in GC patient tumor tissue samples from individuals that did and did not undergo neoadjuvant chemotherapy (NAC) treatment were analyzed via immunohistochemical staining. RESULTS: These analyses revealed the ability of ADAMTS12 to promote in vivo and in vitro cellular proliferative and angiogenic activity, promoting the activation of ERK and the consequent upregulation of VEGF, thereby inducing angiogenesis and decreasing GC cell oxaliplatin sensitivity. A positive correlation between ADAMTS12 levels and both the expression of VEGF as well as the density of microvessels was observed in GC patient tumor tissues. Moreover, those GC patients exhibiting higher intratumoral ADAMTS12 expression exhibited worse responses to NAC treatment and worse overall survival outcomes. CONCLUSIONS: These findings suggest that ADAMTS12 can modulate signaling via the MAPK/VEGF axis in GC cells to enhance tumor cell resistance to oxaliplatin treatment under hypoxic and normoxic conditions. Elevated ADAMTS12 levels can additionally predict vascular abnormalities, worse survival outcomes, and chemoresistance in patients with GC.

Identification of the intersegmental plane via electromagnetic navigation for anatomical segmentectomy.

Accurate identification of the physiological intersegmental plane is crucial for successful anatomical segmentectomy. Current techniques, such as the inflation-deflation method, may result in uncertain cutting lines, leading to unsuitable resection extents. Here, we demonstrated the successful use of electromagnetic navigation with methylene blue dye-marking to preoperatively and precisely identify the physiological intersegmental plane in two patients with small-sized peripheral non-small cell lung cancer (NSCLC). This novel technique offers the potential for precise cutting lines that align closely with the physiological intersegmental plane, thus improving the accuracy and efficacy of anatomical segmentectomy for these selected NSCLC patients.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer.

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagenDL classifier, rather than low-collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Neoadjuvant versus adjuvant imatinib in primary localized gastrointestinal stromal tumor.

Background: The effect of neoadjuvant therapy (NAT) with imatinib versus upfront resection (UR) followed by adjuvant therapy (AT) with imatinib on the outcomes of gastrointestinal stromal tumors (GIST) is unknown. Methods: This is a retrospective study at a high-volume center. All the patients with primary localized GIST were identified in a hospital database from 2007 to 2021. The endpoints included local recurrence-free survival (LRFS), distance recurrence-free survival (DRFS), and overall survival (OS). Cox regression was used to perform multivariate survival analyses. The sensitivity analysis was conducted with the inverse probability of treatment weighting (IPTW) method. Results: A total of 211 patients were included (Group A: UR + AT, n=140; Group B: NAT + resection + AT, n=71). In the entire cohort, 5-year DRFS, LRFS, and OS were 85.6%, 90.7%, and 92.5%, respectively. In the multivariate analysis, better DRFS was linked to NAT, tumor size of 5 cm, and AT. Sixteen patients (11.4%) in Group A and 1 (1.4%) in Group B had distant recurrences after AT discontinuation. The sensitivity analysis by IPTW provided approximately similar results. An interaction effect was observed between NAT and tumor location on DRFS. In non-gastric GISTs, NAT was associated with better DRFS [hazard ratio =0.131, 95% confidence interval (CI): 0.017-0.989, P=0.049], which was not the case in gastric GIST (P=0.08). NAT was not independently associated with LRFS or OS. Conclusions: When compared to UR + AT, NAT + resection + AT may reduce the risk of distant recurrence in localized GIST and may be especially beneficial for patients with non-gastric GISTs.

Corticostriatal Neurons in the Anterior Auditory Field Regulate Frequency Discrimination Behavior.

The anterior auditory field (AAF) is a core region of the auditory cortex and plays a vital role in discrimination tasks. However, the role of the AAF corticostriatal neurons in frequency discrimination remains unclear. Here, we used c-Fos staining, fiber photometry recording, and pharmacogenetic manipulation to investigate the function of the AAF corticostriatal neurons in a frequency discrimination task. c-Fos staining and fiber photometry recording revealed that the activity of AAF pyramidal neurons was significantly elevated during the frequency discrimination task. Pharmacogenetic inhibition of AAF pyramidal neurons significantly impaired frequency discrimination. In addition, histological results revealed that AAF pyramidal neurons send strong projections to the striatum. Moreover, pharmacogenetic suppression of the striatal projections from pyramidal neurons in the AAF significantly disrupted the frequency discrimination. Collectively, our findings show that AAF pyramidal neurons, particularly the AAF-striatum projections, play a crucial role in frequency discrimination behavior.

Single-Agent Neoadjuvant Immunotherapy With a PD-1 Antibody in Locally Advanced Mismatch Repair-Deficient or Microsatellite Instability-High Colorectal Cancer.

BACKGROUND: PD-1 blockade has been recommended as first-line therapy for nonresectable or metastatic mismatch repair-deficient/microsatellite instability-high (dMMR/MSI-H) colorectal cancer (CRC). However, the safety and efficacy of neoadjuvant PD-1 blockade immunotherapy for locally advanced dMMR/MSI-H CRC remain unclear. PATIENTS AND METHODS: From June 2020 to June 2022, 11 locally advanced dMMR/MSI-H CRC patients treated at the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were enrolled. All patients received 6 sintilimab (Innovent, LTD) injections (200 mg/injection, every 3 weeks) before radical laparoscopic resection. The patient clinical and pathological data were analyzed retrospectively. RESULTS: dMMR was confirmed by immunohistochemistry for all patients. However, polymerase chain reaction (PCR) or next-generation sequencing confirmed MSI-H for only 90.9% (10/11) of the patients, while 1 patient had microsatellite stable (MSS) disease. After 6 injections of neoadjuvant anti-PD-1 therapy, 90.9% (10/11) of the patients (those confirmed to have dMMR and MSI-H disease) achieved pathological complete response (pCR). The other patient, who achieved major pathological response with residual tumor <1%, had dMMR but MSS disease. No grade 3 or above immunotherapy-related adverse events occurred [Common Terminology Criteria for Adverse Events ; version 5.0]. Overall, 72.7% (8/11) of the patients had grade 1-2 immunotherapy-related adverse events . No operational mortality or complications occurred within 30 days after surgery. CONCLUSION: Single-agent neoadjuvant PD-1 antibody immunotherapy was safe and effective in locally advanced dMMR/MSI-H CRC. Dual confirmation of MMR and MSI status by immunohistochemistry and next-generation sequencing or PCR is necessary for dMMR/MSI-H CRC patients before immunotherapy. The immunotherapy regimen used in this study deserves further validation in phase II and III clinical studies.

Chemokine CCL14 affected the clinical outcome and correlated with immune infiltrates in thyroid carcinoma.

BACKGROUND: As an important member of the chemokines, CCL14 plays a vital role in cancer progression. However, the role of CCL14 in THCA has not been investigated. This study aimed to reveal the clinical significance of CCL14 in THCA. MATERIAL AND METHODS: This study evaluated the expression and prognostic value of CCL14 in THCA. Also, the correlation between CCL14 and immune infiltrates was assessed. Enrichment analysis was finally performed to predict CCL14-associated pathways involved in THCA. RESULTS: The mRNA and protein expressions of CCL14 in THCA tissues were down-regulated compared with normal tissues. CCL14 high expression predicted favorable DFI and PFI but did not influence the DSS and OS. Further, CCL14 showed a good prediction performance on the PFI of patients. Enrichment analysis found that CCL14 was negatively correlated with migration-related pathways such as Notch signaling, ECM-receptor interaction, and cell adhesion molecules. Further, we found that CCL14 was negatively related to immune infiltrates and their gene markers. A negative relationship was also observed between CCL14 and immune checkpoint genes. These results implied the potential effect of CCL14 on the immune response and immune therapy in THCA. CONCLUSIONS: CCL14 high expression prolonged the DFI and PFI of THCA patients. It was negatively correlated with the migration-related pathways, suggesting that CCL14 might participate in the recurrence of THCA. Further, CCL14 was also shown to be important in immune response and immune therapy in THCA.

A direct interaction between RhoGDIα/Tau alleviates hyperphosphorylation of Tau in Alzheimer's disease and vascular dementia.

RhoGDIα is an inhibitor of RhoGDP dissociation that involves in Aß metabolism and NFTs production in Alzheimer's disease (AD) by regulating of RhoGTP enzyme activity. Our previous research revealed that RhoGDIα, as the target of Polygala saponin (Sen), might alleviate apoptosis of the nerve cells caused by hypoxia/reoxygenation (H/R). To further clarify the role of RhoGDIα in the generation of NFTs, we explored the relationship between RhoGDIα and Tau. We found out that RhoGDIα and Tau can bind with each other and interact by using coimmunoprecipitation (Co-IP) and GST pulldown methods in vitro. This RhoGDIα-Tau partnership was further verified by using immunofluorescence colocalization and fluorescence resonance energy transfer (FRET) approaches in PC12 cells. Using the RNA interference (RNAi) technique, we found that the RhoGDIα may be involved in an upstream signaling pathway for Tau. Subsequently, in Aß25-35- and H/R-induced PC12 cells, forced expression of RhoGDIα via cDNA plasmid transfection was found to reduce the hyperphosphorylation of Tau, augment the expression of bcl-2 protein, and inhibit the expression of Bax protein (reducing the Bax/bcl-2 ratio) and the activity of caspase-3. In mouse AD and VaD models, forced expression of RhoGDIα via injection of a viral vector (pAAV-EGFP-RhoGDIα) into the lateral ventricle of the brain alleviated the pathological symptoms of AD and VaD. Finally, GST pulldown confirmed that the binding sites on RhoGDIα for Tau were located in the range of the ΔC33 fragment (aa 1-33). These results indicate that RhoGDIα is involved in the phosphorylation of Tau and apoptosis in AD and VaD. Overexpression of RhoGDIα can inhibit the generation of NFTs and delay the progress of these two types of dementia.

A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer.

Background: Endoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series. Methods: Two independent cohorts' series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC. Results: The clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830. Conclusions: Our results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients.

Molecular Typing and Clinical Characteristics of Synchronous Multiple Primary Colorectal Cancer.

Importance: Synchronous multiple primary colorectal cancer (sMPCC) is clinically rare, but its incidence has increased over the past decade. However, little is known about the molecular and clinical features of sMPCC, which may differ from those of single primary colorectal cancer (SPCRC). Objective: To evaluate the clinical characteristics and pathogenic variations in lesions and the molecular typing of sMPCC. Design, Setting, and Participants: From November 2012 to April 2021, patients with colorectal cancer (CRC) treated at the Sixth Affiliated Hospital of Sun Yat-sen University were enrolled in this cohort study. Follow-up ended on January 31, 2022. Main Outcomes and Measures: The primary outcome was mismatch repair (MMR) status of each lesion in all patients examined using immunohistochemistry (IHC). Microsatellite instability (MSI) and tumor mutation burden (TMB) were also calculated. Results: A total of 13 276 patients with CRC were enrolled, and 239 patients with sMPCC (mean [SD] age, 63.3 [12.2] years; 173 men [72.4%]) with available clinical data were evaluated. Seventy-eight patients with sMPCC and 94 with SPCRC also underwent next-generation sequencing (NGS)-based molecular testing. The deficient MMR (dMMR)/MSI-H frequencies in sMPCC were significantly higher than those in SPCRC, which was confirmed by both IHC (50 of 239 patients vs 872 of 13 037 patients) and NGS (17 of 78 patients vs 5 of 94 patients). According to the MMR/MSI status of different lesions in patients with sMPCC, they were further divided into 3 subgroups: all dMMR/MSI-H, dMMR/MSI-H and proficient MMR (pMMR)/microsatellite stability (MSS), and all pMMR/MSS. The EGFR and PIK3CA variants were more common, whereas TP53 variants were less prevalent in patients with sMPCC than in those with SPCRC. Moreover, higher tumor mutation burden was associated with higher MSI in patients with sMPCC rather than in those with SPCRC. Conclusions and Relevance: In this cohort study of sMPCC, the incidence of dMMR/MSI-H in patients with sMPCC was significantly higher than that in patients with SPCRC. These findings suggest that sMPCC can be classified into 3 subgroups according to the MMR/MSI status of each lesion, which might be applied to guide personalized therapies for better disease management.

Post-operative anastomotic leakage and collagen changes in patients with rectal cancer undergoing neoadjuvant chemotherapy vs chemoradiotherapy.

Background: A significant difference in the anastomotic leakage (AL) rate has been observed between patients with locally advanced rectal cancer who have undergone preoperative chemotherapy and those undergoing preoperative chemoradiotherapy. This study aimed to quantitatively analyse collagen structural changes caused by preoperative chemoradiotherapy and illuminate the relationship between collagen changes and AL. Methods: Anastomotic distal and proximal "doughnut" specimens from the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were quantitatively assessed for collagen structural changes between patients with and without preoperative radiotherapy using multiphoton imaging. Then, patients treated with preoperative chemoradiotherapy were used as a training cohort to construct an AL-SVM classifier by the Mann-Whitney U test and support vector machine (SVM). An independent test cohort from the Fujian Province Cancer Hospital (Fuzhou, China) was used to validate the AL-SVM classifier. Results: A total of 207 patients were included from the Sixth Affiliated Hospital of Sun Yat-sen University. The AL rate in the preoperative chemoradiotherapy group (n = 107) was significantly higher than that in the preoperative chemotherapy group (n = 100) (21.5% vs 7.0%, P = 0.003). A fully quantitative analysis showed notable morphological and spatial distribution feature changes in collagen in the preoperative chemoradiotherapy group. Then, the patients who received preoperative chemoradiotherapy were used as a training cohort to construct the AL-SVM classifier based on five collagen features and the tumor distance from the anus. The AL-SVM classifier showed satisfactory discrimination and calibration with areas under the curve of 0.907 and 0.856 in the training and test cohorts, respectively. Conclusions: The collagen structure may be notably altered by preoperative radiotherapy. The AL-SVM classifier was useful for the individualized prediction of AL in rectal cancer patients undergoing preoperative chemoradiotherapy.

The Novel Protein ADAMTS16 Promotes Gastric Carcinogenesis by Targeting IFI27 through the NF-κb Signaling Pathway.

A disintegrin and metalloproteinase with thrombospondin motifs 16 (ADAMTS16) has been reported to be involved in the pathogenesis of solid cancers. However, its role in gastric cancer (GC) is unclear. In this study, the role of ADAMTS16 in gastric cancer was investigated. The effects of ADAMTS16 on cell migration, invasion, and proliferation were investigated by functional experiments in vivo and in vitro. Downstream signal pathways of ADAMTS16 were confirmed by using bioinformatics analysis, co-immunoprecipitation, and immunofluorescence. Meanwhile, bioinformatics analysis, qRT-PCR, western blot, and dual-luciferase reporter gene analysis assays were used to identify ADAMTS16 targets. The expression of ADAMTS16 in GC was analyzed in public datasets. The expression of ADAMTS16 and its correlations with the clinical characteristics of GC were investigated by immunohistochemistry. Ectopic ADAMTS16 expression significantly promoted tumor cell migration, invasion, and growth. Bioinformatics analysis and western blot showed that ADAMTS16 upregulated the IFI27 protein through the NF-κb pathway, which was confirmed by immunofluorescence and western blot. Dual-luciferase reporter gene analysis identified a binding site between P65 and IFI27 that may be directly involved in the transcriptional regulation of IFI27. IFI27 knockdown reversed the promoting effect of ADAMTS16 on cell invasion, migration, and proliferation indicating that ADAMTS16 acts on GC cells by targeting the NF-κb/IFI27 axis. ADAMTS16 was associated with poor prognosis in clinical characteristics. ADAMTS16 promotes cell migration, invasion, and proliferation by targeting IFI27 through the NF-κB pathway and is a potential progressive and survival biomarker of GC.