Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung.

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.

Crosstalk between the gut microbiome and clinical response in locally advanced thoracic esophageal squamous cell carcinoma during neoadjuvant camrelizumab and chemotherapy.

Background: The gut microbiome is associated with the response to immunotherapy in a variety of advanced cancers. However, the influence of the gut microbiome on locally advanced esophageal squamous cell carcinoma (ESCC) during programmed cell death protein 1 (PD-1) antibody immunotherapy plus chemotherapy is not clearly demonstrated. To explore the crosstalk between the gut microbiome and clinical response in locally advanced thoracic ESCC during neoadjuvant camrelizumab and chemotherapy. Methods: Patients who were diagnosed with locally advanced thoracic ESCC and had not received treatment were enrolled. The treatment regimen was two cycles of camrelizumab combined with carboplatin and albumin paclitaxel before surgery. The research endpoints were pathological complete response (pCR) and major pathological response (MPR). Fecal samples were collected at three time points: before neoadjuvant therapy, after two cycles of neoadjuvant therapy, and after surgery. We performed 16S ribosomal ribonucleic acid (rRNA) V3-V4 sequencing of the gene amplicons of fecal samples, as well as bacterial diversity and differential abundance analyses. Results: A total of 46 patients were recruited, and 44, 42, and 35 fecal samples were collected at the three time points, respectively. Statistically significant differences were observed in the amplicon sequence variant (ASV)-level alpha diversity indices, including Chao1, Shannon, and Good's coverage, between the three time points. The non-pCR-enriched gut microbiota included Proteobacteria, Dialister, Aeromonadales, Pseudomonadales, Thermi, Deinococci, Moraxellaceae, Rhodocyclales, Rhodocyclaceae, and Acinetobacter. The non-MPR-enriched gut microbiota included Pseudomonadales and the mitochondria family. The MPR-enriched gut microbiota included the Barnesiellaceae, Pyramidobacter, Dethiosulfovibrionaceae, Odoribacteraceae, Butyricimonas, Prevotella, Barnesiella, and Odoribacter. Patients with ≥3 grade adverse events (AEs) exhibited enrichment in the Succiniclasticum, Nakamurella, Rhizobium, Granulicella, Phyllobacteriaceae, Pelagibacteraceae, Actinosynnemataceae, Aquirestis, Flavisolibacter, Chelativorans, Coxiellaceae Acidicapsa, Acidobacteriaceae, Lentzea, Staphylococcus, Plesiomonas, Dysgonomonas, Pseudonocardia, and Ellin6075. Conclusions: We found that the diversity of the gut microbiome declined after neoadjuvant PD-1 antibody immunotherapy plus chemotherapy and surgery. Patients with pCR had different types and proportions of gut microbiota before treatment compared to those without pCR. We also observed the difference between patients with or without ≥ grade 3 AEs. The taxonomic features of the gut microbiome are potential biomarkers that could predict the pathological response and AEs.

Additional treatment prolonged survival of pulmonary artery sarcoma after surgical resection.

BACKGROUND: Pulmonary artery sarcoma is too rare for a randomized controlled trial or cohort study. We aimed to perform a retrospective analysis on the therapeutic effect of adjuvant therapy on postoperative survival time. METHODS: Medline and EMBASE database were searched for articles on pulmonary artery sarcoma published between 1923 and 2018. Age, gender, postoperative overall survival, therapeutic approach, tumor extension, tumor localization, status of resection margins, metastasis, surgical method were extracted as parameters to analyze postoperative overall survival. RESULTS: A total of 162 articles and 275 cases are included in analysis. Median postoperative overall survival time was 31 months. Patients who received adjuvant and/or neo-adjuvant therapy were associated with improved survival [hazard ratio (HR) =0.64, P=0.017, 95% confidence interval (CI): 0.45-0.92]. Patients with complete resection or without metastasis had longer postoperative overall survival compared with incomplete resection (HR =0.55, P=0.002, 95% CI: 0.37-0.79) or with metastasis (HR =6.01, P=0.000, 95% CI: 3.33-10.67). Subgroup analysis suggested chemotherapy was related with longer postoperative overall survival (HR =0.63, P=0.015, 95% CI: 0.43-0.91), especially for patients with incomplete resection (HR =0.53, P=0.025, 95% CI: 0.31-0.92) and metastasis (HR =0.44, P=0.000, 95% CI: 0.28-0.68). CONCLUSIONS: Radical surgery offers the only chance to cure pulmonary artery sarcoma. Palliative and aggressive surgery can relieve the symptoms and extend the life expectance. Patients can benefit from adjuvant and/or neo-adjuvant chemotherapy, especially those who have metastasis or undergo incomplete resection.

Video-assisted thoracic surgery left S1+2+3 segmentectomy for lung cancer.

A 49-year-old female presented with a solitary pulmonary nodule on the chest screening computed tomography (CT) scan. The nodule was 1.3 cm in diameter and located in the apical segment of left upper lobe. The lesion was considered to be cT1aN0M0 non-small cell lung cancer (NSCLC) and a 3-port video-assisted thoracic surgery (VATS) wedge resection was performed. Intraoperative frozen sections revealed a lung adenocarcinoma. Therefore, sequential S1+2+3 segmentectomy of the left upper lobe was performed, also systematic lymph node dissection was carried out. The final pathological stage was pT1aN0M0 (Ia).

Risk factors for No. 12p and No. 12b lymph node metastases in advanced gastric cancer in China.

BACKGROUND: The risk factors for No. 12p and No. 12b lymph node (LN) metastases in advanced gastric cancer (GC) remain controversial. The aim of this study was to investigate the risk factors for No. 12p and No. 12b LN metastases in advanced GC. METHODS: From January 1999 to December 2005, a retrospective analysis of 163 patients with advanced GC who underwent D2 lymphadenectomy in addition to No. 12p and No. 12b LN dissections was conducted. Potential clinicopathological factors that could influence No. 12p and No. 12b LN metastases were statistically analyzed. RESULTS: There were 15 cases (9.2%) with No. 12p LN metastases and 5 cases (3.1%) with synchronous No. 12b LN metastases. A logistic regression analysis revealed that the Borrmann type (III/IV versus I/II, P = 0.029), localization (lesser/circular versus greater, P = 0.025), and depth of invasion (pT4 versus pT2/pT3, P = 0.009) were associated with 11.1-, 3.8-, and 5.6-fold increases, respectively, for risk of No. 12p and No. 12b LN metastases. A logistic regression analysis also showed that No. 5 (P = 0.006) and No. 12a (P = 0.004) LN metastases were associated with 6.9- and 11.3-fold increases, respectively, for risk of No. 12p and No. 12b LN metastases. In addition, significant differences in 5-year survival of patients with and without No. 12p and No. 12b LN metastases were observed (13.3% versus 35.1%, P = 0.022). CONCLUSION: We conclude that Borrmann type, localization, and depth of invasion are significant variables for identifying patients with No. 12p and No. 12b LN metastases. Individuals with No. 5 or No. 12a LN metastases should be on high alert for the possibility of additional metastases to the No. 12p and No. 12b LNs.

[Screening of metastasis-related microRNAs in the large-cell lung cancer cell lines with different metastastic potentials].

BACKGROUND AND OBJECTIVE: MicroRNAs (miRNAs) regulate a wide range of cancer-associated processes, including cell division, proliferation, cell cycle, apoptosis, angiogenesis, invasion, and metastasis. A microarray was performed to analyze metastasis-related miRNAs with different metastastic potentials and to further elucidate their mechanism in the large-cell lung cancer cell lines. METHODS: L9981 and NL9980 cells were harvested, and total RNA was extracted for CY3. RNA hybridization was then performed on the chip with marked miRNAs. MiRNAs with significantly different expression were selected through statistical analysis. A real-time polymerase chain reaction (PCR) was employed to validate the results of the microarray, and target genes were predicted using bioinformatics. RESULTS: Expressions of 22 miRNAs were significantly different in the L9981/NL9980 cell lines. Compared with the NL9980, 13 miRNAs were upregulated in the L9981 cell lines, whereas 9 miRNAs were downregulated. The result of miR-125a-3p expression based on real-time PCR was consistent with the microarray. Insulin-like growth factor 2 might be a target gene of miR-125a-3p. CONCLUSION: The metastatic miRNA profile in large-cell lung cancer was successfully screened out.