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Communication between maternal uterus and blastocyst occurs in the early stages of pregnancy, and the interaction influences the success of embryo implantation. Whereas small extracellular vesicles (sEVs) play an essential role in mediating intercellular communication in numerous biological processes, their role in embryo implantation during the window of implantation (WOI) remains poorly defined. Here, we report that endometrial epithelial cells (EECs) secrete sEVs during early pregnancy, which affects the trophoblast behaviors (migration, invasion, and proliferation), thus influencing embryo implantation. We show that microRNA (miR)-100-5p, sEVs containing microRNA (miRNA), activates both focal adhesion kinase (FAK) and c-Jun N-terminal kinase (JNK), as well as contributes to trophoblast migration and invasion. Furthermore, our findings indicate that the sEV miR-100-5p promotes angiogenesis during the implantation process. In conclusion, this study reveals a novel mechanism by which EEC-derived sEV miR-100-5p crosstalks with trophoblasts, leading to an enhanced ability for implantation., Graphical Abstract, The receptive endometrial cell-derived small extracellular vesicle miR-100-5p promotes the migration, invasion, and proliferation of trophoblast cells, as well as angiogenesis, thereby promoting embryo implantation in the early stage of pregnancy.

Embryo implantation plays a decisive role in pregnancy. While in the process of implantation, microRNA (miRNA) is an important regulatory factor in the post transcriptional level. However, the role of many miRNAs in embryo implantation remained unknown. In this study, microRNA-183 (miR-183) was found differentially expressed in mouse uterus during implantation. In vivo treatment of miR-183 agomir in the uterine horn before implantation could eliminate the number of implantation site. The localization of miR-183 in mouse uteri gradually changed from epithelial to stromal layer in early pregnancy. Mice implantation models demonstrated that the decrease of miR-183 was mainly caused by maternal factors. Loss and gain function of miR-183 in endometrial cell lines showed that miR-183 could inhibit cell migration, invasion and apoptosis. MiR-183 could inhibit embryo implantation by binding Heparin-Binding EGF-like growth factor (Hbegf) and Laminin gamma one (Lamc1), which were key genes in embryo apposition and penetration. All these evidences indicate that miR-183 plays an important role during embryo implantation. This study provides new insights into the functions of miR-183 during embryo implantation and the development of contraceptive drugs in early pregnancy.

Additional file 1. Statistical Results in Figures.

Additional file 2: Supplementary Results.

Additional file 3: Supplementary Figures. Figure S1. Mutation statistics of all curated samples. (A) Regimen combinations of the 14 NSCLC patients. (B) Chemotherapeutic drugs and immunotherapy agents used by each patient. (C) Neoadjuvant immunotherapy duration time distribution between MPR and Non-MPR groups. (D) Correlation between PD-L1 expression and percentage of viable tumor cells in patients. (E) Mutation number retained after filtrations. (F) Mutation number distribution on MPR/Non-MPR and FFPE/Frozen specimen. (G) Six substitution type spectrum plot in two patient groups. P-values were shown above each substitution category. (H) Exposures of three types of mutation signature to known database. SBS: single base substitution; ID: InDel; DBS: double base substitution. Either sum or log2 fold change of the absolute exposures was addedly depicted. (I) Percentage of altered tumor suppressor genes enriched in multiple DNA repair-related pathways in MPR and Non-MPR group. Figure S2. SCNA statistical and signature analyses results in groups with distinct therapeutic response. (A) Chromosome level CNV burden between MPR/Non-MPR groups. (B) Arm level CNV burden in two groups. (C) Chromosome level CNV burden between squamous cell carcinoma (SCC) MPR/Non-MPR patients. (D) Arm level CNV burden in two SCC patient groups. (E) Correlation between chromosome level CNV burden and percentage of viable tumor cells. (F) Correlation between arm level CNV burden and percentage of viable tumor cells. (G) Percentage of genome amplified in two groups. (H) Percentage of genome deleted in two groups. (I) Percentage of genome amplified in SCC patients. (J) Percentage of genome deleted in SCC patients. (K) SCNA heatmap only plotting copy number alternations exceeding the threshold +/-0.2. Two aneuploid samples were marked by pink circles. (L) Contributions of three SCNA signatures in all samples. Adenocarcinoma samples were marked with pink triangles. (M) Proportion of SCNA Signature 1 in patients. (N) Proportion of SCNA Signature 7 in patients. (O) Proportion of SCNA Signature 5 in patients. (P) Proportion of SCNA Signature 1 in SCC patients. (Q) Proportion of SCNA Signature 7 in SCC patients. (R) Proportion of SCNA Signature 5 in SCC patients. Figure S3. HRDscore in non-aneuploid SCC samples. (A) HRDscore calculated on non-aneuploid SCC samples. (B) Correlation between HRDscore and percentage of viable tumor cells in all non-aneuploid SCC samples. Figure S4. Multidimensional ITH comparison results between MPR/Non-MPR groups. (A) TMB value distribution in SCC samples. (B) Correlation between TMB and percentage of viable tumor cells in SCC samples. (C) Clonal TMB value in two groups, only selecting SCC patients. (D) Correlation between clonal TMB and percentage of viable tumor cells on SCC samples. (E) Germline HR pathway gene mutation number in MPR/Non-MPR groups. (F) Subclonal SCNA fragment number in two groups, only selecting SCC samples. (G) Correlation between subclonal SCNA fragment number and percentage of viable tumor cells, only selecting SCC samples. Figure S5. Investigation on the neoantigen load difference between patients with distinct outcomes. (A) TNB in two groups. (B) Correlation between TNB values and percentage of viable tumor cells. (C) TNB in two groups, only selecting SCC samples. (D) Correlation between TNB values and percentage of viable tumor cells, only selecting SCC samples. (E) Clonal TNB value distribution in two groups, only selecting SCC samples. (F) Correlation between clonal TNB and percentage of viable tumor cells, only selecting SCC samples. (G) HLA LOH frequency in MPR/Non-MPR groups. (H) TNB value on kept HLA alleles. (I) Correlation between TNB on kept HLA alleles and percentage of viable tumor cells. (J) TNB value on kept HLA alleles, only selecting SCC samples. (K) Correlation between TNB on kept HLA alleles and percentage of viable tumor cells, only selecting SCC samples. (L) Clonal TNB value on kept HLA alleles. (M) Correlation between clonal TNB on kept HLA alleles and percentage of viable tumor cells. (N) Clonal TNB value on kept HLA alleles, only selecting SCC samples. (O) Correlation between clonal TNB on kept HLA alleles and percentage of viable tumor cells, only selecting SCC samples. Figure S6. Comprehensive analysis on clonal neoantigen and SCNA bring insights to the function of SCNA on neoantigen generation. (A) Percentage of kept clonal neoantigens on SCNA regions in two groups. (B) Correlation between CNV-related neoantigen proportion and percentage of viable tumor cells. (C) Percentage of kept clonal neoantigens on SCNA regions in two groups, only selecting SCC samples. (D) Correlation between CNV-related neoantigen proportion and percentage of viable tumor cells, only selecting SCC samples. (E) Number of clonal neoantigen on kept HLA alleles and their distribution on 3 types of SCNAs. Adenocarcinoma samples were marked with pink triangles. The sample with ATR mutation was marked with arrow. (F) Correlation between clonal neoantigen number on amplified segments and percentage of viable tumor cells. (G) Correlation between clonal neoantigen number on amplified segments and percentage of viable tumor cells, only selecting SCC samples. (H) Correlation values on all 11 non-aneuploid and high purity samples, calculated by Spearman Correlation Coefficient (SCC). (I) Similar with (H), the correlation values were on MPR samples. (J) Similar with (H), the correlation values were on Non-MPR samples. Figure S7. Investigation on HR pathway gene alternation frequency in patients with distinct response from public cohorts. (A) HR pathway gene mutation frequency in J Immunother Cancer. 2020 neoadjuvant dataset. Left: in pCR patients. Middle: in other patients. Right: percentage of distinct mutant reads for each HR gene mutation detected. Mutations from pCR patients were marked with red color. (B) HR pathway gene mutation frequency in N Engl J Med. 2018 neoadjuvant dataset. Left: in MPR patients. Middle: in Non-MPR patients. Right: percentage of distinct mutant reads for each HR gene mutation detected. Mutations from MPR patients were marked with red color. (C) HR pathway gene mutation frequency in DCB patients from Nat Genet. 2018 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (D) Similar with (C), but using non-DCB patients. (E) Similar with (C), but using J Clin Oncol. 2018 dataset. (F) Similar with (C), but using NDB patients. (G) HR pathway gene mutation frequency in patients achieved DCB in chemotherapy from Cancer Discov. 2017 dataset. (H) Similar with (G), but in NDB patients. (I) HR pathway gene SCNA frequency in patients achieved DCB in chemotherapy from Cancer Discov. 2017 dataset. Left: HR gene SCNAs. Right: HR core pathway gene SCNAs. (J) Similar with (I), but in NDB patients. (K) HR pathway gene mutation frequency in patients achieved CR and PR in chemotherapy from Nat Med. 2018 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (L) Similar with (K), but in other patients. (M) Similar with (K), but using patients received immunotherapy. (N) Similar with (M), but in other patients. Figure S8. Comparisons on TMB values stratified by therapeutic response and HR pathway gene status from public cohorts. (A) TMB and clonal TMB value distribution between DCB and other patients in non-squamous population from Nat Genet. 2018 dataset. Left: TMB. Right: clonal TMB. (B) Similar with (A) but using HR pathway gene clonal mutations as the stratification strategy. (C) TMB value distribution between DCB and NDB patients in J Clin Oncol. 2018. (D) Similar with (C) but focusing on different histological subtypes. Left: in adenocarcinoma subgroup. Right: in squamous subgroup. (E) Similar with (D) but using HR pathway gene mutation as the classification strategy. (F) Clonal TMB value distribution between patients stratified by HRD event in J Clin Oncol. 2018 dataset. Left: between patients with and without HR pathway gene mutation. Right: between patients with and without HR pathway gene clonal mutation. (G) TMB value distribution between CR+PR and other immunotherapy patients in Nat Med. 2018 dataset. (H) Similar with (G) but focusing on different histological subtypes. Left: in non-squamous subgroup. Right: in squamous subgroup. (I) TMB value distribution between CR+PR patients with HRD and other CR+PR patients in Nat Med. 2018. (J) Similar with (F), but in Nat Med. 2018 dataset. Figure S9. Survival analyses and TMB comparisons on HRD event-stratified patients in multiple public cohorts. (A) PFS of patients stratified by HR gene mutational condition in Nat Genet. 2018 dataset. Left: between patients with and without HR gene mutation. Right: between patients with and without HR gene clonal mutation. (B) Similar with (A), but on OS data. (C) Similar with (A) but in all patients from J Clin Oncol. 2018 cohort. (D) Similar with (C) but in adenocarcinoma patients. (E) Similar with (C), but in squamous patients. (F) PFS and TMB of all DCB patients with and without HR gene mutations in Nat Genet. 2018 cohort. (G) Similar with (F) but on OS data. (H) Similar with (F) but using HR pathway gene clonal mutations as the classification strategy. (I) Similar with (H) but on OS data. (J) Similar with (F) but in patients from J Clin Oncol. 2018 cohort. Figure S10. Survival analyses and TMB comparisons conducted on Nat Med. 2018 blood cohort. (A) Survival and TMB of all chemo-treated non-squamous patients with and without HR gene mutations. Left: OS. Middle: PFS. Right: TMB distribution. (B) Similar with (A) but on squamous patients. (C) Similar with (A) but on patients received immunotherapy. (D) Similar with (C) but on squamous patients. (E) Survival and TMB of all HR pathway-mutant patients treated with immunotherapy and chemotherapy. Left: OS. Middle: PFS. Right: TMB distribution. (F) Similar with (E) but among patients with HR gene clonal mutations. Figure S11. Investigation on HR pathway gene alternation frequency and HRD events in multiracial untreated patients. (A) HR pathway gene mutation frequency in Sci Rep. 2015 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (B) Similar with (A), but in J Thorac Oncol. 2020 cohort. (C) Similar with (A), but in TCGA-LUAD dataset. (D) Similar with (A), but in TCGA-LUSC dataset. (E) HR pathway gene alternation frequency in TCGA-LUAD dataset. (F) Similar with (E), but in TCGA-LUSC dataset. (G) Correlation between CNV burden and HRDscore in TCGA-LUAD dataset. (H) Similar with (G), but in TCGA-LUSC dataset.

Objectives Food and Drug Administration (FDA) approved crizotinib for advanced ROS1‐rearranged (ROS1+) non‐small‐cell lung cancer (NSCLC) patients due to a single‐arm study PROFILE 1001. However, there is no direct comparison between crizotinib and platinum‐pemetrexed chemotherapy. Materials and methods Clinical data of advanced ROS1+NSCLC patients treated with first‐line crizotinib or platinum‐pemetrexed chemotherapy between August 2010 and December 2017 were analyzed. Results Seventy‐seven patients were eligible, including 30 (39.0%) in the crizotinib group and 47 (61.0%) in the platinum‐pemetrexed chemotherapy group. The median follow‐up was 28.1 months (95% confidence interval [CI]: 19.2‐39.0). The objective response rate (ORR) of crizotinib (86.7%, 95% CI: 73.3‐96.7) was higher than that of platinum‐pemetrexed chemotherapy (44.7%, 95% CI: 29.8‐57.4, P, This is the first study to compare crizotinib with chemotherapy in ROS1+NSCLC. Crizotinib was superior to platinum‐pemetrexed chemotherapy as an initial treatment.

MicroRNA-183(miR-183) is abnormally expressed in many kinds of tumors. It participates in the initiation and development of tumors. There are many pathways regulate the expression of miR-183. The action mechanism of miR-183 in cancer is very extensive, and contradictory conclusions are often drawn. It was upregulated in 18 kinds of cancer, downregulated in 6 kinds of cancer. In addition, there are seven types of cancer, both upregulated and downregulated reports can be found. Evidence showed that miR-183 can not only directly play the role of oncogene or antioncogene, but also regulate the expression of other oncogene or antioncogene in different cancer types. In this review, we discuss the regulator of miR-183 and summarized the expression of miR-183 in different cancers. We also counted the target genes of miR-183 and the functional roles they play. Furthermore, we focused on the roles of miR-183 in cell migration, cell invasion, epithelial-mesenchymal transition (EMT) and microangiogenesis, which play the most important roles in cancer processes. It sheds light on the likely reasons why miR-183 plays different roles in various cancers. In addition, miR-183 and its downstream effectors have the potential to be promising prognostic markers and therapeutic targets in cancer.